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How AI Is Transforming the Future of Business Intelligence and Analytics 
How AI Is Transforming the Future of Business Intelligence and Analytics 
AI is rapidly emerging as a key player in business intelligence (BI) and analytics in today’s data-driven business landscape. With advanced capabilities in data collection, analysis, and decision-making, AI has the potential to revolutionize how businesses approach data-driven strategies. Recent studies have shown that AI can improve analytics performance by 69%. As AI technology continues to evolve and mature, its integration into business intelligence and analytics unlocks new opportunities for growth and innovation. Let’s take a closer look at how AI is transforming the future of business intelligence and analytics and what it means for businesses looking to stay ahead of the curve. Top 6 AI-Driven Strategies for Business Intelligence and Analytics Automated Data Collection Businesses today face the challenge of collecting and analyzing massive amounts of data to power their data-driven initiatives. Unfortunately, up to 90% of business-critical information is buried in unstructured sources, such as PDFs, emails, TXT, and other file formats. To address this challenge, AI-powered solutions have emerged with advanced capabilities such as natural language processing (NLP), optical character recognition (OCR), and computer vision. These tools can effectively identify and extract relevant data from unstructured sources. AI algorithms can recognize patterns and relationships within the data, enabling them to extract data accurately. This, in turn, enables businesses to automate the time-consuming task of manual data entry and processing, unlocking data for business intelligence and analytics initiatives. Moreover, AI-powered automated data collection also improves data quality by minimizing the potential for human errors that commonly occur during manual data entry.By helping businesses automate data collection, AI gives access to restricted data, enabling them to extract crucial insights. Intelligent DataPrep High-quality data is essential for getting the most out of a BI solution. However, a Forbes study revealed up to 84% of data can be unreliable. Luckily, AI-enabled data prep can improve data quality in several ways. AI algorithms can detect and correct errors in data in real-time, minimizing inaccuracies and ensuring access to accurate and reliable data. While at times underlying quality issues can persist, many AI-powered solutions enable users to profile data, identifying hidden patterns and anomalies automatically. For instance, AI-powered data preparation can validate data against predefined rules, automatically highlighting underlying errors. By identifying quality issues early, businesses can address them proactively and correct them before they generate misleading insights. Moreover, intelligent data prep systems use “feedback loop learning” to continuously analyze their performance and adjust algorithms based on identified errors. This improves accuracy, especially for complex or unstructured data. Through this learning process, these tools become more accurate at addressing common data preparation issues automatically. Likewise, data scientists can leverage AI-enabled fuzzy lookup to maintain data consistency by identifying and matching similar but not exact values in a data set. For instance, it can identify “Jonh Doe” and “John Doe” as the same customer despite different spellings if other attributes match. These lookups are essential when dealing with data containing misspellings, typos, or other inconsistencies. Fuzzy lookup can also identify and merge duplicate records, helping clean up data sets and eliminate redundancies. Businesses can make data-driven decisions with greater confidence by improving the overall quality and accuracy of the data. Lastly, intelligent data prep can revolutionize cloud-based BI by automating data processing and analysis. With AI-powered features, data prep and cloud BI tools can automate data cleansing, integration, and transformation, saving time and improving data quality. As a result, organizations access insights faster, enjoy enhanced data governance, and data quality management. Intelligent data prep empowers organizations to make reliable, accurate and informed decisions faster, unlocking the full potential of data in a dynamic business landscape. AI-Generated Synthetic Data Synthetic data is artificially generated data statistically similar to real-world information. With businesses increasingly utilizing business intelligence, leveraging synthetic data can help overcome data access challenges and privacy concerns. For instance, in healthcare, data experts can use synthetic data to train machine learning (ML) models. By supplanting limited patient data, synthetic data can help ML models detect early signs of diseases. Similarly, financial experts can leverage synthetic data to train models to predict stock prices or detect fraudulent activity if they lack historical data or it is in short supply. Also, synthetic data sets can mimic different scenarios, which is particularly useful in the transportation industry. Here, supply chain experts can leverage synthetic data to simulate traffic conditions and test the effectiveness of various route optimization algorithms. By leveraging synthetic data, businesses can gain valuable insights and make better decisions without relying solely on real-world data. With BI tools evolving, synthetic data can catalyze the early adoption of AI-driven technologies. Model Compression Model compression techniques have emerged as critical enablers in the era of data-driven decision-making, providing faster, more efficient, and more accurate insights. These techniques help reduce the size of AI models while maintaining their accuracy. One of the most significant advantages of model compression is the speed it brings to generating insights. By reducing the time, it takes to generate insights, businesses can respond more quickly to changing market conditions and gain a competitive advantage. Another significant advantage of model compression is its ability to reduce the memory footprint of models. This is particularly useful in environments with limited technological resources. Compressed models bandwidth requirements of business intelligence tools, making them more efficient to store and process information. Thus, improving the performance of AI models. Lastly, model compression can enhance the accuracy of models by reducing overfitting. In other words, model compression simplifies AI algorithms, making them better at making predictions against new data.As a result, models become more robust against noise and outliers, leading to more accurate predictions and better decision-making outcomes for businesses. AI-Powered Predictive Analytics AI-powered predictive analytics is transforming how businesses operate by providing unparalleled insights and predictions. These advanced algorithms are designed to easily analyze larger and more complex data, such as big data, giving companies real-time insights that were once impossible to achieve. They can detect hidden patterns within big data and live data sources such as data warehouses. AI analytics has enabled shipping companies to optimize their routes and delivery schedules. For instance, by analyzing data on traffic patterns, weather conditions, and delivery schedules in real-time, the system can generate the most efficient route for each shipment. This can reduce transportation costs, improve delivery times, and ultimately increase customer satisfaction. Similarly, financial services can use AI-powered analytics to identify and prevent fraud through real-time transactional data analysis. Data warehouses provide the necessary historical data foundation, supporting the analysis and training of AI models. Using AI-powered predictive analytics, financial institutions can enhance fraud detection capabilities, protect customers and assets, and securely manage operations. Likewise, AI and machine learning can also help institutions divide customers into segments and improve customer retention by predicting which customers will likely stay or leave. As a result, organizations can generate personalized retention strategies much more efficiently. As AI technology continues to evolve, AI-powered predictive analytics will likely become an integral part of business intelligence across industries. Organizations are increasingly turning to AI, whether in healthcare or retail or manufacturing, to help them better understand data and make more informed business decisions. With the help of AI-powered predictive analytics, businesses can gain a competitive edge, increase efficiency, and ultimately drive growth. Adaptive AI Adaptive AI systems are revolutionizing the way businesses approach business intelligence and analytics initiatives. These systems provide powerful AI features that allow organizations to deploy predictive and forecasting models that learn from past experiences and adapt to changing real-world scenarios. By combining business intelligence with adaptive AI, businesses can gain personalized insights that can be used to tailor marketing, sales, and customer service strategies to individual customers. Adaptive AI systems provide a foundation for building less rigid AI engineering pipelines or building AI models that can self-adapt in production, resulting in more agile and flexible systems. This flexibility allows businesses to respond to changing environments, limited training data, and personalize generalized results, enabling better decision-making and more efficient data processing. Many businesses across various industries are already taking advantage of adaptive AI systems to drive value. For example, Dow, a US chemical and materials manufacturer, has deployed adaptive AI systems to enhance their enterprise analytics. This deployment has resulted in a 320% increase in value generated by the analytics platform. Adaptive AI systems are transforming the way businesses approach data processing, decision-making, and customer service. By leveraging the power of these systems, businesses can gain a competitive advantage by improving personalization, increasing efficiency, and driving growth. Adopting AI for Advanced Business Intelligence and Analytics Artificial Intelligence offers a wide range of benefits that can significantly enhance the capabilities of BI and analytics. From more accurate predictions and real-time insights to personalized recommendations, AI can help businesses better understand their customers, improve operations, and ultimately drive growth and profitability. That said, adopting AI for business intelligence and analytics can be challenging without the right tools in place. Without proper data management, companies struggle to access and use data required for AI implementation, which can lead to poor results or even failure. Leveraging a comprehensive data management platform provides the necessary foundation for successful AI initiatives. It simplifies the implementation of AI models, reduces complexity, and allows organizations to derive meaningful insights from their data while maintaining data quality, security, and compliance standards. Do you want to see how LIKE.TG can help your organization achieve its AI goals? Contact us today to learn more about our data management platform and how it can simplify the deployment and scaling of AI models, reduce the time and cost of AI implementation, and, ultimately, give you a competitive advantage. Let LIKE.TG be your partner in driving operational efficiency and enhancing customer experience through AI-powered insights. Sign up for a custom demo! Try LIKE.TG Data Stack First-hand for All Your Data Management Needs Explore our Platform
What is Data Extraction: Techniques, Tools, and Best Practices
What is Data Extraction: Techniques, Tools, and Best Practices
You collected all sorts of data for your business, but now it’s trapped! It is lying in your Facebook account, POS system, PDFs, website, and other databases. So, how do you feed this data into your analytics software and that too promptly?As important as it is to collect data, what matters more is how quickly you can extract it so it is ready for analysis. Did you know that68% of business data is not utilized at all? This shows the importance of data extraction in any data-driven organization. If you can get this first step right, you can lay a strong foundation for the rest of your data pipeline. What is Data extraction? Data extraction is the process of retrieving or pulling data from various sources and converting it into a usable and meaningful format for further analysis, reporting, or storage. It is one of the most crucial steps in data management that allows you to feed data into applications or analytics downstream. Data can come from various sources, including databases, spreadsheets, websites, Application Programming Interfaces (APIs), log files, sensor data, and more. These sources may be structured (organized into tables or records) or unstructured (textual or non-tabular data). Data extraction is relatively easy when dealing with structured data, such as tabular data in Excel files or relational databases. However, it is better to use data extraction software when it comes to unstructured data sources, such as PDFs, emails, images, and videos. Importance of Data Extraction A significant issue data extraction solves is that it improves data accessibility. Imagine a business with various data sources, and all that data is in different formats, and every department is trying to use that data according to their needs; the amount of mess it would create! Data extraction pulls all data together, allowing you to convert it into a standardized format and then put it in a centralized source for everyone to use as and when needed. The result is more empowered users who can use data without relying on IT resources. Data Extraction Vs. Data Mining Data extraction and data mining are often confused with each other. However, there is a difference between the two. As we explained earlier, data extraction is collecting data from different sources and preparing it for analysis or storage in a structured database. Data mining, on the other hand, is the process of discovering patterns, trends, insights, or valuable knowledge from a dataset. It is all about applying various statistical, machine learning, and data analysis techniques to extract useful information from data. The primary goal of data mining is to uncover hidden patterns or relationships within data and then use them for decision-making or predictive modeling. Data Mining Data Extraction Purpose The focus of data mining is to derive actionable information from data. You can use data mining to discover relationships, make predictions, identify trends, or find anomalies within the data. Data extraction aims to gather, cleanse, and transform data into a consistent and structured format so that users have a reliable dataset to query or analyze. Techniques Data mining often requires a deep understanding of statistical analysis and machine learning. It uses various techniques and algorithms, including clustering, classification, regression, association rule mining, and anomaly detection. Data extraction typically involves data ingestion, parsing, and transformation techniques. Commonly used tools and methods used for data extraction include web scraping, document parsing, text extraction, and API-based data extraction. Output The output of data mining is actionable insights or patterns that you can use for making informed decision-making or building predictive models. These insights may include trends, correlations, clusters of similar data points, or rules that describe associations within data. The output of data extraction is a structured dataset ready for analysis. It may involve data cleansing to remove inconsistencies, missing values, or errors. The extracted data is usually stored in a format suitable for querying or analysis, such as a relational database. Timing Data mining is performed after data is extracted, cleaned, transformed, and validated. Data extraction is typically an initial step in the analysis, performed before any in-depth study or modeling. How does Data Extraction work? Identifying Data Sources The data extraction process starts with identifying data sources. You need to be clear on what data you need and where your data is located. It can be in documents, databases, or social media apps. Once you have identified your data sources, you need select the appropriate method for each source. For images, you might need OCR; for websites, you might need web scraping software, and so on and so forth. Source Connection Then, you need to establish a connection to selected data sources. The connection method may vary depending on the source type. You may use a database connection string, username, and password for databases. You may need to use APIs for web-based sources. Some data extraction software offers a complete solution with various inbuilt connectors so you can connect to all sources simultaneously. Query or Retrieval You can use SQL queries to retrieve specific data from tables for databases. Documents may require text extraction using OCR (or specific document parsers. Most data extraction tools are now code-free, which means all you need to do is just drag and drop a connector and connect to any data source without learning extensive SQL queries or programming languages. Data Transformation and Loading Once the data is extracted, it often doesn’t comply with the format required by the end destination or even for analysis. For example, you could have data in XML or JSON, and you might need to convert it into Excel for analysis. There could be multiple scenarios, which is why data transformation is essential. Some common transformation tasks include: Cleaning data to remove duplicates, handle missing values, and correct errors. Normalizing data by converting date formats or standardizing units of measurement. Enriching data by adding external information or calculated fields. The transformed data is then fed into a destination, which varies according to the objective of the data. You could store data in flat files such as CSV, JSON, or Parquet files or put it in a relational database (e.g., MySQL, PostgreSQL) or NoSQL database (e.g., MongoDB). Case Study Ciena Corporation, a networking industry pioneer, receives purchase orders in PDF format and was facing delays in order fulfillment due to the manual effort required to transcribe and verify order details. To automate data extraction and save time, Ciena evaluated various solutions and found LIKE.TG ReportMiner to be the best fit. As a result, Ciena is now fulfilling customer requests 15x faster and can process purchase orders in just 2 minutes instead of hours. Accelerate Data Extraction with Advanced AI Extract data from unstructured documents within seconds and reduce processing time by up to 15 times. Try our AI-powered extraction tool today. Download Your Free 14-dayTrial! Data Extraction Techniques There are various data extraction techniques; however, the most suitable technique for your organization depends on your particular use case. Here are some of the primary methods: Web Scraping Web scraping is used to collect data from various online sources, such as e-commerce websites, news sites, and social media platforms. Web scraping software access web pages, parse HTML or XML content, and extract specific data elements. API-Based Extraction Many web services provide APIs that allow developers to retrieve data from apps in a structured format. API-based extraction involves sending HTTP requests to these APIs and then retrieving data. It’s a reliable and structured way to extract data from online sources, such as social media platforms, weather services, or financial data providers. Text Extraction (Natural Language Processing – NLP) Text extraction techniques often use Natural Language Processing (NLP) to extract information from unstructured text data, such as documents, emails, or social media posts. NLP techniques include named entity recognition (NER) for extracting entities like names, dates, and locations, sentiment analysis, and text classification for extracting insights from text. OCR Optical Character Recognition (OCR) converts printed or handwritten text from documents, images, or scanned pages into machine-readable and editable text data. An OCR software analyzes processed images to recognize and convert text content into machine-readable characters. OCR engines use various techniques to identify feelings, including pattern recognition, feature extraction, and machine learning algorithms. Document Parsing Document parsing is when a computer program or system extracts structured information from unstructured or semi-structured documents. These documents can be in various formats, such as PDFs, Word files, HTML pages, emails, or handwritten notes. The parsing system identifies the document’s structure. Then, it extracts the relevant data elements, including names, addresses, dates, invoice numbers, and product descriptions, based on specific keywords, regular expressions, or other pattern-matching methods. Here's How Much You Can Save With Automated Data Extraction Calculate Your Savings Types of Data Extraction Once you have your data sources in place and you have decided which technique or techniques work, you need to set a system for your data extraction to work. You can choose from either manual data extraction, full data extraction, or incremental data extraction. Let’s see the pros and cons of each type of data extraction: Full Extraction: Full extraction, or a full load or refresh, extracts all data from a source system in a single operation. You can use this technique when the source data doesn’t change frequently, and a complete and up-to-date copy of the data is essential. Full data extraction, however, can be resource-intensive, especially for large datasets, as it retrieves all data regardless of whether the data has changed since the previous extraction. It is often the best choice as an initial step in data warehousing or data migration projects. Incremental Extraction: Incremental extraction, also called delta extraction or change data capture (CDC), is used to extract only the data that has changed since the last extraction. It is the best choice when dealing with frequently changing data sources, such as transactional databases. Also, it’s more efficient than full extraction because it reduces the amount of data transferred and processed. Common methods for incremental extraction include timestamp-based tracking, version numbers, or using flags to mark updated records. Manual Extraction: In the past, most organizations used to extract data manually. Some still copy and paste data from documents, spreadsheets, or web pages into another application or database. However, manual extraction is time-consuming, error-prone, and inevitably unsuitable for large-scale data extraction tasks. Still, it can be helpful for occasional or ad-hoc data retrieval when automation is impossible. The Role of Data Extraction in ETL ETL, which stands for extract, transform, load, is a comprehensive data integration process that includes extracting data from source systems, transforming it into a suitable format, and loading it into a target destination (e.g., data warehouse). Data extraction plays a crucial role in ETL pipelines. Efficient and accurate data extraction is essential for maintaining data integrity and ensuring that the downstream ETL stages can effectively process and utilize the extracted information for reporting, analytics, and other data-driven activities. Common Challenges for Businesses You would think that with advancements in technology, data extraction might have become easier. However, businesses still need help with data extraction challenges. Here are some common challenges that you should keep in mind while implementing data extraction processes: Data Source Heterogeneity: Do you know that a business draws data from 400 sources on average? All these sources have a different format, structure, and access method, which makes it challenging to extract data and that too on time. According to a surveyconducted by IDG,this explosion in data sources creates a complex environment that stalls projects; in fact, 32% of the people surveyed pointed out that they need help connecting to the data sources. Data Volume: There are around4.95billion internet userswho generate about 2.5 quintillion bytes of data every day. So, it is not just the variety of data sources that is a challenge, but data volume as well. Moving large volumes of data from source systems to a central repository can take time, mainly if the organization’s network bandwidth is limited. Moreover, managing large volumes of data also means potential data governance issues. Data Complexity: We have talked about high volumes of data and a variety of data sources, but it doesn’t end there—data today is more complex than ever. Gone are the days when it was just stored in two tables in Excel. Today, you will find hierarchical data, JSON files, images, PDFs, etc. On top of it, all of this data is interconnected. For example, in social network data, individuals are connected through various types of relationships, such as friendships, follows, likes, and comments. These relationships create a web of interconnected data points. Now imagine extracting these data points, and then fitting them in a schema. Error Handling and Monitoring: Error handling and monitoring are crucial aspects of data extraction, as they ensure reliability and quality of extracted data. It is even more critical in real-time data extraction, when data requires immediate error detection and handling. Scalability: Many organizations require real-time or near-real-time data extraction and analysis. As data streams continuously, the systems must keep up with the pace of data ingestion, which is why scalability is essential. When setting up your infrastructure, you need to ensure that it can handle any growth in data volume. Automation- The Need of the Hour Given that data has become more complex, the only solution to solve data extraction woes is to employ a data extraction tool that can automate most of the tasks. Here are some of the benefits of using a data extraction tool over manual data extraction: Handle Multiple Data Sources: Data extraction tools come with built-in connectors, which make it easier to connect to all data sources at once, including websites, databases, spreadsheets, PDFs, emails, and APIs. Plus, data extraction tools today are now equipped with AI capabilities that can extract data from unstructured documents using powerful AI algorithms. Scalability: The best part about data extraction tools is that they can scale to handle large volumes of data efficiently. They can extract and process data in batches or continuously to accommodate the needs of businesses with growing data requirements. Data Quality: Many data extraction tools include data quality features, such as data validation and cleansing, which help identify and correct errors or inconsistencies in the extracted data. Automation: Data extraction tools can be scheduled to run at specified intervals or triggered by specific events, which reduces the need for manual intervention and ensures that data is consistently updated. Top Data Extraction Tools The tool you choose will depend on your particular use case. However, here are some of the best data extraction tools that you can consider: LIKE.TG Report Miner: LIKE.TG Report Miner is an enterprise-grade, code-free, AI-powered data extraction tool that allows users to extract data from unstructured data sources such as invoices and purchase orders within minutes. All users need to do is identify the fields they want to extract, and the advanced AI algorithms extract data without manual intervention. The tool also comes with in-built connectors, which make it super easy to connect to any data source. The data extraction tool has advanced data preparation features, using which you can quickly clean and validate data. Docparser: Docparser is a cloud-based data extraction tool that allows you to extract data from documents, PDFs, and images. Octoparse: Octoparse is a code-free web scraper. You can use the tool to extract data from any website and store it in a structured format. It is a point-and-click web scraper, meaning you can click on the website element, and it will extract the data for you. Mail Parser: Mail Parser is an excellent choice to extract data from your emails and attachments. You can convert the extracted into webhooks, JSON, XML, or download via Excel. Parsehub: It is an easy-to-use free web scraping tool that allows you to convert website data into a spreadsheet or an API. You can use it to scrape product reviews, pricing, or sales leads. Parting Words Data extraction is the fundamental step of the entire data management cycle. As technology advances and data sources grow in complexity and volume, the field of data extraction will undoubtedly evolve. So, it is essential to keep up with new tools and best practices in the industry. Want to get started with AI-powered data extraction? Download a free 14-day trial for LIKE.TG Report Miner and extract data from PDFs within minutes. Automate data extraction and get analysis-ready data Start your FREE trial
The Importance of Transparency in Automatic Document Processing
The Importance of Transparency in Automatic Document Processing
Imagine you’re a mortgage lender and receive loan applications from prospects who want to borrow money. These applications are all electronic and have a wealth of information about the borrowers, including their finances, credit history, and jobs. You might use AI-powered software to process these applications automatically, but there’s a catch. How can you ensure that the system is making the right decisions about who qualifies for a loan? Sure, AI can speed up the process and reduce errors, but it raises important questions about fairness and accountability. How do you know if the AI system is making unbiased decisions? It is something to think about. Given that transparency plays an important role in document processing, it is imperative for businesses to implement measures that ensure transparency. In this blog, we will explore how businesses can leverage the potential of automated document processing while maintaining transparency. Transparency: The Key Ingredient for Successful Automated Document Processing The global intelligent document processing market revenue stood at $1.1 billion in 2022 and is expected to grow at a CAGR of 37.5% from 2022 to 2027. Not a surprising figure, given the host of benefits these systems have to offer, such as increased efficiency, reduced costs, and improved accuracy. So, how do these systems work? Intelligent document processing uses artificial intelligence (AI) technologies, such as natural language processing (NLP) and machine learning (ML), to automatically analyze and extract relevant information from documents. The goal of AI-based document processing is to automate time-consuming and error-prone manual document processing tasks, such as data entry, document classification, and information extraction, by using computer algorithms that can analyze and understand the content of documents. The only factor that businesses need to be wary of when using these systems is transparency, which is a crucial element that ensures decisions are not influenced by hidden biases or unfair factors. Transparency builds trust with stakeholders and, most importantly, guarantees compliance with laws and regulations. Transparency Laws in Automated Document Processing Currently, there are no specific transparency laws that apply specifically to AI-based document processing. However, there are several broader data protection and privacy laws that can be applicable to the use of AI-based document processing systems. For example, the United States, the California Consumer Privacy Act (CCPA) provides consumers with certain rights regarding the processing of their personal data. The CCPA also includes requirements for transparency and disclosure, which can be relevant to the use of AI-based document processing. Similarly, in the European Union, the General Data Protection Regulation (GDPR) requires that businesses ensure the lawful, fair, and transparent processing of personal data. The GDPR also includes requirements for data minimization, data accuracy, and data security, which can be particularly applicable to the use of AI-based document processing. There are also several industry-specific regulations that may apply to the use of AI-based document processing. For example, the healthcare industry is subject to the Health Insurance Portability and Accountability Act (HIPAA), which ensures privacy and security of personal health information. Common Transparency Challenges in Automated Document Processing Back in 2018, Amazon developed an AI-powered recruiting tool that would analyze resumes and make recommendations to hiring managers. However, the system was found to be biased against female candidates, as it had been trained on resumes submitted to Amazon over a 10-year period, which were mostly from male candidates. This incident is just a small example that highlights that ensuring transparency is not always an easy task. Here are some more common challenges you should be aware of when employing automated document processing: Limited access to training data: The main challenge for businesses implementing AI-based document processing systems is accessing diverse and representative training data, just like Amazon. Poor data quality can lead to biased or inaccurate results, undermining the system’s transparency and fairness. Lack of standardization: Algorithms, models, and processes used in AI-based document processing are not currently standardized, making it difficult for businesses to compare different systems and ensure that they are using the most effective and ethical solution. Balancing transparency with privacy: While transparency is important for ensuring the fairness and accountability of AI-based document processing systems, it needs to be balanced with the need to protect sensitive or confidential information. Best Practices for Ensuring Transparency in Automated Document Processing Despite the challenges, the benefits of automated document processing are just too hard to ignore. If you are planning to utilize automated document processing, then here are some best practices that can help you mitigate these challenges: Develop clear policies and procedures: Clearly define policies for data governance, data privacy, and data security, as well as procedures for auditing and monitoring the system. Perform regular audits: Conduct regular audits to ensure that the system is operating in accordance with ethical and legal requirements. It is better that audits are performed by independent third parties to ensure impartiality. Document algorithms and models: Document the design, implementation, and training data of the algorithms and models used in the system. This documentation is usually made public, especially to stakeholders, such as auditors and regulators, to ensure that the system is being used ethically. Provide explanations: A good AI-based document processing system provides explanations for its decisions, including providing visualizations or explanations of the features that the model is using to make decisions. Use bias detection and mitigation techniques: Use techniques such as bias detection algorithms, fairness constraints, or other approaches to ensure that your system can detect or mitigate any biases in the data or algorithms used in the process. Regularly update training data: Regularly update the training data used to train the algorithms and models to ensure that AI-based document processing systems is are always in touch with the current norms and practices. Involve diverse stakeholders: Involve diverse stakeholders in the development and deployment of AI-based document processing systems, including representatives from different departments within the organization, as well as external stakeholders such as regulators and customers. Implementing Transparency with a Code-Free Tool The increase in automatic document processing has led to the rise of no-code or low-code tools. These no-code tools not only allow people with no technical experience to automate their tasks, but also offer an excellent opportunity to maintain transparency by: Providing a more accessible and user-friendly approach to creating AI models and workflows. Offering clear and auditable data extraction rules that allow users to easily track how the system is working and make immediate adjustments accordingly. Giving real-time feedback on the accuracy of the data extraction process, allowing users to quickly identify and correct any errors or inconsistencies. Ready to Automate Document Processing? The benefits of automated document processing are clear. However, transparency is the key to leveraging these benefits and maintaining the trust of customers and stakeholders. By implementing best practices for transparency, such as using code-free tools, establishing clear accountability measures, and monitoring for biases, you can ensure that your automated document processing is fair, reliable, and consistent. Transparency not only improves business outcomes but also builds a foundation of trust. Download a 14-day free trial of LIKE.TG Report Miner and see how LIKE.TG can help you implement a transparent, automated document process.
The Future of AI in Data Warehousing: Trends and Predictions 
The Future of AI in Data Warehousing: Trends and Predictions 
Data management can be a daunting task. It requires significant time and resources to collect, process, and analyze large volumes of information. To address these challenges, approximately 44% of companies are planning to invest in artificial intelligence (AI) to streamline their data warehousing processes and improve the accuracy of their insights. AI is a powerful tool that goes beyond traditional data analytics. It identifies patterns and trends often missed by manual methods, leading to increased efficiency and improved accuracy. The current use of AI in data warehousing is just the beginning, and the possibilities for new applications are endless. AI and Data Warehousing: Trends AI is already shaping the processes surrounding data warehousing in many ways. By implementing AI-powered trends, companies have experienced dramatic improvements in the speed and accuracy of data processing. This has led to better decision-making and increased efficiency. AI-assisted ETL Processes One of the trends seen in data warehousing is the use of AI to assist in the extract, transform, and load (ETL) process. AI-powered ETL tools can automate repetitive tasks, optimize performance, and reduce the potential for human error. By AI taking care of low-level tasks, data engineers can focus on higher-level tasks such as designing data models, training machine learning algorithms, and creating data visualizations. For instance, Coca-Cola uses AI-powered ETL tools to automate data integration tasks across its global supply chain to optimize procurement and sourcing processes. Smart Data Modeling Another trend in data warehousing is the use of AI-powered tools for smart data modeling. AI can analyze data sources and automatically generate data models, considering relationships between data points. This saves time and resources for data scientists who would otherwise need to spend hours manually creating data models. Additionally, AI-powered data modeling can improve data accuracy and completeness. For instance, Walmart uses AI-powered smart data modeling techniques for specific use cases, such as supply chain management and customer analytics. Data warehouse optimization enables Walmart to quickly and accurately identify trends in customer behavior, as well as forecast demand for specific products to ensure a smooth customer shopping experience. Automated Data Cleansing AI is increasingly being used to automate the process of data cleansing in data warehousing. Automated data cleansing, or data preparation, involves using AI to detect and remove inaccuracies, inconsistencies, errors, and missing information from a data warehouse, ensuring that the data is accurate and reliable. With AI-powered data cleansing tools, organizations can leverage advanced algorithms and robust computing power to efficiently process and clean massive amounts of data. The ability to handle diverse data types allows for comprehensive data cleansing, resulting in high-quality data for analysis and decision-making. For example, GE Healthcare leverages AI-powered data cleansing tools to improve the quality of data in its electronic medical records, reducing the risk of errors in patient diagnosis and treatment. Continuous Data Quality Monitoring According to Gartner, poor data quality cost enterprises an average of $15 million per year. With continuous data quality monitoring, businesses can avoid the costly and time-consuming process of identifying and correcting errors after the fact. Continuous data quality monitoring is a new trend in data warehousing, transforming the way businesses manage their data in the future. Unlike traditional data management approaches that involve periodic data quality checks, continuous monitoring involves real-time monitoring of data quality. With AI technology, it ensures that data is always clean, accurate, and up to date by automatically detecting anomalies and errors as they occur, streamlining the data management process. For example, Airbnb has implemented AI-powered data quality monitoring tools to identify and correct data quality issues in real-time, leading to more accurate search results and pricing algorithms. AI and Data Warehousing: Predictions As artificial intelligence continues to rapidly advance, its potential applications are constantly expanding. In the realm of data warehousing, AI is predicted to play a significant role in the future. Automated Schema Design AI-powered schema design tools will analyze data sources and suggest the best schema design that fits the data, resulting in more efficient and accurate data warehousing. This technology will greatly benefit businesses dealing with large and complex data sets, such as financial institutions, healthcare organizations, and e-commerce companies. For example, an e-commerce company could use an AI-powered schema design tool to optimize their data warehouse schema for different types of products, enabling them to easily add new product categories as they expand their offerings. AI-Driven Data Curation With a rise in big data, manual curation has become a time-consuming and tedious task. AI-powered data curation tools automate data cleaning and organization, enabling businesses to derive mission-critical insights. Moreover, AI will automatically classify data using machine learning algorithms based on criteria like keywords, metadata, or content type, ensuring consistency and saving time. Safe to assume, the future of data curation with AI is bright, and it will undoubtedly pave the way for more accurate and insightful data-driven decision-making. For example, a healthcare organization could leverage predictive analytics and use an AI-powered data curation tool to analyze patient data and identify trends or correlations between certain symptoms and diagnoses, leading to improved patient care and outcomes. Intelligent Data Discovery As data warehousing becomes increasingly complex, Intelligent Data Discovery (IDD) will become a crucial trend in business intelligence. With the ability to automatically identify patterns, trends, and relationships in large datasets, IDD systems offer real-time data analysis, providing instant insights that can help businesses make informed decisions. For example, a transportation company could use an IDD system to analyze customer data and identify patterns in travel habits, leading to new service offerings or pricing models. Additionally, IDD systems can help businesses better understand their customers and improve their products or services by analyzing customer feedback and sentiment. Final Thoughts AI will play an increasingly important role in the future of data warehousing. The use of machine learning models, natural language processing, and other advanced data science techniques will enable data warehousing systems to become more intelligentand efficient at analyzing complex data sets. A successful AI-enabled data warehousing tool should possess features such as advanced data mapping and transformation capabilities, automated code generation, support for multiple data formats, seamless integration with data lakes, and real-time learning capabilities. These intelligent and autonomous data warehousing systems will be able to identify patterns and trends that are not immediately apparent and provide insights and recommendations that can help businesses stay ahead of the curve. As we move forward, we can expect to see more innovative solutions that continue to push the boundaries of what is possible in the world of data warehousing. This will help businesses of all sizes unlock the full potential of their data.
How AI is Changing the Data Integration Process 
How AI is Changing the Data Integration Process 
Coca-Cola, the beverage giant, sells around 500 soft drink brands in over 200 countries. Imagine the amount of data it gets, not just from conventional sources, such as social media or POS systems, but also from unconventional sources, like its vending machines. So, how does Coca-Cola leverage this data? The soft drink giant uses AI to analyze data at a lightning rate to ensure that its drinks are always at “an arm’s reach of desire.” Coca-Cola is just one of the many companies using AI to manage and analyze data to handle large volumes of data with greater efficiency. From data integration to analytics, it’s amazing how AI is changing the game for businesses like Coca-Cola. The Explosion in Data Volume and the Need for AI The global AI market today stands at $100 billion and is expected to grow 20-fold up to nearly two trillion dollars by 2030. This massive growth has a spillover effect on various areas, including data management. Traditional methods of data management and integration simply can’t keep up with the scale and complexity of modern data. AI-powered data integration tools, on the other hand, can automate complex data matching, cleansing, and transformation tasks, making sense of massive amounts of data more efficiently. A great example here would be General Electric and how it uses AI to unlock the potential of its data. General Electric has built a centralized data platform that allows it to integrate data from various sources, including sensors and other IoT devices. The platform uses AI algorithms to automatically identify and extract data from these sources, ensuring that the data is accurate and up to date. How AI Supercharges Data Integration AI-powered data integration automates and streamlines the entire data integration process, from data extraction to processing and analysis. By automatically discovering and ingesting data from various sources, such as databases, spreadsheets, and APIs, AI algorithms considerably speed up the data integration process without the need for manual intervention. Let’s look at how AI speeds up each stage of the data integration process Data Extraction Advanced AI algorithms can easily analyze the structure and content of data sources and extract the relevant information automatically. These algorithms can identify patterns in data and use machine learning (ML) models to learn and adapt to new data sources, simplifying document data extraction. One of the biggest challenges is extracting data from hierarchical and unstructured data sources such as text and images, especially in industries such as healthcare and finance, where a large amount of data is contained in unstructured medical records or financial reports. AI addresses this challenge by using Natural Language Processing (NLP) that recognizes patterns in language and identifies relevant keywords and phrases to extract information from unstructured data sources. AI also uses computer vision to extract data from images and videos. The perfect application of this would be in industries such as healthcare, where medical images such as X-rays and MRIs contain important diagnostic information. Data Quality While traditional data integration tools have been sufficient to tackle data quality issues, up till now, they can no longer handle the extent of data coming in from a myriad of sources. AI-based data integration tools can automatically profile data sources to identify patterns, relationships, and anomalies. AI can also identify and correct errors in data sets and automate the data cleaning process. For example, AI can identify duplicate records or missing data elements and automatically correct them, making data quality management a breeze. Data Mapping Using ML algorithms, AI systems can analyze large data volumes and automatically map data elements from one system to another, saving significant time and resources, particularly when dealing with large and complex data sets. AI-powered systems can also make intelligent mapping suggestions based on historical data and patterns. It utilizes advanced data-matching algorithms that identify matching data elements across different data sources and automatically maps them to the correct location in the target system. These algorithms are particularly useful when dealing with data sources that have different data formats or structures. How AI-based Data Integration Is Enabling Innovation Across Different Industries Healthcare: AI-based data integration is revolutionizing the healthcare industry by improving patient outcomes and diagnostics. It helps healthcare providers gain a more comprehensive view of a patient’s health by helping them integrate and analyze patient data from multiple sources, such as electronic health records, medical images, and genomics data., Finance: Financial institutions also reap benefits from AI-powered data integration, using it to enhance risk management, detect fraud, and improve customer service by gaining a comprehensive view of their customers. AI-powered data integration is also helping financial institutions comply with regulatory requirements. AI-powered data integration provides a centralized platform to manage customer data from various sources while adhering to data privacy and protection standards. The AI algorithms can analyze customer data and flag any suspicious activity or anomalies, making it easier for the institution to comply with KYC and anti-money laundering regulations. Retail: AI-based data integration also has multiple applications in the retail industry. Retailers collect data from various sources, such as point-of-sale systems, social media, and customer feedback, and traditional tools are not equipped to handle data from these unconventional sources. AI allows retailers to gain valuable insights into their customers’ preferences and behavior and tailor their offerings accordingly. AI is the Future AI-powered data integration is proving to be a game-changer for businesses as it helps unlock the full potential of their data. AI-powered data integration tools can automate complex tasks and streamline the entire data integration process. AI offers the perfect opportunity for businesses to improve efficiency and reduce costs, and there is no doubt that AI-based data integration is here to stay, and businesses that embrace it are ones that will stay ahead of the curve. Choosing the Right Data Integration Solution As the explosion in data volume continues, traditional methods of data management and integration cannot be used anymore. While AI-based data integration is now the talk of the town, it’s important to choose the right data management software to handle modern data requirements. In addition to AI capabilities, an effective data integration solution should have the following features: A user-friendly interface with a seamless workflow designer. Powerful processing engine that can manage large amounts of data and various data formats. Robust security features such as encryption, access controls, and data masking. Support for diverse data sources, including cloud-based applications, databases, and APIs. Looking for an efficient data integration that automates most of your tasks and accelerates data-driven initiatives? Download a 14-day free trial of LIKE.TG Centerprise and see how it can unload the IT burden. AI-power your Data Integration Initiatives Centerprise’s built-in AI features can help make your data integration process fast and easy. Try it now! Book Free Demo!
AI-Driven Innovations in Enterprise Data Storage for Financial Data Management 
AI-Driven Innovations in Enterprise Data Storage for Financial Data Management 
The financial industry is facing a data revolution. With more information available than ever before, it’s crucial that companies are equipped with the right tools to manage, store, and analyze this data. However, that’s rarely the case. McKinsey reports only 7% of banks are completely utilizing crucial analytics, which shows that a vast majority of financial institutions are not maximizing the potential of their data. This is where artificial intelligence (AI) comes in. AI-driven innovations in enterprise data storage are transforming the way financial data is managed and stored, enabling businesses to unlock valuable insights and stay ahead of the competition. This blog delves into the advancements in AI-driven technologies that are revolutionizing the way financial data is stored and managed in enterprises, and their impact on the future of the industry. Rethinking Data Storage: The Evolution of Financial Services Technology For many years, the financial industry relied on relational database management systems (RDBMS) as the primary data storage solution. These systems store data in a structured format and are based on a predefined schema. However, as financial institutions continue to digitize their services, the amount of data they generate and store is growing exponentially, and the limitations of RDBMS are becoming evident. The rigid structure of these systems makes it difficult to store unstructured data, such as audio and video, which is becoming increasingly important in the financial industry. The Rise of Big Data and AI-Powered Data Storage The rise of big data has brought challenges to the forefront of financial institutions. They are now seeking sophisticated and scalable data storage solutions capable of handling massive amounts of structured and unstructured data in real-time. This is where AI-driven innovations in enterprise data storage come in, allowing financial institutions to store and manage data more efficiently and effectively than ever before. AI technologies, like machine learning, can help optimize data storage and retrieval processes, automate maintenance and backup tasks, and improve overall system performance. For instance, AI can analyze user behavior and predict future storage needs, enabling financial institutions to scale their storage solutions accordingly. Additionally, AI can automatically detect and address system errors and anomalies, reducing downtime and improving system reliability. Cloud-based solutions are also an important aspect of this revolution, as they offer scalability, improved accessibility and collaboration, advanced security features, and cost savings over traditional on-premise storage solutions. Embracing these technologies gives financial institutions a competitive edge in a data-intensive world. Practical Applications and Use Cases In addition to solving the challenges of data storage in the financial industry, AI-driven innovations in data storage are also being used to create new products and services, improve customer experience, and drive revenue growth. Here are some of the most innovative use cases of AI-driven data storage in the financial industry: Fraud Detection and Prevention: Financial institutions are using AI to detect and prevent fraud. AI algorithms can analyze large amounts of data in real-time to identify unusual patterns of behavior, flag suspicious transactions, and predict potential fraud before it occurs. In fact, 64% of financial institutions believe AI can get ahead of fraud before it happens. Trading and Investment Management: AI-powered data storage solutions are being used to analyze market data and provide insights into trading and investment opportunities. These solutions can process large amounts of data quickly, identify patterns, and make predictions that can help traders and investors make informed decisions. Risk Management and Compliance: AI-powered data storage solutions also help identify and mitigate risks in the financial industry. These solutions can analyze vast amounts of data to identify potential risks and make predictions about future risks, allowing financial institutions to take proactive measures to minimize risk and ensure compliance with regulatory requirements. Innovations in AI-Driven Enterprise Data Storage Let’s examine some of the most promising AI-driven innovations in enterprise data storage and their potential to transform the financial industry. AI-based data classification and storage optimization This solution leverages AI to automatically classify data based on various factors such as age, usage frequency, and importance. The AI then assigns the most appropriate storage tier to the data, optimizing storage utilization and reducing costs. Hybrid cloud solutions with AI-driven auto-tiering and data migration Hybrid cloud combines public and private cloud benefits for financial institutions. Sensitive finance data can be stored on-premise while leveraging cloud scalability and cost-effectiveness. AI-driven auto-tiering and data migration facilitate the seamless movement of data between on-premise and cloud-based storage, ensuring optimal storage utilization and cost efficiency. Multi-cloud solutions with AI-powered data replication and synchronization Multi-cloud systems enable financial institutions to store data across multiple cloud providers, minimizing the risk of data loss and downtime due to cloud outages. AI-powered data replication and synchronization ensure that data is seamlessly and continuously replicated across multiple cloud providers, providing redundancy and data availability. AI-powered data backup and disaster recovery Financial institutions can leverage AI to automatically back up critical data in real-time and quickly recover from disasters such as cyber-attacks, natural disasters, and human error. AI can monitor data changes and patterns to detect anomalies and proactively trigger backup and recovery processes, minimizing data loss and downtime. AI-based data encryption and security With the increasing threat of data breaches, financial institutions are turning to AI-based solutions to protect their sensitive data. AI can analyze patterns in user behavior, network traffic, and system logs to detect and respond to potential security threats in real-time. AI can also automate data encryption, ensuring that sensitive data is protected both at rest and in transit. How AI is Shaping the Future of Financial Data Management The use of AI in financial data management has come a long way, and there are many more exciting advancements on the horizon. Here are some of the most significant ways AI is expected to shape the future of financial data management: Blockchain Technology Blockchain technology, with its decentralized, immutable ledger system, has the potential to revolutionize the way financial data is stored, secured, and shared. With AI-powered smart contracts, blockchain can be used to automate and streamline financial transactions, reduce errors, and improve overall efficiency. Natural Language Processing (NLP) NLP is another technology that is rapidly evolving and expected to play a significant role in the future of financial data management. NLP can be used to analyze vast amounts of unstructured financial data such as news articles, social media posts, and earnings reports. This can provide financial institutions with valuable insights into market trends, sentiment, and other factors that can impact investment decisions. Automated Data Management Solutions AI-powered automated data management solutions with native support for multiple cloud storage providers have revolutionized enterprise data storage by enabling the creation of end-to-end data pipelines. This allows financial institutions to optimize their data storage, analysis, and retrieval processes across multiple cloud platforms seamlessly. AI-Driven Future for Financial Institutions Incorporating these AI-driven innovations in enterprise data storage can help financial institutions optimize storage utilization, ensure data availability, eliminate redundancy, and protect sensitive data from security threats. From automated data management solutions to blockchain and NLP, AI-driven innovations are poised to revolutionize the way we handle financial data. As we look to the future, we can expect even more groundbreaking developments to emerge, empowering financial institutions to harness the full potential of AI and drive greater business growth. 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AI and Real-World Evidence (RWE): Extracting Insights from Real-World Health Data 
AI and Real-World Evidence (RWE): Extracting Insights from Real-World Health Data 
By automating tedious data tasks, AI enables scientists to focus on innovation and discovery Real-world evidence (RWE) holds significant potential for practitioners to gain insights into the safety and effectiveness of medical products within real-life environments. This valuable resource is derived from real-world data (RWD), encompassing diverse sources like electronic health records (EHRs), claims data, patient-generated data, as well as information from mobile health apps and wearable devices. Artificial Intelligence and RWE The transformative effect of Artificial Intelligence (AI) on RWE in healthcare is undeniable. AI enables practitioners to extract meaningful insights from RWE by analyzing large and complex datasets. By leveraging AI, researchers and healthcare providers can identify patterns and trends in real-world data that can inform clinical decision-making, optimize drug development, and improve patient outcomes. For example, AI is used to analyze EHRs to identify patients at risk for certain conditions or predict treatment responses based on patient characteristics. Additionally, AI is used to analyze social media data and patient-generated data from mobile health apps and wearables to gain a more comprehensive understanding of patient behavior and treatment outcomes. How AI and RWE Are Changing the Landscape of Medicine While drug safety regulation remains the most well-known application of real-world data, AI-powered analytics has garnered interest from various stakeholders in the healthcare ecosystem. Biopharmaceutical companies, payors, providers, policymakers, and patients alike are increasingly drawn toward AI-powered RWD’s potential to unlock transformative insights into healthcare outcomes and treatment efficacy. Biopharmaceutical companies: The integration of AI-driven RWD analytics is attracting the attention of biopharmaceutical companies. By analyzing real-world data with AI, these companies can optimize drug development and improve patient outcomes. Payors: The incorporation of AI in RWD analysis is of great interest to payors, as it enables them to make more informed decisions regarding reimbursement and coverage. The use of AI in RWD analysis can help payors better understand the value of different treatments and interventions. Providers: AI-powered RWE analytics can provide providers with insights into patient outcomes and guide clinical decision-making. By analyzing real-world data with AI, providers can identify areas for improvement in patient care and optimize treatment plans. Policymakers: The utilization of AI in RWD analysis is also of interest to policymakers, as it can inform health policy decisions and resource allocation. By using AI in RWD analysis, policymakers can better understand the impact of different interventions and make informed decisions about healthcare spending. Patients: With the help of AI-powered RWE analysis, patients can make more informed decisions regarding their healthcare. By analyzing real-world data with AI, patients can gain insights into how treatments perform in real-world settings and make more informed choices about their treatment options. McKinsey projects a top-20 pharmaceutical company can generate $300M annually by integrating advanced RWE analytics over the next three to five years. Navigating the Complexities of AI-Driven RWE: Key Challenges The combination of AI and RWE has the potential to transform healthcare by providing more personalized and effective care to patients. However, there are still challenges to be addressed to ensure that these technologies are used ethically, effectively, and responsibly. Data quality and standardization: Real-world data can come from various sources, including electronic health records, claims data, and patient-generated data, which may have different formats, coding systems, and levels of completeness. By ensuring data quality and consistency, companies can obtain reliable and meaningful insights. Data privacy and security: RWE often contains sensitive patient information, such as their health status, demographics, and behaviors. It is crucial to protect the privacy and confidentiality of this data, especially when using AI algorithms that can potentially identify individuals or infer sensitive information. Using a data extraction solution to secure this data can protect against breaches of privacy. Regulatory and legal frameworks: The use of RWE and AI in healthcare is subject to various regulatory and legal requirements, such as data protection laws, ethical guidelines, and validation standards. Solutions that comply with these frameworks allow companies to focus on making faster, more insightful decisions, while AI based extraction solutions do the heavy lifting. Interoperability and integration: AI applications that rely on RWE may need to integrate with existing healthcare systems, such as electronic health records, clinical decision support tools, and telemedicine platforms. Finding a solution that can integrate these heterogeneous systems is the key to interoperability. Clinical validation and adoption: The utility of AI and RWE in healthcare requires rigorous validation and testing to ensure their safety, efficacy, and clinical utility.A solution that is intuitive and requires a short learning curve allows healthcare providers to put their time where it is most needed: their patients. End-to-End Data Management: The Ultimate Solution to AI-Driven RWE Challenges End-to-end data management with automated data extraction can be a game-changer in addressing the challenges posed by AI-powered RWD and RWE in healthcare. By leveraging advanced technologies such as machine learning and optical character recognition, automated data extraction can streamline the collection and standardization of real-world data from various sources, such as electronic health records, clinical trials, and social media. This can help ensure the quality and consistency of data, reducing the risk of errors and biases that can compromise the accuracy and reliability of insights derived from RWE. Automating the data extraction process also enhances data privacy and security by minimizing the need for human intervention in handling sensitive information and ensuring compliance with data protection laws and ethical guidelines. In addition, it can enable interoperability and integration of AI applications with existing healthcare systems, such as EHRs and clinical decision support tools. This can facilitate seamless data exchange and communication between different systems, improving the efficiency and effectiveness of patient care. The automation also accelerates clinical validation and adoption of AI and RWE by providing real-time access to relevant and reliable data for healthcare providers and patients. By enabling data-driven decision-making and personalized interventions, end-to-end data management with automated data extraction can unlock the full potential of AI-powered RWD and RWE, transforming how modern organizations and practices approach healthcare. Learn more here to see how LIKE.TG can help.
Data Governance Vs. Data Management: What’s the Difference?
Data Governance Vs. Data Management: What’s the Difference?
The difference and relationship between data governance vs. data management can be confusing, even though both are essential for data strategy. This blog takes a close look at the roles that data governance and data management play in leveraging data as a strategic asset, and how they contribute to enhance data-driven decision-making and operational excellence. What is Data Governance? Data governance includes the frameworks, policies, standards, and processes that guide how data is collected, stored, accessed, utilized, and disseminated within an organization. Its primary goal is to certify data’s trustworthiness, security, compliance, and alignment with the business objectives and values. Data governance involves establishing clear data ownership, accountability, and decision-making authority regarding data-related matters. Data governance serves as a foundational element of data management, focusing on overseeing the operational lifecycle of data as it flows through an organization. What is Data Management? Data management is the process of setting up, applying, and managing frameworks, rules, and methods to address all the data lifecycle requirements of an organization. This process ensures that data, treated as an asset, is managed with care to support complex analysis and reporting. Data management includes various tasks and projects focusing on establishing policies and standards for data quality and security. Data Governance Vs. Data Management: Differences While data governance and data management are essential components of an organization’s data strategy, they serve different purposes, have distinct scopes, and achieve separate outcomes. These two concepts can be differentiated in terms of process, people, and technology. Process Data governance focuses on establishing the “how” of decision-making around data use. Its processes involve: Setting up data quality checks to ensure data reliability. Defining data access policies to control who can view and use data. Ensuring compliance with various regulations to mitigate legal risks. Creating and maintaining a business glossary to standardize data terminology. Data management, in contrast, is about implementing the “how” of using data within the established framework of governance. Its processes include: Performing data transformations to keep data in consistent formats. Storing data in appropriate repositories like warehouses or lakes. Exploring data to address both operational and analytical needs. People Data governance involves a mix of business and IT stakeholders, including business managers, domain data owners, and other non-technical roles. This group focuses on setting the rules and policies for data use and protection without delving into technical specifics. Data management requires technical expertise to implement the governance framework practically. Roles such as data engineers, architects, and database administrators work on the technical execution, from data storage setups to access rights and controls. Technology Data governance tools document and enforce data rules and policies across an organization. They include solutions for managing data dictionaries, glossaries, and catalogs, helping to ensure everyone operates from a common understanding of data assets. Data management tools are designed for the operational aspects of data handling, including storage, processing, and exploration. These tools facilitate the technical work of managing data flows, maintaining databases, and analyzing data. How Data Governance Data Management Work Together Data governance and data management are complementary practices, working together to fulfill an organization’s data strategy. Their collaboration ensures data is managed efficiently and aligns with company policies and objectives, thus maximizing its value. Improving Data Quality for Decision-Making and Operations Data governance involves creating a comprehensive system that catalogs and defines every piece of data, detailing its purpose, usage, and origin. It provides a structured framework that focuses on data quality and involves accessibility, privacy, and ensuring data is effectively managed across its lifecycle. Data management, on the other hand, is about systematically organizing and integrating data from various sources into cohesive storage solutions, enhancing seamless operation and accessibility. This process supports the execution of data governance policies by applying data quality checks, cleansing, and profiling to maintain the integrity and usability of data. Enhancing Data Security and Privacy Data governance sets the standards for legal compliance with data regulations and provides training for business units on handling data securely and legally. It outlines clear policies on who has the right to access which data, in what situations, and with which protective measures in place, serving as a strategic guide for implementing security and privacy safeguards. Data management, on the other hand, puts these guidelines into action. It involves specific steps like automating data encryption and setting up access controls, ensuring that data handling across the organization aligns with the governance framework. This ensures that sensitive data is only accessible to those who need it, under strict conditions, safeguarding privacy and compliance. Integrating Data for Shared Business Operations Data governance ensures that data integration efforts align with company policies, facilitating smooth collaboration between teams through tools like data catalogs and business glossaries. Data management is responsible for the technical side, implementing ETL processes and data pipelines as guided by governance policies to enable efficient data sharing and integration. Benefits of Integrating Data Governance Data Management Integrating data governance and data management into a unified strategy offers various benefits to organizations, converting data into a powerful tool for decision-making and operational improvement. Below are five key advantages of this integrated approach: 1. Streamlined Regulatory Compliance Integrating data governance and data management streamlines compliance with industry-specific regulations. By establishing clear guidelines for data usage and storage, organizations can more easily demonstrate compliance, reducing the risk of penalties and enhancing trust with stakeholders. 2. Optimized Data Utilization A unified strategy improves the organization’s ability to use data effectively. Tailored data management processes, informed by governance policies, ensure data is consistent and aligned with specific business objectives, leading to more targeted and impactful data analysis. For example, a retail chain would use integrated data strategies to precisely target customer segments, improving marketing campaign responses by aligning offers with customer preferences. 3. Accelerated Innovation When organizations harmonize data governance and management practices, they can accelerate their innovation cycles. Having reliable, well-governed data at their fingertips allows teams to quickly prototype, test, and refine new ideas, driving faster time-to-market for new products or services. 4. Reduced Data Management Costs Efficient data governance and management practices reduce the overall costs associated with data storage, processing, and cleanup. By eliminating redundancies and streamlining data workflows, organizations can achieve significant savings, reallocating resources to strategic initiatives. For instance, an educational institution could streamline its data management by integrating governance principles, which leads to the elimination of duplicate databases and automates data quality checks. 5. Strengthened Data Security A cohesive approach enhances data security, combining governance policies that define data access controls with management practices that implement these controls in practical terms. This dual layer of protection ensures sensitive data is safeguarded against unauthorized access and potential breaches. Investing in the Right Tools for Data Governance Data Management Tools and software are essential for managing and governing data. They automate and integrate processes, streamlining the establishment and enforcement of data governance policies across an organization. The impact of tools and software on data governance and management extends significantly beyond simplification, offering efficiency and effectiveness. According to a study by the International Data Corporation (IDC), organizations that use automated tools for data governance and management can see up to a 65% reduction in time spent on data-related tasks. This automation enables companies to focus more on strategic initiatives rather than routine data maintenance, ensuring that data governance policies are applied consistently and comprehensively across the board. By integrating these processes, organizations facilitate a more cohesive data strategy, allowing for seamless policy enforcement and streamlined data handling that align with their objectives and compliance requirements. Consistency A tool must have the feature of automating data quality checks. The technology can systematically scan through vast datasets, identifying and correcting errors without human intervention. This saves considerable time and enhances data accuracy, a fundamental aspect of good governance and management. Integration Capabilities The software should also combine different data sources into one organized data warehouse. Such a tool enables organizations to consolidate their information assets, making it easier to manage, analyze, and utilize data in line with governance standards. This integration is essential for maintaining a single source of truth, important for informed decision-making and strategic planning. Efficiency and Scalability These tools provide scalability, allowing data governance and data management practices to evolve with the organization. As data volumes grow and business needs change, a scalable software solution can adapt, ensuring that governance and management efforts remain efficient and responsive. Final Words To improve a company’s data, having a good link between data governance and management is key. It is essential to recognize that aligning data governance and data management successfully is important for turning data into a key asset for any organization. This alignment streamlines compliance, data security, and drives businesses towards operational excellence and innovation. As we move into the age of big data, having a unified approach to data governance and data management is becoming increasingly important. LIKE.TG offers intuitive solutions designed to simplify the complexity of data operations, for those seeking to streamline their data governance and data management processes. With its user-friendly interface and powerful automation capabilities, LIKE.TG facilitates the seamless integration of data governance policies and data management practices. Experience how LIKE.TG can transform your data governance and management initiatives by signing up for a 14-day free trial. Unify Data Governance and Data Management Empower your data strategy with LIKE.TG's intuitive solutions. Sign up for a 14-day free trial and streamline your data governance and management processes today! Start Your Free Trial
How AI is Optimizing Data-Driven Ad Targeting 
How AI is Optimizing Data-Driven Ad Targeting 
Unleashing the power of AI in data-driven Ad Targeting: personalization, optimization, and innovation for modern digital advertising. Are you tired of seeing irrelevant ads while browsing the internet? You’re not alone. The results of a survey carried out among US adults in 2021 revealed that 51% of consumers are bothered by ads that are not relevant to them. Many users find generic ads annoying and ineffective. But thanks to the power of artificial intelligence (AI), digital advertising has undergone a transformation, revolutionizing data-driven ad targeting. AI enables marketers to deliver more personalized and engaging ads that resonate with their audience. These personalized ads have proven to significantly improve user engagement, click-through rates, and conversion rates, making them an essential strategy for optimizing data-driven ad targeting. How AI is Revolutionizing Data-Driven Ad Targeting More sophisticated Machine Learning algorithms: With the advent of AI, marketers now have access to a wealth of data that can be used to train machine learning algorithms and make more accurate predictions for ad targeting. Traditional machine learning (ML) algorithms, such as logistic regression and decision trees, have been used in digital advertising for years. However, recent advancements in deep learning and neural networks have significantly improved the accuracy and performance of AI algorithms in ad targeting. Deep learning algorithms can process complex data sets and identify subtle patterns and correlations that may not be apparent to humans or traditional machine learning algorithms. This enables more precise and effective ad targeting, resulting in higher engagement and conversion rates. For instance, there are times when you are browsing for a new pair of running shoes, and suddenly you start seeing ads for running shoes on various websites and social media platforms. This is not a coincidence. It’s the power of AI in action. AI algorithms analyze your browsing behavior and other data, such as search queries, social media interactions, purchase history, and more, to deliver personalized ads relevant to your interests. The availability of such rich data sets allows AI algorithms to gain deeper insights into user preferences, interests, and behavior, leading to more effective ad-targeting strategies. Optimization of ad delivery and bidding strategies In addition to advancements in machine learning algorithms, AI is being used to optimize ad delivery and bidding strategies. Real-time bidding (RTB) is a popular technique in programmatic advertising that uses AI algorithms to automatically bid on ad inventory in real time. RTB allows marketers to target specific audience segments based on data such as demographics, interests, and location, and bid on ad placements that are most likely to perform well with their target audience. AI algorithms analyze various data points, such as historical performance data, user behavior data, and contextual data, to determine the optimal bid price and ad placement for each impression, which results in more efficient ad spend and improved campaign performance. Cross-channel Advertising The beauty of AI-powered ad targeting is that it is not limited to just one platform or channel. AI can analyze data from multiple sources, such as website visits, app usage, social media interactions, and more, to create a comprehensive user profile. This user profile can then be used to deliver relevant ads across different platforms and channels, ensuring a consistent and personalized user experience. For example, if a user has shown an interest in fitness products on a website, AI can use that information to deliver targeted ads on social media platforms, search engines, and other websites the user visits. This level of cross-channel ad targeting is made possible by the power of AI in analyzing and leveraging vast amounts of data. Real-time Analysis Real-time measurement and optimization are also driving advancements in AI for data-driven ad targeting. AI algorithms can analyze ad performance data in real time and adjust ad placements, bidding strategies, and ad content to optimize campaign performance on the fly. Consequently, marketers can quickly adapt their ad targeting strategies based on real-time data, ensuring that their ads are always relevant and effective. For example, if an ad campaign is not performing well with a particular audience segment, AI algorithms can quickly identify this and adjust the targeting parameters to focus on a different audience segment that is more likely to engage with the ads. This level of real-time optimization allows marketers to make data-driven decisions and continuously improve their ad targeting strategies for better results. Predictive Analytics Predictive analytics uses AI algorithms to analyze historical data and make predictions about future user behavior. This can be immensely valuable in ad targeting, as it allows marketers to proactively identify and target users who are most likely to convert in the future. For example, by analyzing past purchase history, browsing behavior, and other data, AI algorithms can identify users who are likely to make a purchase soon and deliver targeted ads to them to drive conversions. Predictive analytics helps marketers stay one step ahead and make proactive decisions based on data-driven insights, resulting in more effective ad targeting strategies. Challenges of AI in Data-Driven Ad Targeting AI-powered ad targeting is not without its challenges. One significant challenge is the issue of data privacy and ethics. With AI, marketers have access to vast amounts of user data, raising concerns about how this data is collected, stored, and used. Marketers need to ensure that they follow all relevant data privacy laws and regulations, such as General Data Protection Regulation (GDPR) in Europe and California Consumer Privacy Act of 2018 (CCPA) in California and use data ethically and responsibly. Transparency in data collection and usage, as well as obtaining proper consent from users, is crucial to maintain user trust and ensuring the ethical use of AI in ad targeting. AI algorithms also have a potential for bias that needs to be addressed. AI algorithms learn from data, and if the data used to train these algorithms is biased, the resulting ad-targeting strategies can also be biased. This bias can result in discriminatory ad targeting, where certain groups of users are excluded or disadvantaged based on race, gender, age, or other protected characteristics. However, with the right mindfulness and monitoring, AI can revolutionize digital marketing while ensuring fairness and inclusivity. Final Word Embracing AI in ad targeting can be a game-changer for digital advertisers. As AI continues to evolve, we can expect further advancements in data-driven ad targeting, leading to more personalized and engaging digital advertising experiences for users and improved results for marketers. Try LIKE.TG Data Stack First-hand for All Your Data Management Needs Explore our Platform
Cracking the Code: How AI is Revolutionizing Unstructured Data Integration 
Cracking the Code: How AI is Revolutionizing Unstructured Data Integration 
AI is revolutionizing how businesses handle unstructured data, which accounts for up to 80% of all data generated. By automating data extraction, cleaning, and wrangling tasks, AI frees up resources for more valuable tasks like data analysis and decision-making. From Messy to Meaningful: The Importance of Unstructured Data Integration Unstructured data integration is the key to unlocking the hidden value in today’s vast and messy data landscape. With a large portion of data generated in unstructured formats like text, audio, and video, it can be challenging to extract insights manually. However, this type of data contains valuable information such as customer sentiment, product feedback, and market trends, making it critical for businesses to analyze it efficiently. Hence, the power of unstructured data integration lies in transforming this unstructured messy data into meaningful insights that can drive business decisions. The Obstacles of Integrating Unstructured Data Integrating unstructured data is the most challenging task for organizations due to its diverse formats and lack of structure. Here are some of the obstacles that organizations face when integrating unstructured data using traditional methods: Increased costs: Traditional data management methods require extensive manual labor to extract insights from unstructured data, resulting in higher business costs. Data quality concerns: Unstructured data often contain missing fields, duplications, and inconsistencies, affecting the accuracy of the insights. Security and privacy risks: Unstructured data often contains sensitive information, posing security and privacy risks if not managed properly. Integration complexity: Integrating unstructured data with existing structured data can be a complex process that requires specialized skills and knowledge. Difficulty in data extraction: Unstructured data is often embedded in different media types, such as audio and video, making it difficult to extract the relevant data. Unleashing the Power of Unstructured Data with AI The abundance of unstructured data is a gold mine for businesses, but extracting insights from it can be challenging. Luckily, AI has emerged as a game-changer in unstructured data integration, providing advanced tools and technologies to unlock its potential. Natural Language Processing (NLP), Computer Vision, and Deep Learning are ways AI revolutionizes unstructured data integration, allowing businesses to extract valuable insights and make better-informed decisions. Natural Language Processing NLP is a subfield of AI that enables machines to understand and interpret human language. With AI-powered NLP, businesses can extract valuable insights from unstructured data sources such as customer feedback, social media posts, and emails. For instance, NLP-powered chatbots can analyze customer conversations to identify trends and sentiment, allowing businesses to improve customer service and better understand their customers’ needs. Computer Vision Computer vision is another AI subfield that enables machines to interpret and analyze visual data, such as images and videos. With AI-powered computer vision, businesses can extract valuable insights from unstructured data sources such as security footage, product images, and medical images. For example, in Healthcare, computer vision can analyze medical images to detect diseases like cancer, enabling early intervention and potentially saving lives. Deep Learning Deep learning is a subset of machine learning that uses artificial neural networks to analyze and interpret data. With deep learning, businesses can extract insights from large, complex unstructured data sets, such as voice recordings and sensor data. For instance, deep learning can be used to improve predictive maintenance of machinery in the manufacturing industry. By analyzing sensor data from the equipment, deep learning algorithms can identify patterns and anomalies that indicate when maintenance is required. Unlocking the Benefits of Unstructured Data Integration with AI Unstructured data integration doesn’t have to be complicated. With AI and machine learning, businesses can improve their data integration flow and gain a competitive advantage across the enterprise. Here are some of the ways AI can transform data integration efforts: Faster data mapping and processing: AI-powered data mapping automates the data transformation mapping process and offers advanced features for agile data mapping predictions, saving time and reducing the need for technical expertise. Improved data quality: AI and ML enhance the reliability of data consistency, helping organizations proactively respond to issues related to data quality and provide better company-wide data quality management. Address traditional data integration issues: AI and ML techniques help to handle large volumes of data from sources such as IoT and streaming, reducing usage complexity and offering easy-to-use data integration templates. Autonomous learning capability: AI and ML technology enable enterprises to learn more about hidden patterns and trends in large datasets, providing accurate business insights through statistical models. Faster processing of big data: AI and ML algorithms make data analysis faster, allowing for improved big data processing, including legacy and more modern business solutions, such as business texting. Looking at an AI-Led Future Extracting insights from unstructured data is now a necessity, not an option. Fortunately, AI and its subfields, such as NLP, computer vision, and deep learning, provide businesses with a means to unlock the potential of unstructured data. With AI-powered unstructured data integration, organizations can save time, reduce costs, and make informed decisions. AI-powered end-to-end data management solutions can play a critical role in data management processes. They enable companies to seamlessly extract, integrate, store, and process data to help you turn raw unstructured data to business insights. By having the ability to store, analyze and report data from various sources and share it with decision-makers, organizations can finally uncover the enormous business value of unstructured data. The evolution of AI opens endless possibilities for unstructured data integration, making it an exciting time for businesses to embrace the power of AI to drive success. So don’t let unstructured data hold you back. Partner with LIKE.TG and harness the power of AI to drive business success. Learn more here or get in touch! Explore LIKE.TG's AI-Powered Data Extraction Tool Explore ReportMiner!
AI and Data Storage: Reducing Costs and Improving Scalability 
AI and Data Storage: Reducing Costs and Improving Scalability 
In today’s rapidly evolving digital landscape, data has become the bloodline of businesses across the globe. As organizations strive to harness the power of this valuable resource, they are presented with exciting opportunities to efficiently store, effectively manage, and extract valuable insights from vast quantities of data. According to a study by IDC, the world’s data volume is projected to reach a staggering 163 zettabytes by 2025, which is up from 33 zettabytes in 2018! This exponential growth highlights the urgency for companies to find efficient, cost-effective, and scalable solutions for data storage and management. Fortunately, in this era of rapid technological advancement, we’re witnessing the rise of innovative solutions. Artificial Intelligence (AI) stands at the forefront of these developments, offering the potential to revolutionize data storage and management, and turn this challenge into a transformative opportunity for businesses worldwide. The Evolution of Data Storage and the Rise of AI Data storage has come a long way since the mid-20th century when punch cards and magnetic tapes were the primary storage options. Innovations such as floppy disks, hard drives, optical storage, and cloud storage have revolutionized the industry. In recent years, the growing complexity of data management tasks, coupled with the need for efficient, cost-effective, and scalable storage solutions, has driven the integration of AI technologies into data storage systems. This integration has led to the development of intelligent storage solutions capable of self-optimizing, predicting, and automating various aspects of data management. One significant impact of AI is the shift from a reactive to a proactive approach for storage management. Traditional systems require manual intervention, while AI-driven solutions leverage advanced algorithms and machine learning models to predict and address potential issues, reducing human intervention and minimizing downtime. AI-Driven Innovations in Data Storage Intelligent Storage Systems One of the most prominent advancements in AI-driven data storage is the development of intelligent storage systems. These systems leverage machine learning and AI algorithms to continuously monitor, analyze, and optimize storage performance, capacity, and overall health. By proactively identifying and addressing potential issues, intelligent storage systems minimize downtime, enhance resource utilization, and ensure that data is readily available when needed. Consider a modern tech company that manages vast amounts of user data. An intelligent storage system in such a setting would continuously monitor the status of the storage infrastructure, including factors like capacity usage, read/write speeds, and error rates. Using AI algorithms, it can predict potential system faults or capacity issues. For instance, if the system detects a pattern suggesting an impending disk failure, it can alert the IT team or even initiate a data backup process to prevent data loss. This way, potential issues are addressed proactively, ensuring data availability and system performance. AI-Powered Data Compression Data compression plays a vital role in efficient data storage. It allows organizations to decrease the storage space needed for their data. AI-powered data compression algorithms have taken this process to new heights, providing more efficient and intelligent ways to compress data without sacrificing quality or accessibility. These algorithms can automatically identify patterns and redundancies in data and adaptively compress it in a way that minimizes storage requirements while maintaining optimal performance and quality. Consider a healthcare system managing vast amounts of medical images such as X-rays, MRIs, or CT scans. These images require significant storage space. An AI-powered compression algorithm could intelligently analyze and compress these images, preserving the necessary diagnostic details while significantly reducing storage requirements. This not only saves storage space but also makes the transmission of these images across networks more efficient. Predictive Analytics and Storage Optimization Predictive analytics is another key innovation enabled by AI in data storage. By analyzing historical usage patterns, performance metrics, and other relevant data, AI-driven predictive analytics can forecast future storage needs and optimize storage resources accordingly. This foresight enables organizations to proactively plan for storage capacity expansion. It helps them avoid potential bottlenecks and ensures smooth operation as data volumes grow. Consider Amazon, a massive e-commerce platform. It generates huge amounts of data daily from various operations. By leveraging AI-powered predictive analytics, Amazon can forecast future storage needs based on past trends. For instance, anticipating increased storage demands during the holiday season, it can proactively allocate more resources, preventing system bottlenecks, ensuring smooth operations, and enhancing the user experience. Automated Data Tiering and Lifecycle Management AI-driven data storage solutions also excel at automating data tiering and lifecycle management. These systems can intelligently classify and prioritize data based on various factors, such as frequency of access, age, and importance. AI-enhanced storage systems automatically transfer data between various storage tiers and media types. This helps optimize resource use, guarantees quick access to frequently used data, and reduces storage expenses. Reducing Costs with AI in Data Storage Optimizing Storage Resources One of the primary ways AI-driven data storage solutions help reduce costs is by intelligently optimizing storage resources. These systems can analyze usage patterns to automatically allocate resources. This ensures that high-priority applications and data have access to the necessary storage capacity and performance. Meanwhile, less critical data is stored more cost-effectively. This optimization not only reduces the need for over-provisioning storage but also helps organizations make more efficient use of existing storage resources. Cutting Energy Consumption Energy consumption is a significant factor in data storage costs, particularly in large-scale data centers. AI-enhanced storage solutions can help minimize energy consumption by intelligently managing power usage. For instance, these systems can automatically power down or spin down disks that are not in use or move infrequently accessed data to low-power storage media. By reducing the overall energy footprint, AI-driven storage solutions contribute to lower operating costs. Reducing Maintenance and Support Costs Traditional storage systems often require frequent manual intervention for tasks such as troubleshooting, performance tuning, and capacity planning. By implementing AI-driven storage solutions, many of these tasks can be automated. This reduces the need for dedicated staff and lowers maintenance and support costs. Additionally, AI-powered predictive analytics can identify potential hardware failures or performance issues before they become critical. This allows for more cost-effective, proactive maintenance. Minimizing Downtime and Data Loss Downtime and data loss can be extremely costly for organizations, both in terms of direct financial losses and reputational damage. AI-driven storage solutions can help minimize these risks by proactively monitoring system health, detecting anomalies, and automatically addressing potential issues. This not only reduces the likelihood of unexpected downtime but also helps to ensure data integrity and availability, protecting valuable business data from corruption or loss. Improving Scalability through AI-Enhanced Data Storage Flexible and Dynamic Storage Architectures AI-driven storage solutions offer organizations the flexibility and adaptability required to handle ever-growing data volumes. These systems can dynamically adjust to changing storage requirements. This enables seamless capacity expansion and performance tuning without the need for manual intervention. By supporting a variety of storage media and architectures, AI-enhanced storage systems can efficiently scale to accommodate the demands of diverse workloads and use cases. Seamless Integration with Cloud and Hybrid Environments As more organizations adopt cloud and hybrid infrastructure models, AI-driven data storage solutions have evolved to integrate seamlessly with these environments. By intelligently managing data across on-premises, public cloud, and private cloud storage, AI-enhanced systems offer a unified storage platform that simplifies data management and accelerates application performance. This integration allows organizations to benefit from the strengths of different storage environments. Simultaneously, it ensures the necessary scalability and flexibility to accommodate data growth. Streamlined Data Migration Processes AI-driven storage solutions can also simplify and streamline data migration processes, making it easier for organizations to scale their storage infrastructure as needed. By automating data movement between different storage tiers or systems, AI-enhanced storage solutions reduce the time and effort required for data migration while minimizing the risk of data loss or corruption. This enables organizations to rapidly adapt to changing storage needs and maintain high levels of performance and availability. Preparing for the AI-Driven Data Storage Future The Importance of Choosing the Right Data Storage Solution As AI continues to transform the data storage landscape, organizations should carefully evaluate their storage needs. It is important to select solutions that align with their business objectives. Factors such as scalability, performance, security, and cost-effectiveness should be carefully considered when choosing an AI-enhanced storage solution. Furthermore, organizations should assess the level of AI integration and automation provided by various solutions. This will help ensure they can effectively leverage the benefits of AI-driven data storage. Investing in AI and Data Engineering Talent To fully capitalize on the potential of AI-driven data storage, organizations should invest in building and nurturing a skilled workforce of AI and data engineering talent. This includes hiring experts in machine learning, data analytics, and storage technologies, as well as providing ongoing training and development opportunities for existing staff. This focus on technical skill-building ensures the organization is equipped to implement and manage AI-enhanced storage solutions effectively. Embracing a Data-Driven Culture and Mindset Organizations looking to thrive in the AI-driven data storage future must adopt a data-driven culture and mindset. This goes beyond technical teams and involves the entire organization adopting a mindset where data is viewed as a crucial business asset. By fostering a culture of data literacy and encouraging the use of data-driven insights throughout the organization, businesses can better leverage the potential of AI-enhanced storage solutions and drive competitive advantage. Final Word The integration of AI in data storage has the potential to radically transform the way organizations store their data. By embracing AI-enhanced data storage solutions, organizations can not only optimize their storage infrastructure but also unlock new opportunities for growth and innovation. The benefits of AI-driven data storage go beyond cost savings and scalability. These solutions enable businesses to make more informed decisions and enhance operational efficiency. Additionally, they help maintain a competitive edge in an increasingly data-driven world. As the world of data storage and management continues to evolve at a rapid pace, it is crucial for organizations to stay ahead of the curve and embrace emerging technologies. LIKE.TG Software offers cutting-edge data management solutions that leverage the power of AI and automation. Contact us today to find out how we can help you reduce costs and improve scalability. Power your Data Storage with AI Take your data storage and management to the next level using LIKE.TG’s wide range of AI-powered tools. Applicable to a variety of use-cases. Learn more about how we can serve you! Discover what we offer!
AI-Powered Data Extraction: Revolutionizing Business Intelligence 
AI-Powered Data Extraction: Revolutionizing Business Intelligence 
Imagine a world where businesses can effortlessly gather structured and unstructured data from multiple sources and use it to make informed decisions in mere minutes – a world where data extraction and analysis are an efficient and seamless process. This is the world of AI-powered business intelligence, where AI does the heavy lifting and humans reap the rewards. Extracting Value: Unleashing Business Intelligence through Data Business intelligence (BI) refers to the practice of using data to gain insights and drive decision-making. An integral part of this process is data extraction, which involves collecting data from multiple sources and transforming it into a usable format. Traditionally, data extraction is performed manually, which involves hand-keying data from different sources and formats, such as spreadsheets, websites, and documents. However, the manual approach faces challenges in effectively handling the large volumes of data produced today. Furthermore, manual extraction poses challenges in maintaining accuracy, quality, and consistency across different sources, and limits the ability to make real-time decisions, which is crucial in today’s fast-paced business environment. The Power of Synergy: AI and Data Extraction Transforming Business Intelligence The technologies of AI and Data Extraction work in tandem to revolutionize the field of Business Intelligence. AI can analyze vast amounts of data but needs high-quality data to be effective. This is where data extraction comes in, providing AI with the data required to identify patterns and generate valuable insights. AI-powered data extraction automates the process of extracting information from various sources, saving time and reducing the risk of errors. An AI-powered data extraction tool can recognize specific types of data, such as names, addresses, or phone numbers, and extract these field values without human intervention. AI-driven Data Extraction involves three key components: data preprocessing, feature extraction, and machine learning: Data Preprocessing: Transform and clean raw data for machine learning usability, handling missing values and outliers, and scaling the data. Feature Extraction: Identify relevant features, reduce dimensionality, and extract meaningful representations from the data for prediction. Machine Learning: Train the algorithm to identify patterns, generate insights, and apply appropriate machine learning techniques based on the problem. The synergies between AI and Data Extraction are revolutionizing how businesses collect and analyze data, enabling them to focus on more important tasks and make real-time decisions based on accurate and timely information. Benefits of AI-Driven Data Extraction for Business Intelligence (BI): Improved Accuracy and Efficiency: AI-powered data extraction boosts accuracy and efficiency by precisely extracting diverse data, eliminating manual entry and analysis. It can process large volumes of data effortlessly, enabling BI professionals to work with reliable, high-quality data. Enhanced Decision-Making Capabilities: AI-powered data extraction helps gain valuable insights into large volumes of data that would be difficult to uncover manually. It enables businesses, especially in finance and other data-driven industries, to make informed decisions based on accurate and timely information. Scalability and Adaptability: AI-powered data extraction is highly scalable and adaptable, making it ideal for businesses relying on BI. It efficiently handles data extraction from any number of sources and can adapt to new sources as they emerge. Cost Reduction and Time Savings: Automated data extraction saves time and money by eliminating manual entry and analysis. It enables resource allocation to other business areas and reduces the risk of errors, saving businesses from costly mistakes. Transforming Industries with AI-Driven Data Extraction The convergence of AI and data extraction is transforming industries, enabling efficient access, analysis, and utilization of large amounts of data, resulting in data-driven decision-making. Let’s explore some real-world applications of AI-driven data extraction across different industries: Revolutionizing Financial Services and Fraud Detection AI-powered data extraction has transformed the financial services industry, particularly in fraud detection. By scrutinizing real-time transaction data, AI systems swiftly identify fraudulent activities and patterns, bolstering fraud prevention measures. This invaluable technology has saved financial institutions millions of dollars in potential losses. AI-driven data extraction also facilitates personalized services. By analyzing customer data, financial institutions offer tailored investment advice and financial planning, elevating customer satisfaction and loyalty. Advancing Healthcare and Medical Research In the realm of healthcare, AI-driven data extraction plays a pivotal role. Medical professionals enhance diagnosis and treatment outcomes by unveiling insights from copious patient data. A prime example is AI’s ability to analyze medical images like X-rays, MRIs, and CT scans. These systems detect subtle abnormalities, enabling faster and more accurate diagnoses and, ultimately, improving patient well-being. Optimizing Supply Chain and Logistics with AI AI-driven data extraction is revolutionizing supply chain and logistics management, empowering businesses with real-time visibility into operations. By analyzing vast data streams from multiple sources, AI optimizes inventory levels, predicts demand, and identifies bottlenecks. This streamlines operations, reducing lead times, improving delivery efficiency, and heightening customer satisfaction. Embracing the Future: AI-Powered Data Extraction for Business Intelligence AI-powered data extraction has emerged as a transformative force in business intelligence. The synergy between AI and data extraction enables businesses to unlock hidden insights, make accurate predictions, and optimize operations in real time. However, businesses need an end to end data management solution with built-in AI capabilities to harness the full potential of AI-driven data extraction. AI-driven data extraction tools, such as LIKE.TG’s code-free solution, are designed to automate and streamline unstructured data extraction. The intuitive, zero-code environment allows organizations to pull important unstructured data trapped in business documents efficiently and precisely within seconds. Such tools open the doors to a world of endless possibilities, where data becomes a powerful asset, and businesses gain the competitive edge they need to thrive in today’s dynamic marketplace. Learn more here or get in touch to see how LIKE.TG can help you unlock actionable insights for smarter decision-making. Revolutionize Your Business Intelligence with LIKE.TG’s AI-Powered Data Extraction Embrace the future of data management with LIKE.TG's AI-powered, code-free data extraction solution. Discover how our platform can unlock hidden insights and drive smarter decision-making for your business. Schedule a Demo
Extracting Actionable Insights from Learning Analytics Data with AI: Enhancing Teaching and Learning 
Extracting Actionable Insights from Learning Analytics Data with AI: Enhancing Teaching and Learning 
Enhancing Student Success with AI-Driven Learning Analytics Every school strives for a classroom where every student is engaged, actively participating, and making progress at their own pace. Sounds like a dream, right? Well, it’s not too far off with the help of learning analytics and AI. These tools enable educators to gain insights into student behavior and performance, personalize teaching to student needs, and identify areas where students may struggle. By leveraging data to inform teaching practices, educators can create a more effective and equitable educational experience for all students. Moreover, with learning analytics, teachers can harness data to empower students and enable them to thrive in an ever-changing world. The Power of Learning Analytics In today’s digital education age, learning analytics is a powerful tool for improving student success. According to a report by MarketsandMarkets, the global learning analytics market is expected to reach US$8.2 billion at a compound annual growth rate (CAGR) of 21.5% during 2019-2024. This growth is being fueled by the adoption of digital learning tools post-pandemic. These tools offer remote access to educational materials, foster seamless communication between teachers and students, and enable individualized instruction. With these advanced technologies, educational institutes can analyze a wealth of data to gain insights into student conduct and progress. Through learning analytics, instructors can tailor their teaching methods to better support students’ individual needs. They can also identify at-risk students early on to provide personalized interventions for academic success. The potential of learning analytics is enormous and expected to proliferate in the coming years. Using Learning Analytics to Customize Teaching to Student Needs As the traditional one-size-fits-all approach to teaching becomes outdated, educators are now turning to technology to improve student outcomes. Consequently, learning analytics has emerged as a widely accepted tool that empowers educators to tailor their teaching strategies and provide targeted support, unlocking the potential of every student. Here are some ways that learning analytics helps to customize the learning experience: Identifying Student Learning Styles Learning analytics can provide insights into how individual students learn best. Analyzing data on student interactions with course materials and assessments can help teachers identify patterns in student learning styles. They can then adjust their teaching methods accordingly. For example, if a student learns best through visual aids, educators can incorporate more visual elements into their teaching materials. Providing Targeted Support Educators can use learning analytics to identify at-risk students needing additional support to succeed. By analyzing student performance, behavior, and engagement data, educators can identify struggling students and provide targeted interventions to help them succeed. For example, suppose students are not engaging with course materials or having difficultyconsistently performing poorly on assessments. In that case, educators can intervene early and provide personalized support. This can help students re-engage with course materials and improve their performance. Enhancing Student Engagement By providing personalized instruction and support, learning analytics can enhance student engagement. For instance, when students receive individualized support, they are more likely to be motivated and engaged in their coursework. As a result, they are more likely to succeed academically. This approach creates an inclusive learning environment where every student has the opportunity to excel. Unstructured Data Extraction: Turning Raw Data into Actionable Insights In the age of e-learning and mobile learning, the amount of data generated by students and instructors is growing exponentially. However, most of this data is unstructured and lies dormant, leaving educators clueless to pursue new ways to extract valuable insights that can shape the learning journey for students. What kind of unstructured data are we talking about? Consider sources such as student feedback, discussion forums, social media posts, and emails. Analyzing this data can provide deep insights into student preferences and learning outcomes. Fortunately, AI and machine learning advancements have paved the way for institutes to unravel the valuable insights hidden in these unstructured data sources. Modern AI-powered data extraction tools can effectively process and analyze unstructured data. Let’s look at some techniques that educators can use to guide the decision-making process: Text Mining: By using natural language processing (NLP) algorithms, text mining tools can analyze unstructured textual data, like discussion forums and student feedback, to identify patterns and areas of improvement. For example, NLP can be used to analyze student behavior and provide personalized recommendations for further study. Social Media Analysis: Social media platforms like Twitter and Instagram can provide valuable insights into student opinions and sentiments. With sentiment analysis tools, educators can analyze the tone and emotions expressed in social media posts. This will give them a better understanding of their students’ needs and experiences. LMS Data Analysis: Learning management systems generate vast amounts of data on student behavior, such as time spent on course materials, engagement with discussion forums, and quiz scores. Using data analytics tools, educators can identify patterns in student behavior, like areas of difficulty or low engagement, and adjust their teaching strategies accordingly. Predictive Analytics: Predictive analytics can forecast student performance based on historical data. By leveraging machine learning algorithms, educators can identify early warning signs of at-risk students. Consequently, they can provide targeted interventions to prevent students from falling behind. Best Practices for Implementing Learning Analytics Implementing learning analytics can be daunting, but the right approach can lead to improved student success and more personalized learning experiences. Here are three key best practices steps for incorporating learning analytics to enhance teaching and learning: Defining clear goals and objectives: Before implementing learning analytics, it’s crucial to identify the specific goals and objectives the institution wants to achieve. This ensures the data collected is relevant and helps achieve the desired outcomes. It’s also important to clearly communicate these goals to all stakeholders, including students, faculty, and administrators. Integrating data from multiple sources: Learning analytics involves collecting and analyzing data from various sources, such as student information systems, learning management systems, and social media. Integrating this data and ensuring its accuracy, completeness, consistency, and privacy is essential. This requires using a data integration platform to extract, transform, and load (ETL) data from different sources into a centralized repository. Ensuring data privacy and security: Protecting sensitive student information is crucial in learning analytics. Institutions must have clear policies and procedures for data collection, storage, and usage, including data anonymization and encryption. Data access should be limited to authorized personnel, and compliance with relevant data protection regulations such as GDPR and CCPA is essential. Learning Analytics: Shaping the Future of Education As we continue to navigate the ever-evolving landscape of education, it’s clear that AI-driven learning analytics will play a critical role in shaping the future of student success. By embracing these technologies and leveraging their potential, educators and institutions can empower learners to achieve their goals and reach their full potential. But to fully harness the power of AI-driven learning analytics, it’s important to have a reliable data extraction tool like LIKE.TG ReportMiner. With its advanced data extraction capabilities, ReportMiner can help you easily extract and integrate data from various sources to create a unified student database. Ready to unlock the potential of AI-driven learning analytics? Contact us now and take the first step towards a brighter future for education!
Tackling Layout Variability in Data Extraction Using AI
Tackling Layout Variability in Data Extraction Using AI
Data extraction is a critical component of modern data processing pipelines. Businesses across industries rely on valuable information from a range of documents to optimize their processes and make informed decisions. One commonly employed method for data extraction is the traditional template-based approach. This technique involves creating predefined templates or rules that define the expected structure and data fields within the documents. These templates instruct the extraction system on where and how to locate and extract the relevant data fields. The extraction system matches the document against these templates and extracts the data accordingly. When using traditional template-based data extraction, various aspects need to be considered to ensure seamless data retrieval from such documents, such as: Document structure inconsistencies that can hinder the extraction process. The time-intensive nature of template creation, which demands significant resources. The potential for errors during the extraction procedure, posing a risk to data accuracy. Scalability issues that may limit the ability to efficiently handle a growing volume of documents. Maximum Accuracy and Efficiency: The Impact of Automated Data Extraction If we consider that creating a template for a single invoice takes approximately 20-30 minutes and there are 20 invoices with varying layouts, it would require a total of 30 * 20 = 600 minutes, equivalent to 10 hours, to complete the template creation process. This time-consuming process highlights the need for more advanced and efficient data extraction techniques to manage diverse document layouts. Therefore, modern businesses are exploring a hybrid approach that combines the efficiency of template-based data extraction with the power of advanced language models, such as OpenAI’s GPT or other similar large-scale Language Models (LLMs), to streamline the process of data extraction and tackle the problem of creating templates. Integrating generative AI into the data extraction pipeline can significantly reduce the time and effort required for template creation. That’s where LIKE.TG ReportMiner comes in. AI-powered data extraction in ReportMiner can quickly and accurately extract data from a variety of document types. This feature allows extracting data from purchase orders and invoices with varying layouts without hassle. Use Case: Automating Purchase Order Data Extraction with LIKE.TG ReportMiner Let’s consider a use case. SwiftFlow Services Inc. (SFS) must manage a daily influx of purchase orders from various vendors received via email. Each day, they receive approximately 10 to 20 purchase orders, with each vendor presenting a unique purchase order layout. SFS aims to extract specific fields from these purchase orders and store the data in a database for further analysis, such as evaluating vendor performance, identifying cost-saving opportunities, and optimizing supply chain management. SFS wanted an efficient and streamlined solution that could effortlessly extract the required information without requiring manual template creation. Therefore, they chose LIKE.TG’s AI-powered data extraction solution. Users must only specify the document type and desired layout for extraction, and the system harness AI’s context-building ability to extract the information and generate templates consisting of regions and fields using heuristics. The tool automatically creates templates for all sources within a folder at the project level. Acknowledging the importance of human feedback, the system stores any problematic templates (RMDs) that require user adjustments in a designated folder. After RMD verification and customization per business requirements, users can create a workflow to loop through these RMDs and write the extracted data to a destination. A Data Quality Rules object further enhances efficiency by ensuring the extracted data adheres to the specified business rules, resulting in faster and more accurate data retrieval. By simplifying and automating the data extraction process, SFS can reduce manual labor, improve the accuracy of extracted data, and focus on more critical tasks in its data processing pipeline. Check out this video to learn more: If you want to learn more about ReportMiner, contact our sales team to schedule a demo today. Explore LIKE.TG's AI-Powered Data Extraction Tool Explore ReportMiner!
Mastering Retail: Picking the Right Invoice Data Extraction Tool 
Mastering Retail: Picking the Right Invoice Data Extraction Tool 
In the fast-paced world of retail, data is the cornerstone of decision-making, strategic planning, and customer relations. One particular type of data that stands out is invoice data. It’s a treasure trove of unique insights that, when utilized effectively, can drive business growth. In fact, the Billentis Report indicates that global invoice exchanges topped 500 billion in 2019, and this number is set to quadruple by 2035. Thanks to the rise of artificial intelligence (AI) and automation, working with this data has become easier and more efficient. New-age data management tools, powered by these technologies, offer a smart way to not just manage but also analyze invoice data effectively. In this blog, we’ll guide you through the process of choosing the right invoice data extraction tool for your retail business. The Evolution of Data Management Tools in Retail Data management tools have been critical in the retail sector, playing a key role in storing, organizing, and analyzing data. Over time, we’ve seen these tools evolve from basic spreadsheet programs to complex database systems, and now to AI-powered solutions. The Advent of AI-Powered Tools In the current marketplace, we see a diverse range of data management tools, from data warehouses and data lakes to advanced database management systems. These tools each have their unique strengths and use cases, catering to different aspects of retail operations. However, the game-changer has been the emergence of AI-driven tools. These solutions do more than just store and organize data; they enable smart interpretation and analysis, providing insights that were beyond the reach of traditional tools. For instance, they can analyze sales trends and customer behavior patterns, offering retailers predictive insights to strategize their sales and marketing efforts effectively. The Multifaceted Role of AI in Data Management AI’s potential to revolutionize retail data management is enormous. It can automate repetitive tasks, such as invoice data extraction, freeing up staff to focus on strategic initiatives. Moreover, it can spot trends and patterns in large volumes of sales data and predict future outcomes based on historical data. For instance, consider the task of inventory management, a critical aspect of retail operations. Using AI, retailers can analyze past sales data, identify patterns related to specific seasons or promotional periods, and predict future inventory requirements. This predictive capability helps retailers maintain optimal stock levels, minimizing the risk of overstocks or stock-outs and leading to cost savings and improved customer satisfaction.AI and Invoice Data Extraction: A Powerful Partnership When it comes to invoice data extraction, the power of AI lies in its ability to deal with diverse invoice formats and large data volumes. By automating this process with AI, retailers can enhance accuracy, save time, and allocate their resources to more strategic tasks. But how does this look in a practical retail scenario? Let’s take a large retail chain as an example. This retailer receives thousands of invoices weekly from a multitude of suppliers. Each invoice has a unique format, containing critical information such as product IDs, quantities, and prices. Traditionally, the data extraction process would involve manual data entry, which is time-consuming and prone to errors. With an AI-driven invoice data extraction tool, the retailer can automate this process. The AI tool can recognize and extract the relevant data from the diverse invoice formats, significantly reducing the time spent on data extraction and increasing overall accuracy. The extracted invoice data, which includes valuable insights into purchasing trends and supplier performance, can then be automatically pushed through data pipelines for payment processing, reporting, and analytics. This allows the retailer to gain a comprehensive view of their operations, enabling them to make more informed decisions. In essence, the use of AI in invoice data extraction empowers retailers. It allows them to leverage their data effectively, anticipate customer needs, optimize operations, and make strategic, data-driven decisions. This ability is vital in maintaining a competitive edge in the ever-evolving retail market. Key Considerations for Retailers When Selecting an AI-Enhanced Invoice Data Extraction Tool Incorporating an AI-enhanced invoice data extraction tool into your operations is a significant decision. It’s not just about using new tech—it’s about finding the tool that perfectly fits your business requirements. Here are some key points to consider during this selection process: Ease of Use and User Experience: In retail, time is of the essence. You need an invoice data extraction tool that’s intuitive and user-friendly, reducing the need for extensive training. Look for features which can make tasks like processing returns, managing inventory, and analyzing sales data much more efficient. For instance, a tool with drag-and-drop functionality can expedite tasks such as managing returns or processing inventory. This feature allows users to easily manipulate data and generate insights, saving time and reducing the likelihood of errors. Similarly, easy template creation is another feature to consider. In retail, templates can be used for a variety of tasks, including sales data analysis or tracking vendor invoices. An invoice data extraction tool that offers easy template creation can simplify these tasks, making it quicker to get the insights you need to make informed decisions. AI Capabilities: In the fast-paced retail sector, you need a tool that can keep up with diverse invoice formats and large volumes of data. A sophisticated AI-powered tool will be able to extract data from various invoices automatically, freeing up your team to focus on strategic tasks like customer engagement and product selection. Additionally, the data will be automatically pushed through data pipelines to the desired destination for payment processing, reporting, and analytics. Connectivity: The ideal tool should be able to seamlessly connect with various databases and integrate with your existing software infrastructure. It should support data import and export in multiple formats and provide APIs for custom integrations. It should be able to connect with various databases and integrate with your existing retail software, such as inventory management and point of sale (POS) systems. This way, you can easily link the extracted invoice data with product and sales data, giving you a holistic view of your operations. Data Security: Given the sensitive nature of invoice data, which could include vendor contracts and pricing details, strong security measures are essential. Look for tools that offer strong encryption and comply with data protection regulations like GDPR or CCPA. By doing so, you can ensure the security of your business and your vendors’ information. Scalability: Another critical aspect to consider is the scalability of the chosen tool to adapt to your evolving business needs and increasing invoice volumes. It’s essential to select a tool that can seamlessly scale alongside your business, effortlessly managing larger data volumes without compromising performance. For businesses operating in retail, especially during peak seasons like the holidays, invoice volumes can significantly surge. Your tool must be able to effectively handle these peak periods without experiencing performance degradation. This will ensure that your back-end operations remain efficient and do not become a bottleneck. Customer Support: It’s crucial to evaluate the level of customer support offered by the vendor of the chosen tool. Prompt and reliable support can make a significant difference, particularly during the initial stages of adopting the tool and integrating it into your business processes. A responsive support system that addresses any queries, concerns, or technical issues in a timely manner can greatly enhance your overall experience and ensure a smooth implementation and utilization of the tool. Conclusion With the number of invoice exchanges in the world expected to quadruple in the coming years, choosing the right AI-enhanced invoice data extraction tool is a decision that can significantly impact your retail business. It’s a decision that requires careful consideration of several factors, encompassing AI capabilities, scalability, ease of use, security, and customer support, to ensure the tool aligns seamlessly with your invoice data extraction needs. LIKE.TG ReportMiner leads the pack in data extraction with its advanced features, ease of use, and strong security. Ready for a revolution in your invoice data management? Explore the transformative power of AI with LIKE.TG Software. Request a demo today and kickstart your journey towards streamlined, efficient invoice data management.
Revolutionizing Retail Invoicing: How Automated Data Extraction Can Boost Efficiency and Save 80% Time 
Revolutionizing Retail Invoicing: How Automated Data Extraction Can Boost Efficiency and Save 80% Time 
In the highly competitive retail sector of today, time is of the essence. Manual data extraction processes are laborious, error-prone, and consume valuable resources that could be better utilized elsewhere. But this is where automated invoice data extraction comes to the rescue. By harnessing the power of AI technology through automated data extraction, retailers can revolutionize their invoice processing, unlocking significant time savings and boosting overall efficiency. Invoice processing—which normally takes days to complete—can now be done within a couple of minutes. Currently, the average time for processing invoices manually stands at 10.9 days per invoice. For retailers, longer invoice lifecycles beget account payable and inventory delays. Additionally, most organizations find the cost of invoice processing to be between $15 and $40 per invoice. With these numbers in retrospect, let’s look at how a large retail chain has cut down their invoice lifecycle by nearly 80% using automated invoice data extraction. What is Automated Invoice Data Extraction? A retailer receives multiple invoices against purchase orders every day. The invoices shared by vendors or suppliers are first processed through the accounting and finance departments. They pull out the necessary data—ofttimes manually entering it into enterprise databases—and process payments accordingly. Similarly, other departments like Supply Chain need invoices to update their own inventory records. Automated Invoice Data Extraction is a process that uses either logical templates or Artificial Intelligence (AI) to automatically extract data from invoices, including purchase order numbers, vendor information, and payment terms. The more modern AI-driven extraction autonomously identifies, extracts, validates and then stores data without any manual intervention—eventually reducing invoice processing time to mere minutes. Adding Automation to Invoice Processing: Success Story of a Retail Chain A US-based supermarket chain, with several branches spread across North America, receives a little over 300 invoices from different suppliers each day. Processing these invoices in a timely manner, most of which are still shared in PDF or paper format, posed a real challenge to the retailer. On top of that, each invoice document—with its own distinct layout—carried long lists of goods being ordered for broad categories of products. The retailer had a ten-person team responsible for extracting information, such as order numbers, vendor information, dates, shipping details etc., and entering it into the system manually. On average, it took the retailer 15 days (about 2 weeks) to process the invoices—from data extraction to payment. Consequently, the inefficient process was time-consuming and error-prone, causing delays in account payables, data quality discrepancies, and supply-chain disruptions. To make matters worse, there was a growing trust deficit with the suppliers due to late payments and ofttimes incorrect invoicing details. How did the retailer circumvent this challenge? They replaced manual invoice processing with an automated invoice data extraction solution. And the results were magical! By implementing automated data extraction, they were able to replace the manual invoice processing approach with an agile one. What was otherwise being done manually by multiple resources is now handled by a single AI-driven solution. It automatically recognizes relevant fields on the invoice and extracts and loads data for consumption. This has significantly reduced the time and resources needed to process invoices. Saving Time and Improving Efficiency through Automated Data Extraction An internal audit had earlier revealed that the supermarket retailer’s inefficient invoice processing was inadvertently causing the company thousands of dollars each year. This changed with the implementation of automated invoice data extraction. The invoice processing lifecycle—which was initially taking 15 days to complete—was reduced to a mere 2 days. That is a near 85% reduction in the time spent on invoice data extraction, loading, and the eventual payment processing. This has led to timely account payments, satisfied vendors, and zero stalk-outs due to seamless inventory management. But that is not it. With AI-driven data extraction in place, invoice processing has become nearly self-serving. The resources manually extracting data from invoices are now focusing on more important, less redundant tasks e.g., financial analytics and supply chain management. Additionally, the retailer is reporting a decrease in data discrepancies and quality issues. This is precisely because the new data extraction solution eliminates human errors and validates the data automatically before loading into the database. How the Retailer Implements AI-driven Invoice Data Extraction The supermarket chain is revolutionizing retail invoicing by going automated! The benefits in terms of 80% time-savings, inventory management, and data quality are unprecedented in the retail sector. Let’s take a look at how our retailer leverages AI and automation for invoice data extraction. Invoice Capture: The retailer receives hundreds of invoices each day in different formats. For example, some vendors share PDF invoices while others email images or text files. The first step is to capture these invoices automatically, recognize their distinct format, and convert them to optimized electronic copies. Electronic copies are better for data extraction purposes. Here, the system is running on self-service which means that invoices are captured automatically as they arrive digitally through email alerts. AI-Template based Data Extraction: It then uses AI-template based data extraction for pulling data from captured invoices, irrespective of their layouts. Here, self-serving AI utilizes Natural Language Processing (NLP) to automatically generate a template based on the fields or data required by the user. For example, the retailer identifies the fields it needs data for—such as order number, vendor information, dates, shipping details, etc.—and the AI itself extracts relevant data from any type of invoice in no time. It autogenerates flexible templates based on the different layouts eliminating the need for creating new templates for every distinct invoice. And you know what’s best? Since AI-based templates are created through machine learning algorithms, they are highly accurate and reliable. For example, they recognize even small discrepancies in invoicing terms and still be able to extract relevant data. Data Quality and Validation: Once the data is extracted from the invoices, it is validated and cleansed for consumption. The retailer’s automated data extraction uses pre-built checks to automate the process of data validation and cleaning. The cleansed data is then converted to JSON to ensure compatibility with the retailer’s other data processing systems. Invoice Processing Approval: Next, the invoice is shared with the retailer’s finance department for approval and processing of account payable. Rather than sending extracted data manually, the retailer has set up a workflow which automatically alerts the approval section of the finance department whenever a new invoice is received. The workflow routing is based on AI algorithms that seamlessly share the data with relevant stakeholders. Finally, after due approval, the account payable for that invoice is cleared by finance. Integration With Other Systems: The retailer couples the ability to intelligently recognize data with seamless integration with other systems in the invoice data pipeline. Their AI-driven data extraction solution provides native support for popular cloud databases or financial management systems such as Quickbooks, SAP, Oracle, Snowflake etc. This means that the retailers can automatically pass on the invoice information to relevant systems/databases through self-serving workflows. Here, deep interoperability with other systems ensures that the retailer’s invoice data is not processed in isolated silos. Other departments, such as Supply Chain or Auditing, are also able to access this data for meaningful analytics. LIKE.TG ReportMiner: The Ultimate AI-Driven Invoice Data Extraction Tool for Retailers Retailers deal with large volumes of invoices every day. With an exponential increase in their business activities, manual processing of these invoices is affecting their efficiency and productivity. Here, an AI-powered data extraction solution offers to revolutionize invoice data extraction and processing. Equipped with LIKE.TG ReportMiner, retailers can follow in the footsteps of the aforementioned retail chain and reduce their invoice processing lifecycle by more than 80%. Our way of invoice data extraction provides several advantages over other manual methods, including improved accuracy, consistency, speed, and flexibility. Essentially, LIKE.TG ReportMiner empowers retailers to extract data from unstructured invoices using cutting-edge AI capabilities. With advanced AI Capture technology, our tool enables you to build reusable extraction templates in seconds, extract relevant data, and process it using robust data pipelines or workflows. Want to experience LIKE.TG ReportMiner’s magic? Sign up for a free 14-day trial today and gear up to revolutionize retail invoicing.
Transforming Policy Development: The Role of Automated Legal Data Extraction Tools 
Transforming Policy Development: The Role of Automated Legal Data Extraction Tools 
Policy development plays a critical role in government agencies within the legal sector, shaping a society’s social, economic, and political landscape. Advances in technology have ushered in a new era of policy development, with automated legal data extraction tools emerging as a game-changer for legal professionals in government institutions. Exploring the Power of Automated Legal Document Data Extraction Tools Automated legal document data extraction tools are software programs that use natural language processing and machine learning algorithms to extract relevant data and insights from legal documents. These documents include laws, regulations, court cases, and other legal documents relevant to government policymaking. Automated legal document data extraction tools are becoming increasingly important in the legal industry. They can help lawyers and policymakers quickly and accurately extract relevant information from large volumes of legal documents. This can save time and resources and help to inform policy-making decisions. Some of the key features of automated legal document data extraction tools include: Text recognition: the ability to recognize and extract text from various legal document formats, including PDFs and Word documents. Entity recognition: the ability to identify and extract named entities such as people, organizations, and locations. Relationship extraction: the ability to identify and extract the relationships between named entities. Topic modeling: the ability to identify and extract the key topics and themes in a legal document. Empowering Government Organizations: The Advantages of Data Extraction Tools Some of the benefits of implementing data extraction tools in policy development include: Resource Reallocation: By freeing up human resources from manual data analysis, organizations can allocate them to other areas of operations where their expertise is required, optimizing resource utilization and maximizing productivity. Insights in Digestible Format: These tools present extracted data in an easily understandable format, such as summaries or visualizations, enabling policymakers to gain insights quickly and efficiently. This enhances their ability to derive meaningful conclusions from the data. Improved Policy Outcomes: Informed decision-making based on comprehensive data analysis leads to more effective and impactful policy outcomes. Data extraction tools help ensure that policies are evidence-based and aligned with the needs of the people. Focus on Policy Formulation: By automating data extraction and analysis, policymakers can dedicate more time and attention to policy formulation and strategic thinking. This allows for a deeper understanding of complex issues and the development of well-crafted policies. Accelerated Timelines: Faster data extraction and analysis enable government organizations to make policy decisions more promptly, keeping pace with rapidly evolving societal needs. This ensures that policies are timely and responsive to current challenges. Mitigating Biases and Errors: Objective data extraction reduces the risk of subjective interpretations and biases in data analysis. This promotes a more accurate and consistent understanding of legal documents and improves decision-making integrity. Standardized Information Sharing: Data extraction tools facilitate the sharing of legal information in a standardized format, making it easier for government organizations to communicate and collaborate. This enhances transparency, fosters trust, and allows for greater public scrutiny. Accessible and Open Governance: By leveraging data extraction tools, government organizations can improve transparency in decision-making processes and ensure accountability to the public they serve. This promotes open governance, where citizens can access and understand the legal basis for policies, fostering an inclusive and participatory democratic system. Real-World Applications of Data Extraction Tools in Government Policymaking Here are some examples of how automated legal document data extraction tools have been used to improve government policies: Case Study: Improving Public Health Policy The New York City Department of Health and Mental Hygiene used text mining and data extraction tools to analyze over 6,000 public health laws and regulations. This analysis provided insights into the prevalence of certain health issues and the effectiveness of existing policies. For example, the analysis found that there were gaps in policies related to mental health and substance abuse. As a result, the department developed and implemented new policies that focused on addressing these gaps. The use of data extraction tools helped the department develop more targeted and effective public health policies that improved the health outcomes of New York City residents. Case Study: Enhancing Environmental Regulations The United States Environmental Protection Agency (EPA) used automated legal document data extraction tools to analyze thousands of environmental laws and regulations. This analysis helped identify gaps and inconsistencies in the regulations, which were then addressed by developing clearer and more comprehensive regulations. For example, the analysis found that there were inconsistencies in regulations related to air pollution from industrial sources. The EPA used the insights gained from the analysis to develop new regulations that were more effective in reducing air pollution and protecting public health. Case Study: Optimizing Tax and Revenue Policies The Australian government used automated legal document data extraction tools to analyze tax laws and regulations. This analysis helped identify opportunities for revenue optimization and led to the development of more effective tax policies. For example, the analysis found that there were gaps in the tax code related to offshore tax avoidance. The government used the insights gained from the analysis to develop new policies that closed these gaps and generated additional revenue for the government. Conclusion Automated legal data extraction tools are revolutionizing policy development for government organizations. They have the potential to transform policy development in the legal sector, making it faster, more accurate, and more efficient. By leveraging the power of AI and natural language processing, these tools streamline research and analysis, provide valuable data insights, promote transparency and accountability, and overcome legal complexity and compliance challenges. Legal professionals must prepare for a data-driven policy development landscape by embracing and implementing automated legal data extraction tools. By doing so, they can improve the efficiency and effectiveness of policy development, making it possible to develop policies that are grounded in data-driven insights. Automate Document Data Extraction with LIKE.TG ReportMiner Download Trial
Beyond the Transaction: How Invoice Data Extraction Tools Strengthen Retail-Vendor Relations 
Beyond the Transaction: How Invoice Data Extraction Tools Strengthen Retail-Vendor Relations 
Exploring the untapped potential of retail data management for stronger partnerships In today’s fast-paced retail environment, building and nurturing strong relationships with vendors is essential. External goods and services can represent up to 70% of a company’s cost of goods, so even small savings in this area can lead to large improvements in a company’s profitability.  As the retail industry evolves, efficient retail data management becomes vital for transparency and collaboration between retailers and vendors. An invoice data extraction tool emerges as a valuable asset in strengthening these relationships. Strengthening Retail-Vendor Bonds with Invoice Data Extraction Tools Retailers and vendors are increasingly relying on invoice data extraction tools to enhance their operations. These tools help unlock the full potential of their data by automating the extraction of important information from different invoice formats. By streamlining the invoicing process, these tools extract crucial details like invoice numbers, item descriptions, quantities, and prices, with greater accuracy and efficiency. Here are some ways in which automating invoice data extraction can positively contribute to retail-vendor relations: Enhancing Collaboration and Transparency One of the key advantages of using invoice data extraction tools in retail data management is the enhanced collaboration and transparency they offer in retail-vendor relationships. Retailers can use these tools to easily share accurate and up-to-date invoice data with their vendors, allowing both parties to collaborate more closely to optimize their operations. For example, retailers can align vendor production schedules with sales forecasts and assess inventory levels in real-time to re-order efficiently and avoid stockout. Furthermore, the transparency provided by invoice data extraction tools fosters trust between retailers and vendors, which is necessary for long-term, mutually beneficial partnerships. Reducing Disputes and Strengthening Communication With these tools, retailers, and vendors have access to a single source of truth, reducing the potential for misunderstandings and discrepancies. For instance, a retailer manually inputs data from a vendor invoice and pays using the incorrect product price, causing a time-consuming dispute. With an automated invoice data extraction tool, all payments are processed with complete confidence, and conflicts of this nature are easily avoided. Streamlining Supply Chain Management Retailers can leverage an automated invoice data extraction tool and use this extracted data to manage optimal inventory levels, ensuring they always have the products in hand to meet customer demand. Here are some ways an automated invoice extraction tool can optimize a retailer’s supply chain operations: Invoice billing and processing without human touch: Invoices that are matched are effortlessly settled and allocated to the appropriate expenditure accounts, drastically lowering the invoice-associated costs. Enhance the management of operational capital: Businesses have the opportunity to take advantage of early payment benefits or extend the days payable outstanding to preserve cash. Minimize inaccuracies: Techniques based on artificial intelligence can potentially help companies save 30-40% of the hours usually devoted to these tasks, resulting in fewer mistakes and lessening the need for adjustments and withdrawals. Integration with other systems: These tools can often integrate with other business systems like Enterprise Resource Planning (ERP) or Supply Chain Management (SCM) systems, creating a seamless flow of information and improving overall operational efficiency. Better resource allocation: With less time spent on manual invoice processing, resources can be reallocated to areas that can drive more significant improvements in supply chain operations, such as supplier relationship management or strategic planning. Furthermore, these tools enable retailers and vendors to respond quickly to supply chain disruptions by providing real-time visibility into invoice data. With this visibility, both parties can take immediate action in response to sudden changes in demand or unexpected supply shortages. This agility and adaptability are essential for maintaining uninterrupted operations and ensuring customer satisfaction. Negotiating Vendor Terms Invoice data extraction tools can help tailor these relationships to individual needs by providing customized insights into each vendor’s performance. Most importantly, faster payment processing allows retailers to negotiate better terms and improve service levels to build stronger and mutually beneficial ties with vendors. These tools can also help retailers identify which vendors are meeting their expectations and which are falling short. This can lead to more productive discussions and help both parties work together to increase revenue. Ensuring Compliance and Reducing Risk Invoice data extraction tools are paramount in ensuring compliance and maintaining high standards within retail operations. These automated systems, equipped with validation checks, eliminate the potential for human errors, thereby enhancing the accuracy of data extraction. This high level of precision significantly boosts auditors’ confidence, as they can rely on the data’s accuracy. Such tools provide a comprehensive audit trail of all transactions, an invaluable resource during audits or inspections. By eliminating human errors, these tools inherently reduce the risk of understating payables, ensuring a more accurate representation of financial obligations. Fostering Scalability and Growth Invoice data extraction tools are designed to scale alongside your business. As a company’s retail operations grow, so does the complexity and volume of invoice data. However, these tools can easily handle an increase in data volume without compromising accuracy or efficiency. This scalability allows retailers to manage their growth effectively and ensures that their relationships with vendors continue to strengthen even as their operations expand. Conclusion Invoice data extraction tools are revolutionizing the way retailers and vendors manage their data and build relationships. By streamlining supply chain processes, enhancing collaboration and transparency, reducing disputes, improving compliance, and fostering scalability, these tools provide invaluable benefits to both parties. As the retail industry continues to evolve, retailers must embrace an automated invoice extraction tool to extract the best out of their vendor relations and remain competitive in the future of retail data management. LIKE.TG ReportMiner: A New Paradigm in Retail Data Management LIKE.TG ReportMiner is an invoice data extraction tool that guarantees 100% accuracy by combining the power of AI with template-based extraction; it uses AI to extract important fields of data and allows retailers to customize the template to extract precisely what they need, providing them with the ability to automate the entire invoice extraction process for each vendor.
The Power of Automated Medical Data Processing: Optimizing Insurance Rates for Effortless Savings 
The Power of Automated Medical Data Processing: Optimizing Insurance Rates for Effortless Savings 
Efficient medical claims processing is crucial for timely patient care and financial stability in healthcare. Processing claims swiftly and accurately ensures that healthcare providers receive prompt reimbursements, enabling them to maintain services, invest in staff training, and acquire advanced medical technologies. LIKE.TG is an advanced data extraction solution that is specifically designed to handle complex data extraction tasks, such as medical claims processing. It’s engineered to convert unstructured data, often found in medical claim forms, into structured, actionable information. Furthermore, LIKE.TG has an intuitive and user-friendly interface that allows users to ingest data from various sources, define extraction rules, and specify output formats without writing code. This makes it an ideal tool for healthcare providers and insurance companies who need to extract data from medical claims for analysis but do not have a technical background. Therefore, our solution expedites the claim settlement cycle, enhances operational efficiency, and benefits patients by facilitating quicker access to necessary healthcare services. Example Use Case: Medical Claims Processing An insurance organization processes hundreds of medical claims daily. Various healthcare providers submit these claims, each presenting crucial data such as patient details, diagnosis codes, and treatment procedures in unique formats. Traditionally, the administrative staff manually extracts this data, a process that is time-consuming and susceptible to human errors. LIKE.TG offers an automated and efficient solution to these challenges. With its rich set of features tailored for handling the complexities of medical claims data, it simplifies and automates the data extraction process. The solution enables the configuration of specific extraction parameters, allowing the system to intelligently identify and pull relevant information from various document types, including: PDFs. Excel sheets. Or even scanned documents. By choosing LIKE.TG, insurance organizations can significantly reduce the risk of errors and improve the claims processing time, thereby improving its overall efficiency and service quality. A Step-By-Step Guide to Streamlining Claims Processing As a large-scale insurer processing a substantial volume of medical claims daily, you encounter challenges with claims submitted by different healthcare providers containing critical data in diverse formats. You’re looking for a solution to efficiently extract, process, and analyze this data to expedite claims processing. Here’s how you can use LIKE.TG’s data stack to streamline your medical claims processing: 1. Receiving Claims The process starts when you receive healthcare claims on a regular basis from various healthcare providers. Each claim is a unique document, rich with vital information that needs to be accurately processed. These claims are promptly stored in a dedicated folder upon receipt. Healthcare Insurance Claim Form in PDF Format 2. Data Extraction Next, you can use LIKE.TG to automate the extraction of relevant data from these claims. LIKE.TG’s AI recommended layout feature allows you to automatically generate models for multiple source files at once. This can be achieved by specifying the data layout that needs to be extracted from the document, which can be done manually, through an existing LIKE.TG artifact, or in JSON format. This feature, coupled with its ability to handle different formats and complex hierarchical data structures, ensures accurate and efficient data extraction. Automating data extraction using LIKE.TG 3. Data Verification and Validation After the data is extracted, it is ready for validation. By employing the report model as a source within dataflows, you can effortlessly utilize LIKE.TG to ensure the accuracy, consistency, and integrity of the data through rigorous validation and verification processes tailored to meet its specific requirements. Verifying details from the insurer’s database Key steps in this phase include cross-verifying insurer details in the claim form with the company’s database, validating the requested claim amount against policy limits, and generating a unique claim ID for each new record. Data transformations in LIKE.TG 4. Workflow Automation The final step involves automating the entire workflow. Upon detection of a new claims file in the directory where all our unstructured claims appear, the dataflow, configured to use the source file path information, initiates real-time data extraction and transformation. Once processed, the file is moved to a designated folder, ensuring an organized workflow. To maintain efficiency, a scheduler is deployed to monitor the directory for unprocessed claims. LIKE.TG’s file drop automation feature ensures that the arrival of a new file automatically triggers the workflow, eliminating manual intervention and enhancing the overall efficiency of the claims processing operation. Scheduling claims file drop automation in LIKE.TG Utilizing LIKE.TG’s unstructured data extraction solution, each claim is processed in real time, ensuring all claim records are swiftly and accurately added to the repository. Medical claims data is consolidated in an Excel File On structured, consolidated claims records, you can leverage LIKE.TG’s advanced data preparation capabilities to prepare it for analysis. This involves cleansing the data to remove any inaccuracies, filling in missing values, and eliminating duplicates. LIKE.TG provides instant data preview, allowing you to modify and monitor the data in real-time. Moreover, its profile browser, displayed as a side window, allows you to assess the data’s health, ensuring that it is clean, accurate, and ready for analysis. Data profiling and preparation using LIKE.TG Benefits of LIKE.TG in Medical Claims Processing LIKE.TG offers many benefits that significantly simplify the process of medical claims processing: Automation: LIKE.TG plays a pivotal role in automating the data extraction process within medical claims processing, reducing the reliance on manual efforts. By streamlining the handling of diverse medical data formats, the tool introduces efficiency into a traditionally time-consuming task, allowing healthcare organizations to process claims with greater speed and accuracy. Enhanced Accuracy: The intelligent extraction capabilities of LIKE.TG significantly enhance the accuracy of medical claims processing. The tool is adept at precisely identifying and retrieving relevant information from various document types, minimizing the risk of errors that could impact the integrity of critical data points, including patient details, treatment histories, and billing codes. Efficiency Gains: LIKE.TG contributes to efficiency gains by accelerating the overall claims processing timeline. This not only expedites the reimbursement cycle but also frees up valuable resources that would otherwise be dedicated to manual data entry. The result is a more streamlined and resource-efficient workflow for healthcare providers. Time Savings: The rapid and consistent data extraction facilitated by LIKE.TG translates into significant time savings for healthcare organizations. The automation of labor-intensive processes enables staff to allocate their time more strategically, focusing on tasks that require human expertise and contributing to a more effective and agile healthcare environment. Adaptability: LIKE.TG’s adaptability to diverse data formats commonly encountered in the healthcare sector, including PDFs, Excel sheets, and scanned documents, underscores its versatility. By ensuring seamless integration with existing systems, LIKE.TG proves to be a robust solution that aligns with the dynamic nature of medical claims data and supports the interoperability of various data sources. Final Word LIKE.TG is a powerful tool that empowers healthcare providers to streamline their medical claims processing. Its suite of features, including automated data extraction, advanced pattern recognition, and AI-recommended layouts, are designed to enhance productivity, ensure accuracy, and simplify workflows. By reducing manual effort, minimizing errors, and accelerating claims processing times, LIKE.TG allows healthcare providers to focus on what truly matters — delivering quality patient care. Take the first step towards a more efficient and effective medical claims processing system with LIKE.TG by downloading its free 14-day trial today. Experience Hassle-Free Medical Claims Processing with LIKE.TG ReportMiner Unlock the power of automated data extraction with LIKE.TG ReportMiner. Simplify your medical claims processing, reduce errors, and save valuable time. Try LIKE.TG ReportMiner free for 14 days and see the difference it can make. Start Your Free 14-Day Trial
The Power of Automated Medical Data Processing: Optimizing Insurance Rates for Effortless Savings 
The Power of Automated Medical Data Processing: Optimizing Insurance Rates for Effortless Savings 
The insurance industry is no stranger to data. From claims history to customer demographics, insurers rely on vast amounts of information to assess risk and set rates. To efficiently collect and analyze medical data, large insurers with millions of customers are now turning to an innovative solution – automated medical data processing. By leveraging artificial intelligence and other advanced technologies, insurers can extract valuable insights from medical data quickly and efficiently, ultimately leading to more accurate rates and, more importantly, savings for customers. Unlocking Efficiency in Insurance through Automated Data Extraction Automated data extraction is quickly becoming a game-changer in the medical insurance industry, enabling insurers to effortlessly navigate the complex realm of medical data processing, transforming the way they serve their customers. But what exactly is automated data extraction, and how does it work? Simply put, it is the use of artificial intelligence (AI) and other advanced technologies to automatically extract relevant information from large volumes of medical data. This could include everything from claims data to electronic health records (EHRs). Here are some of the advantages of using AI-powered automated medical data extraction in the insurance industry: Faster claims processing AI algorithms can extract relevant information, such as diagnosis codes, procedures, and medications, with remarkable speed and accuracy. Therefore, automated data extraction accelerates the claims processing cycle by swiftly extracting and validating relevant medical data. This streamlined process expedites claims evaluation, leading to faster claim settlements and improved customer satisfaction. Improved risk assessment Medical data contains crucial insights into an individual’s health conditions, risk factors, and treatment history. Automated data extraction enables insurers to analyze large volumes of medical data, identifying patterns and trends that contribute to accurate risk assessment. By leveraging AI algorithms, insurers can make informed underwriting decisions and determine appropriate premium rates based on a policyholder’s unique health profile. This precision in risk assessment helps insurers set fair premiums and optimize their overall risk exposure. Enhanced fraud detection Fraud detection is a major concern for insurance providers. With AI-powered automated medical data extraction, insurers can quickly extract relevant information and detect irregularities in medical data, enabling them to uncover and investigate potential fraud. By using AI algorithms to monitor medical data, insurers can significantly reduce the risk of fraudulent claims, leading to cost savings for insurance providers and lower premiums for customers. Regulatory Compliance AI-powered automated data extraction ensures regulatory compliance in the insurance industry. It accurately extracts medical data, maintains privacy and security regulations, and generates transparent records for audits and compliance reviews. Insurers can confidently navigate regulatory frameworks, uphold data privacy of medical records, and meet industry guidelines. The Future of Personalization in Healthcare with AI-Powered Data Extraction As the insurance industry looks to the future, the potential implications of AI-powered medical data extraction are significant. Here’s how insurers can leverage this technology to offer customized coverage, optimize rates, and deliver effortless savings to policyholders: Tailored Coverage and Rates: By analyzing extensive healthcare data, insurers can gain insights into each policyholder’s unique healthcare needs and risk profile. This enables them to offer personalized coverage and set rates that accurately reflect the individual’s level of risk. For instance, policyholders with lower risk profiles, such as non-smokers with healthy lifestyles, may benefit from lower rates, while those with higher risk profiles, such as individuals with pre-existing conditions, may have rates adjusted accordingly. Real-time Health Monitoring: Insurers can leverage real-time health data obtained through AI-powered data extraction to dynamically adjust rates based on changes in an individual’s health status. For instance, if a policyholder consistently maintains a healthy weight, exercises regularly, and demonstrates good cholesterol levels, the insurer may lower their rates as a reward for their healthy behaviors. Proactive Health Management: Insurers can offer policyholders proactive health management solutions that help them maintain and improve their health. This can include access to wellness programs, health coaching, and personalized resources. For example, a policyholder with diabetes may receive assistance in managing their condition through educational materials, glucose monitoring devices, and personalized diet and exercise plans. By supporting proactive health management, insurers can promote healthier lifestyles and potentially reduce healthcare costs, leading to more competitive rates for policyholders. Policyholders can benefit from rates that are tailored to their needs, ensuring they pay for the coverage that aligns with their level of risk. The Affordable Care Act has set the stage for these advancements by emphasizing the importance of patient-centric healthcare and improved access to coverage. With AI-powered data extraction, insurers can embrace these principles and deliver cost-effective solutions that prioritize the well-being of policyholders. It is an exciting time for the insurance industry as AI continues to reshape the landscape and make personalized coverage and savings a reality for all. AI and Personalization in Insurance: A Real-Life Example Oscar Health, a renowned insurance company, harnesses the transformative power of AI to revolutionize the customer experience, prioritizing individual needs first. It utilizes advanced algorithms powered by AI to provide personalized coverage recommendations based on factors such as medical history and conditions. For example, if a customer has a history of asthma, the algorithm suggests plans with enhanced respiratory-related treatment coverage, ensuring comprehensive protection tailored to their specific needs. Additionally, the company has implemented an AI-powered chatbot named Dr. Chrono, which assists customers in scheduling appointments, refilling prescriptions, and addressing medical inquiries, thereby enhancing care delivery. Oscar Health optimizes premium rates through advanced AI-powered data analysis. With access to extensive data, the insurance company accurately assesses risk and sets competitive premiums, ensuring that customers pay rates that align with their risk profiles. By providing personalized coverage options and fair premiums, Oscar Health gains a significant advantage over its competitors. As more and more insurance providers embrace AI, we can expect to see even greater innovation and transformation within the industry. By using innovative technology to personalize customer experiences and streamline claims processing, insurance providers can improve customer satisfaction while achieving better business outcomes. LIKE.TG ReportMiner: Unlocking the Power of AI-Powered Data Extraction In the fast-paced world of insurance, seamless and accurate data extraction is crucial for staying competitive. LIKE.TG ReportMiner emerges as the ultimate solution, empowering insurance industries to extract critical information effortlessly. With its advanced AI-powered capabilities, ReportMiner revolutionizes the data extraction process by recommending report model templates through its innovative North Star feature. This cutting-edge functionality enables insurance professionals to automatically generate models for multiple source files, eliminating manual effort and ensuring consistent data extraction. From complex documents like policies and claims to intricate medical records, ReportMiner delivers unrivaled efficiency and precision, enabling insurers to unlock valuable insights and make data-driven decisions. Curious about the untapped potential of AI in the insurance industry? Download our free eBook and embark on a journey towards greater savings and financial success.
Streamlining Healthcare Billing and Revenue Cycles with Automated Data Extraction 
Streamlining Healthcare Billing and Revenue Cycles with Automated Data Extraction 
Optimizing billing and revenue cycles is crucial for healthcare organizations to thrive. The advent of automated data extraction technologies has opened new possibilities for streamlining these critical processes. By leveraging data extraction automation, healthcare providers can unlock significant advantages, including improved accuracy, reduced costs, enhanced compliance, and increased operational efficiency. Importance of Optimizing Billing and Revenue Cycles Efficient billing and revenue cycles are the lifeblood of any healthcare organization. Healthcare organizations that prioritize and excel in revenue cycle optimization improve their financial health and gain numerous advantages that propel them ahead of their peers. Here’s a brief exploration of how revenue cycle optimization serves as a powerful competitive advantage: Accurate Reimbursement: Timely and accurate reimbursement is essential for maintaining financial stability. Optimizing billing and revenue cycles ensures that claims are submitted correctly, reducing the likelihood of denials or delays in reimbursement. Cost Reduction: Manual billing processes are time-consuming, resource-intensive, and prone to errors. Streamlining these cycles can significantly reduce operational costs, freeing up resources that can be reallocated to improve patient care. Compliance and Audit Readiness: Healthcare billing is subject to rigorous regulations and compliance standards. Automating data extraction enables consistent adherence to these requirements, minimizing compliance risks and facilitating audit readiness. Enhanced Efficiency: By automating repetitive and labor-intensive tasks, healthcare organizations can increase operational efficiency and productivity. This allows staff to focus on higher-value activities, such as patient care and strategic initiatives. Improved Patient Experience: Smooth billing and revenue cycles have a direct impact on the overall patient experience. Simplified and accurate billing processes enhance patient satisfaction, reduce billing inquiries, and contribute to a positive healthcare journey for patients. The Transformative Impact of Data Extraction Automation Data extraction automation plays a pivotal role in optimizing billing and revenue cycles for healthcare organizations. By harnessing the power of automation technologies, healthcare providers can unlock a range of benefits that significantly enhance operational efficiency, accuracy, and financial performance. Let’s delve deeper into the role of data extraction automation and its impact on streamlining healthcare billing and revenue cycles. Streamlined Data Capture Traditional manual data entry processes are error-prone and time-consuming. With data extraction automation, relevant information from various sources, such as medical records, insurance claims, and patient demographics, can be accurately and swiftly extracted. This eliminates manual errors, improves data accuracy, and accelerates the billing process. Intelligent Document Classification Automating data extraction enables the intelligent classification of documents, such as Explanation of Benefits (EOBs), invoices, and claims. By categorizing and routing documents automatically, healthcare providers can reduce manual effort, improve workflow efficiency, and ensure timely processing. Faster Claims Processing Data extraction automation expedites claims processing by capturing key data elements, such as patient demographics, diagnosis codes, and procedure codes, from relevant documents. This enables the generation of clean and accurate claims, reducing the likelihood of rejections or denials. Accelerated claims processing leads to faster reimbursement and improved cash flow. Scalability and Adaptability Data extraction automation is highly scalable and adaptable to the evolving needs of healthcare organizations. Whether it’s processing increasing claim volumes, incorporating new billing codes, or adapting to regulatory changes, automated systems can handle the workload seamlessly. This scalability and adaptability will future-proof healthcare organizations, allowing them to grow and expand. Integration with Revenue Cycle Management Systems Automated data extraction seamlessly integrates with existing revenue cycle management systems, allowing for efficient data transfer and workflow automation. This integration facilitates end-to-end visibility and control over the billing and revenue cycles, enabling proactive monitoring, exception handling, and real-time reporting. Conclusion The healthcare industry is at a pivotal point where leveraging automation technologies can drive significant improvements in billing and revenue cycles. Automated data extraction enables healthcare organizations to streamline processes, enhance accuracy, reduce costs, and improve compliance. By embracing these transformative technologies, healthcare providers can shift their focus from administrative tasks to patient care, creating a sustainable future where financial success and optimal healthcare outcomes go hand in hand. Embracing data extraction automation is a strategic move that paves the way for streamlined billing and revenue cycles, ultimately leading to better financial health and improved patient care.
What Is a Data Pipeline? Definition, Types, Benefits and Use Cases
What Is a Data Pipeline? Definition, Types, Benefits and Use Cases
What is a data pipeline? A data pipeline is a set of processes and methods used to move data from different source systems into a centralized repository, usually a data warehouse or a data lake, for analysis and further use. It streamlines the flow of data from source systems, transforms data to align it with the schema of the target system, and loads it into a data warehouse. While data undergoes processing before it moves into the destination system, it does not always require transformation, especially if it flows into data lake. Data scientists and analysts use data pipelines to prepare data for various initiatives, such as feature engineering or feeding it into machine learning models for training and evaluation. Business users leverage a data pipeline builder—a no-code/low-code GUI based tool—to build their own pipelines without relying on IT. Build end-to-end Data Pipelines Without Writing a Single Line of Code! Get Started Today What is a big data pipeline? The concept of managing large volumes of data has been around for decades, but the term “big data” gained popularity in the mid-2000s as the volume, velocity, and variety of data being generated started to increase dramatically. With technologies like social media, mobile devices, IoT devices, and sensors becoming more common, organizations began to realize the potential value of harnessing and analyzing vast amounts of data. However, to process data at such a scale, businesses need an equally capable data pipeline—a big data pipeline. A big data pipeline refers to the process of collecting, processing, and analyzing large volumes of data from disparate sources in a systematic and efficient manner. Like a traditional data pipeline, it involves several stages, including data ingestion, storage, processing, transformation, and analysis. A big data pipeline typically utilizes distributed computing frameworks and technologies, given the need to handle data at a massive scale. How have data pipelines evolved? Data pipelines have come a long way over the past four decades. Initially, data scientists and engineers had to manually extract, transform, and load (ETL) data into databases. These processes typically ran on a scheduled basis, usually once a day, for data ingestion and processing, making them time-consuming and prone to errors. With the proliferation of internet-connected devices, social media, and online services, the demand for real-time data processing surged. Traditional batch processing pipelines were no longer sufficient to handle the volume and velocity of incoming data. Evolving with time, these pipelines became more flexible, facilitating data movement from cloud sources to cloud destinations, such as AWS and Snowflake. Today, they focus on ingesting data, particularly real-time data, and making it available for use as quickly as possible, making workflow automation and process orchestration all the more important. As such, modern data pipeline tools now also incorporate robust data governance features, such as: Data catalog Data lineage tracking Metadata management Data quality management Role-based access control and management Data pipeline architecture A data pipeline architecture refers to the structure and design of the system that enables the flow of data from its source to its destination while undergoing various processing stages. The following components make up the data pipeline architecture: Data sources: A variety of sources generate data, such as customer interactions on a website, transactions in a retail store, IoT devices, or any other data-generating sources within an organization. Data ingestion layer: This layer establishes connections with these data sources via appropriate protocols and connectors to retrieve data. Once connected, relevant data is extracted from each source. The business rules define whether entire datasets or only specific data points are extracted. The method of extraction depends on the data source format—structured data can be retrieved using queries, while unstructured data mostly requires specialized data extraction tools or techniques. Data storage layer: The ingested data is in raw form and, therefore, must be stored before it can be processed. Data processing layer: The processing layer includes processes and tools to transform raw data. Data delivery and analytics layer: The transformed data is loaded into a data warehouse or another repository and made available for reporting and data analytics. Read more about the data pipeline architecture. Types of data pipelines There are multiple types of data pipelines, each catering to different usage scenarios. Depending on the need and infrastructure, businesses can deploy data pipelines both on-premises and in the cloud, with the latter becoming more and more prevalent lately. Here are the different kinds of data pipelines: Batch processing data pipelines ETL batch processing pipelines process data in large volumes at scheduled intervals. They are ideal for handling historical data analysis, offline reporting, and batch-oriented tasks. Streaming data pipelines Also called real-time data pipelines as well as event-driven pipelines, these pipelines process data in real-time or near real-time, that is with very low latency. They are designed to ingest and move data from streaming data sources, such as sensors, logs, or social media feeds. streaming data pipelines enable immediate analysis and response to emerging trends, anomalies, or events, making them critical for applications like fraud detection, real-time analytics, and monitoring systems. Data integration pipelines Data integration is an automated process that moves data from various sources, transforms it into a usable format, and delivers it to a target location for further analysis or use. Data integration pipelines can be further categorized depending on whether the data is transformed before or after being loaded into a data warehouse. ETL Pipelines ETL pipelines are widely used for data integration and data warehousing. They involve extracting data from various sources, transforming it into a consistent format, and loading it into a target system. ETL pipelines are typically batch-oriented but can be augmented with real-time components for more dynamic data processing. ELT Pipelines Extract, load, and transform (ELT) pipelines are similar to ETL pipelines, but with a different sequence of steps. In ELT, data is first loaded into a target system and then transformed using the processing power and capabilities of the target system to transform data. Data pipeline vs. ETL pipeline Given the similarities between a data pipeline and ETL, it’s fairly common to come across the question “what is an ETL data pipeline?” Data pipelines and ETL are closely related; in fact, a data pipeline is a broader concept that includes ETL pipeline as a sub-category. However, there are some fundamental differences between the two: While a data pipeline doesn’t always involve data transformation, it’s a requisite step in an ETL data pipeline. Additionally, ETL pipelines generally move data via batch processing, while data pipelines also support data movement via streaming. Automate your data pipelines without writing any code Find out How Data pipeline Data Movement and Integration: Data pipelines are primarily focused on moving data from one system to another and integrating data from various sources. They enable the efficient and real-time transfer of data between systems or services. Flexibility: They can be more flexible and versatile compared to ETL processes. They are often used for real-time data streaming, batch processing, or both, depending on the use case. Streaming Data: Data pipelines are well-suited for handling streaming data, such as data generated continuously from IoT devices, social media, or web applications. Use Cases: Common use cases for data pipelines include log and event processing, real-time analytics, data replication, and data synchronization. ETL pipeline Structured Process: ETL processes follow a structured sequence of tasks: data extraction from source systems, data transformation to meet business requirements, and data loading into a target repository (often a data warehouse). Batch Processing: ETL processes are typically designed for batch processing, where data is collected over a period (e.g., daily or hourly) and transformed before it is loaded into the target system. Complex Transformations: ETL is the right choice in case you need to perform complex data transformations, such as aggregations, data cleansing, and data enrichment. Data Warehousing: You should opt for ETL processes when you need to consolidate data from multiple sources and transform it to support business intelligence and reporting. Historical Analysis: ETL processes are suitable for historical data analysis and reporting, where data is stored in a structured format, optimized for querying and analysis. Commonalities: Data Transformation: Both data pipelines and ETL processes involve data transformation, but the complexity and timing of these transformations differ. Data Quality: Ensuring data quality is important in both data pipelines and ETL processes. Monitoring and Logging: Both require monitoring and logging capabilities to track data movement, transformation, and errors. Read more about data pipeline vs. ETL pipeline. Building a data pipeline Building an efficient system for consolidating data requires careful planning and setup. There are typically six main stages in the process: Identifying Data Sources: The first step is to identify and understand the data sources. These could be databases, APIs, files, data lakes, external services, or IoT devices. Determine the format, structure, and location of the data. Data Integration: Extract and combine data from the identified sources using data connectors. This may involve querying databases, fetching data from APIs, reading files, or capturing streaming data. Data Transformation: After extracting data, transform and cleanse it to ensure its quality and consistency. Data transformation involves tasks such as data cleaning, filtering, aggregating, merging, and enriching. This stage ensures that the data is in the desired format and structure for analysis and consumption. Data Loading: After transforming, load the data into the target system or repository for storage, analysis, or further processing. During the loading stage, the pipelines transfer the transformed data to data warehouses, data lakes, or other storage solutions. This enables end-users or downstream applications to access and utilize the data effectively. Automation and Scheduling: Set up automation and scheduling mechanisms to execute the data pipeline at regular intervals or in response to specific events. Automation minimizes manual intervention and ensures data is always up-to-date. Monitoring and Evaluating: Implement robust data pipeline monitoring and metrics to track the health and performance of the data architecture. Set up alerts to notify you of issues or anomalies that require attention. This stage helps optimize your data pipelines to ensure maximum efficiency in moving data. Read more about building a data pipeline. Build data pipeline easily with LIKE.TG Software! Find out How Benefits of a data pipeline Automated data pipelines combine data from different sources and are essential for the smooth and reliable management of data throughout its lifecycle. Here are some benefits of data pipelines: Increased efficiency Data pipelines automate data workflows, reduce manual effort, and increase overall efficiency in data processing. For instance, they can extract data from various sources like online sales, in-store sales, and customer feedback. They can then transform that data into a unified format and load it into a data warehouse. This ensures a seamless and error-free conversion of raw data into actionable insights, helping the business understand customer behavior and preferences better. Promoting data governance Data pipelines ensure that data is handled in a way that complies with internal policies and external regulations. For example, in insurance, data pipelines manage sensitive policyholder data during claim processing. They ensure compliance with regulations like the European Union’s General Data Protection Regulation (GDPR), safeguarding data and building trust with policyholders. Greater scalability They can handle large volumes of data, allowing organizations to scale their operations as their data needs grow. By adopting a scalable architecture, businesses can accommodate increasing data demands without compromising performance. Data quality improvements Through data cleansing and transformation processes, they enhance data quality and ensure accuracy for analysis and decision-making. By maintaining high data quality standards, organizations can rely on trustworthy insights to drive their business activities. Real-time insights Real-time data enables organizations to receive up-to-date information for immediate action. Modern data pipelines are capable of delivering data for analysis as it is generated. By leveraging timely data insights, businesses can make agile and proactive decisions, gaining a competitive advantage in dynamic market conditions. For example, in the ride-sharing industry, they enable swift processing of data to match drivers with riders, optimize routes, and calculate fares. They also facilitate dynamic pricing, where fares can be adjusted in real-time based on factors like demand, traffic, and weather conditions, thereby enhancing operational efficiency. Cost-effectiveness They optimize resource utilization, minimizing costs associated with manual data handling and processing. By reducing the time and effort required for data operations, organizations can allocate resources efficiently and achieve cost-effectiveness. Data pipeline use cases Data pipelines serve a multitude of purposes across industries, empowering organizations with timely insights and data-driven decision-making. They are utilized in numerous industries to enhance the efficiency of data flow within organizations. For instance, in the finance sector, they help integrate stock prices and transaction records, enabling financial institutions to enhance risk management, detect fraud, and ensure regulatory compliance. In the healthcare industry, pipelines integrate electronic health records and lab results, contributing to improved patient monitoring, population health management, and clinical research. In the retail and e-commerce sector,they integrate customer data from e-commerce platforms and point-of-sale systems, allowing for effective inventory management, customer segmentation, and personalized marketing strategies. Some more data pipeline use cases: Real-time analytics Data pipelines enable organizations to collect, process, and analyze data in real time. By harnessing the power of real-time analytics, businesses can make timely decisions, react swiftly to market changes, and gain a competitive edge. Data integration Data pipelines consolidate data using data connectors from various sources, including databases, APIs, and third-party platforms, into a unified format for analysis and reporting. This integration allows organizations to harness the full potential of their data assets and obtain a holistic view of their operations. Data migration They facilitate smooth and efficient data migration from legacy systems to modern infrastructure. By ensuring a seamless transition without disruption, organizations can leverage advanced technologies and drive innovation. Machine learning and AI They provide a seamless flow of data for training machine learning models. This enables organizations to develop predictive analytics, automate processes, and unlock the power of artificial intelligence to drive their business forward. Business intelligence Data pipelines support the extraction and transformation of data to generate meaningful insights. By harnessing the power of business intelligence, organizations can make data-driven decisions, identify trends, and devise effective strategies. Use LIKE.TG's no-code data pipeline to solve your data management problems! Find out How Working with data pipeline tools Building data pipelines manually is time-consuming and prone to errors. For example, organizations that use Python to build data pipelines realize that managing pipelines quickly becomes a challenging endeavor as data sources and complexity grow. Instead of investing more in building a bigger team of developers, a more cost-effective and sustainable strategy would be to incorporate a modern data pipeline solution into the data stack. Data pipeline tools make it easier to build data pipelines as they offer a visual interface. However, choosing the right tool is a critical decision, given their widespread availability and the fact that no two solutions are built equal. The right tool will be the one that provides connectivity to a wide range of databases, APIs, cloud destinations, etc. It also provides support for near real-time data integration via ETL, ELT, and change data capture. It is scalable and handles growing data volumes and concurrent users with ease. For example, LIKE.TG is a no-code data management solution that enables you to build enterprise-grade data pipelines within minutes. It allows you to create and schedule ETL and ELT pipelines within a simple, drag and drop interface. LIKE.TG supports seamless connectivity to industry-leading databases, data warehouses, and data lakes with its vast library of native connectors. Additionally, you can automate all dataflows and workflows and monitor data movement in real-time. Business users can take advantage of advanced built-in data transformations, data quality features, version control, and data governance and security features and build data pipelines on their own. Emerging trends surrounding data pipelines Beyond the common use cases, data pipelines have applications in various advanced scenarios and emerging trends: Real-time Personalization: Data pipelines enable real-time personalization by analyzing user behavior data and delivering personalized content or recommendations in real time. Internet of Things (IoT) Data Processing: With the rise of IoT devices, data pipelines are used to ingest, process, and analyze massive amounts of sensor data generated by IoT devices, enabling real-time insights and automation. Data Mesh: The data mesh concept decentralizes them and establishes domain-oriented, self-serve data infrastructure. It promotes data ownership, autonomy, and easy access to data, leading to improved scalability and agility in data processing. Federated Learning: They support federated learning approaches, where machine learning models are trained collaboratively on distributed data sources while maintaining data privacy and security. Explainable AI: They can incorporate techniques for generating explainable AI models, providing transparency and interpretability in complex machine learning models. Conclusion Data pipelines play a vital role in the modern data landscape, facilitating efficient data processing, integration, and analysis. By leveraging the power of an automated data pipeline builder, you can enhance decision-making, improve operational efficiency, and gain valuable insights from their data. Data integration tools like LIKE.TG simplify the creation of end-to-end dataflows. Ready to build and deploy high-performing data pipelines in minutes? 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How to Extract Data from PDF to Excel
How to Extract Data from PDF to Excel
Import PDF files and extract data to Excel in bulk Whether you’re at work handling documents or gathering material for your research proposal, the information you need is mostly stored in a variety of different formats, from webpages and documents to images on Google. While most documents today are formatted as PDF files, primarily because they are easy to use and share, you might find that you need the data they contain and use it in another place, for example, in an Excel file, to visualize the data. But to do this, you must first extract data from PDF to Excel. So, you decide it’s best to copy and paste the data as it is. This might not be much of an issue if you only need to do it twice or thrice. However, it certainly becomes time-consuming and laborious when you have to gather data from hundreds of PDF files. Naturally, you begin your search for intelligent ways to convert or extract data from PDF to Excel in bulk. And this is precisely what this article is for—to help you find the best solution. Ways to Extract Data from PDF to Excel There are multiple ways you can extract data from PDF to Excel. Some of the most common ones are: Manual Copy and Paste This method is the most straightforward way of importing data from a PDF to Excel. All you have to do is open the source PDF document, select the data you need to extract, and then copy and paste it into your target Excel spreadsheet. However, some documents, and the data therein, are not structured nicely, making it nearly impossible to copy and paste while preserving the formatting and layout and getting the desired outcome. To overcome this issue, you can open the PDF document using Word and then copy and paste the data. However, keep in mind that: You still won’t be able to correctly import certain elements into Excel, such as complex tables. While this method works for small data sets, it quickly becomes tedious and error-prone as the number of documents and the size of the data increase. Import PDF data into Excel When traditional copy-and-paste methods fall short, importing PDF files directly into Excel offers a convenient alternative. Open Excel: Open an Excel sheet on your computer. Access Data Tab: Navigate to the Data tab located at the top menu bar. Import from PDF: From the Get Data drop-down menu, select “From File” and then choose “From PDF.” Select PDF File: Locate and select the PDF file you wish to import into Excel, then click “Import.” Navigate Table Preview: A Navigator pane will appear, displaying tables and pages from your PDF and a preview. Choose Data to Import: Select the desired table from the Navigator pane and click “Load.” Import Complete: The selected table will now be imported into your Excel sheet, ready for further analysis or manipulation. PDF to Excel Converters You can use generic PDF converters when you regularly need to extract data from many PDF files with similar layouts. These tools automatically extract data and offer a range of features to streamline the process, such as selective extraction and data structure recognition. See How You Can Automate PDF Data Extraction Download Whitepaper Along with offline PDF converters, you can also try out online solutions. However, the process is essentially the same for both converters: import the PDF document and click on the convert button. Some of the most commonly used PDF converters are: Adobe Acrobat DC Nitro Pro PDFCreator PDF Reader Pro SODA PDF While the process is considerably faster than copying and pasting data into Excel, you’ll still have to repeat it as many times as the number of documents you need to process. Power Query A third option is to use Power Query, offered as an Excel feature by Microsoft. Power Query is a tool that enables you to import PDF documents into Excel easily. It can automatically identify and extract tables and other data structures you might otherwise have difficulty with using PDF converters. However, it still has its own set of limitations: It can be challenging to learn and work with. Using large PDF files results in performance degradation and excessive timeouts. You must learn to use complex functions, as Power Query cannot correctly identify multi-line rows. It can only process PDF files in bulk if they have a consistent layout or structure. Therefore, if you regularly deal with a variety of PDF documents with complex structures and layouts, this might not be the best option. Python and PDF Extraction Libraries This method is only relevant if you know how to write code using Python. Nevertheless, here’s what you’ll need to do to extract data from PDF to Excel using Python and its extraction libraries: Download and install Python Choose your preferred PDF extraction library. Popular ones include: Tubula-py, Pypdf2, and Camelot Set up Python Environment Install the extraction libraries Write Python script for PDF extraction Execute the script Review and verify the extracted data These steps are just the primary ones in the process, and each step can contain multiple sub-steps. While this method offers incredible customization and robust capabilities, it requires advanced Python knowledge and a considerable amount of time to code and set up the entire extraction workflow. Moreover, since this is a manual process, there may be updates or changes that require you to manually update your Python script or adapt to new versions of the libraries. This can require ongoing maintenance to ensure compatibility and optimal performance. Automated Data Extraction Tools Here's How Much You Can Save With Automated Data Extraction Calculate Your Savings An automated data extraction tool, such as LIKE.TG ReportMiner, provides a convenient and efficient method of extracting data from PDF files and loading it into an Excel spreadsheet. These tools are purpose-built to handle files in bulk with great accuracy and speed. Import PDF files and extract data to Excel in bulk Data extraction tools leverage some advanced technologies, such as artificial intelligence (AI), optical image recognition (OCR), and machine learning (ML) algorithms, to ensure speed and accuracy when identifying and extracting data. While every tool is designed differently, they all share the same goal—to simplify and accelerate data extraction from documents in bulk. Here are the simple steps that you need to take to extract data from PDF to Excel: Install and set up the tool. Import your PDF files into the software. Identify and select the data you want to extract. Define data extraction rules. Apply transformations (if needed). Export data to Excel. As you can see, the process is straightforward and seamless. Which PDF to Excel Extraction Method Should You Use? Now that you have a clear picture of all the different ways to extract data from PDF to Excel, the question that remains now is: Which method should you choose? While the answer to this question depends on your specific business requirements, you should consider some key factors to make an informed decision. File and Data Volume Consider the number of documents and the data you need to extract from them. If you frequently deal with very high document and data volumes, an automated data extraction tool should be your first choice. On the other hand, if you only occasionally need to extract data from PDF to Excel, then it’s only a matter of preference as both PDF converters and copy-and-paste methods will deliver. Document Structure Extracting data from PDF to Excel using PDF converters can be a viable option if the documents you deal with have a simple and consistent structure. However, if the PDF files contain complex elements or unstructured data, you should consider an automated data extraction tool. Speed and Accuracy If speed and accuracy are of the essence, then there’s nothing better than automated document data extraction. The technologies that power these tools, such as OCR and AI, make them unrivaled. Automation Automation is necessary when dealing with a high number of documents, and scalability, consistency, and accuracy come with it. Automated data extraction tools feature batch processing, enabling you to extract data from multiple PDF files simultaneously. These tools significantly reduce the time and effort required compared to manual methods like copy-pasting or converting files individually. Available Resources and Expertise If your team has the required resources and expertise, you can consider building an in-house solution using Python and PDF extraction libraries. However, using Python and its extraction libraries to extract PDF data can be complex for many people, especially business users. So, if you want a simpler solution while still benefiting from all its features and capabilities, you should consider an automated data extraction tool. Not only will you be able to leverage customization and flexibility, but you will also be able to integrate it with downstream systems. This integration will enable you to analyze and visualize data without external assistance. LIKE.TG ReportMiner—the PDF to Excel Data Extraction Champion Extracting data from PDF to Excel doesn’t have to be a time-consuming task. Since most situations call for a simplified solution capable of extracting data from hundreds of PDF documents simultaneously, as we’ve seen above, you should look for an automated data extraction platform, such as LIKE.TG ReportMiner. PDF to Excel Dataflow in ReportMiner LIKE.TG ReportMiner is a high-end document data extraction platform powered by automation. It leverages OCR and AI techniques to automatically identify and comprehend the structure of a wide range of different documents, including PDF files. Extract Data From PDF To Excel In Bulk With LIKE.TG ReportMiner View Demo With LIKE.TG ReportMiner, you can: Extract data from structured, semi-structured, and unstructured documents using an intuitive, drag-and-drop UI Auto-generate layouts instantly using AI Capture Handle multiple invoices and purchase orders using AI-powered multi-document processing Automate the entire data extraction workflow from end to end Ensure that only healthy data reaches your destination system with embedded data quality management Easily transform data according to your business needs using built-in transformations If you need to extract data from PDF to Excel, or any other format, in bulk without compromising on speed and accuracy, try LIKE.TG ReportMiner today!
Transforming Healthcare Efficiency: Save 80% of Time with Automated Medical Record Data Extraction 
Transforming Healthcare Efficiency: Save 80% of Time with Automated Medical Record Data Extraction 
Medical record management is a critical aspect of any hospital’s operations. Accurate and timely access to patient data is crucial for providing quality care and making informed decisions. While many hospitals still rely on manual data entry and retrieval processes, there is a growing opportunity for ways hospitals can streamline their operations and achieve substantial time savings. Luckily, there’s a game-changing solution: automated medical record data extraction. Studies show that by automating just 36% of document processes, healthcare organizations can save up to hours of work time and $11 billion in claims. So, let’s delve further into how healthcare organizations are significantly improving their medical record management processes using an automated data extraction tool. The Digital Revolution in Healthcare: Understanding Automated Medical Record Data Extraction Automated medical record data extraction is a software-driven process that automatically extracts relevant data from medical records and enters it into electronic health records (EHRs) or other systems. It uses sophisticated AI algorithms to read medical records and automatically identify relevant data. This healthcare data management software is designed to recognize critical data points such as patient demographics, medical history, and test results. Automated medical record data extraction can be used for various types of medical records including doctor’s notes, lab reports, and discharge summaries. It can also identify and extract data from unstructured data sources, such as handwritten notes, which can be particularly challenging for manual data entry. Streamlining Medical Record Management: XYZ Hospital’s Real Life Journey from Manual to Automated Processes XYZ Hospital, a leading healthcare institution, prioritizes efficiency in delivering exceptional patient care. Like many healthcare institutions, XYZ Hospital recognized the opportunities for improvement posed by traditional paper-based medical record systems. The reliance on manual data entry processes introduced the risk of human error, leading to inaccuracies in medical records. XYZ Hospital understood that such inaccuracies could have dangerous consequences, including incorrect diagnoses and inappropriate treatments, which could harm patients and result in legal implications. To make matters more challenging, inefficient tagging and document archiving practices contributed to approximately 30% of patient charts being misplaced. Additionally, care transitions posed a significant risk, as over 80% of serious medical errors occurred during these transitions. Retrieving and analyzing patient records was another area of concern for XYZ Hospital. The manual processes in place caused delays in locating and retrieving records, resulting in increased response times, and reduced overall efficiency. Such delays were particularly unacceptable in critical situations, such as when patients were brought into the emergency room. Accessing necessary records could range from several minutes to hours, potentially jeopardizing patient outcomes. To overcome these challenges and optimize its operations, XYZ Hospital turned to automation, resulting in substantial time savings and improved overall productivity. Unlocking Efficiency: Why Automated Data Extraction Tools are Vital for Medical Record Management Automated data extraction tools offer numerous advantages for medical record management within healthcare organizations. By choosing to implement these tools, healthcare providers can experience the following benefits: Time Efficiency: Automating the data extraction process significantly reduces the time required for manual data entry and retrieval tasks. Healthcare professionals can save valuable time and allocate it to more critical activities such as direct patient care. With automated data extraction, accessing and analyzing patient records becomes faster and more streamlined. Enhanced Accuracy: Automated data extraction tools utilize advanced algorithms to extract and transcribe information from medical records accurately. This improves data accuracy, minimizing the risk of incorrect diagnoses, treatments, or medication administration. Accurate data ensures better patient safety and outcomes. Improved Workflow and Productivity: By reducing the reliance on manual processes, healthcare professionals can focus on providing quality care instead of spending excessive time on administrative tasks. Streamlined workflows lead to more efficient patient care delivery. Cost Reduction: By reducing manual labor and minimizing errors, healthcare providers can allocate their resources more effectively. This can lead to reduced staffing needs, decreased transcription costs, and better financial utilization, ultimately contributing to cost reduction and increased operational efficiency. Enhanced Data Accessibility and Collaboration: Automated data extraction tools digitize and organize medical records, making them easily accessible to authorized healthcare personnel. Digitized records stored in electronic health record (EHR) systems allow for quick retrieval and seamless sharing of information among healthcare teams. Compliance and Security: Automated data extraction tools help healthcare organizations maintain compliance with privacy regulations, such as HIPAA. Compliance with regulations safeguards patient privacy and mitigates legal and financial risks for healthcare providers. Revolutionizing Medical Record Management: The Journey of Implementing a Data Extraction Tool at XYZ Hospital Need Assessment The first phase of adopting the data extraction tool at XYZ Hospital was assessing the hospital’s specific needs and requirements. The hospital’s management team conducted a thorough analysis of the current medical record management process to identify areas that could benefit from the implementation of the data extraction tool. Customization of Tool The data extraction tool was then customized to meet XYZ Hospital’s specific needs. The management team worked closely with the tool’s developers to configure extraction algorithms, specify data fields, and customize formatting for easy interpretation. Staff Training A brief training was provided to all staff members involved in medical record management—the training covered tool usage, data interpretation, and leveraging extracted data for clinical decision-making. Ongoing support was provided to ensure effective and efficient tool utilization. Unleashing the Power of Automation: Introducing XYZ Hospital’s Game-Changer Tool XYZ Hospital successfully implemented LIKE.TG’s advanced automated data extraction tool – ReportMiner, revolutionizing their medical record management process. With its remarkable capabilities, LIKE.TG ReportMiner effortlessly processes vast volumes of documents, eliminating the need for laborious manual data entry. This automation ensures unparalleled accuracy and efficiency in data extraction, liberating staff members from time-consuming transcription tasks. The user-friendly interface of the automated healthcare data management software requires minimal training, enabling all staff members involved in medical record management to adapt quickly. Moreover, the seamless integration of LIKE.TG ReportMiner with XYZ hospital’s existing systems, particularly the EHR system, facilitates real-time data availability for healthcare providers, leading to enhanced decision-making and improved patient care quality. The tool’s integration also allows for effortless data transfer between various departments meaning data can be accessed by different departments, such as the billing department and the research department, without manual data transfer eliminating the need for manual interventions and promoting seamless collaboration across the organization. XYZ Hospital’s commitment to excellence in data accuracy and compliance has been further amplified with the adoption of LIKE.TG ReportMiner. The tool’s automated data validation feature ensures accuracy and consistency, enabling XYZ Hospital to avoid costly mistakes and maintain compliance with regulatory standards, including the stringent HIPAA guidelines. A productivity analysis conducted by XYZ Hospital revealed that after the implementation of LIKE.TG Reportminer, XYZ’s healthcare professionals now spend 80% less time on administrative tasks related to data extraction, allowing them to focus more on patient care indicating the positive impact LIKE.TG has had on the hospital’s operations. Conclusion: The Value of Automation in Medical Record Management The remarkable success achieved through the implementation of LIKE.TG ReportMiner has propelled XYZ Hospital into new realms of data management innovation. Building upon this solid foundation, the hospital is actively exploring additional applications of AI technology, such as predictive analytics, and intelligent decision support systems. The story of XYZ Hospital serves as an inspiration for other healthcare providers to embrace innovative technologies and unlock the full potential of data-driven decision-making, ultimately leading to improved patient outcomes and a brighter future for the healthcare industry. Are you also ready to unlock the potential of automated medical data extraction just like XYZ Hospital? Discover the power of efficiency, accuracy, and compliance with LIKE.TG ReportMiner. Sign up for a 14-day free trial or schedule a demo with our experts today to revolutionize your healthcare institution’s data extraction processes.
Harnessing AI: Choosing the Right Automated Legal Document Data Extraction Tool for Your Law Firm 
Harnessing AI: Choosing the Right Automated Legal Document Data Extraction Tool for Your Law Firm 
Navigating the world of law, every attorney knows the importance of maintaining an eagle-eye for detail. Accuracy isn’t a luxury in the legal field—it’s an absolute necessity. Every document holds valuable information, and a morsel of truth that can turn a case on its head. Once upon a time, this information was hidden in a sea of legalese and dense paperwork. However today, we’re at a tipping point, with technology stepping up to meet these very demands of the modern legal landscape. In fact, a recent survey revealed that an overwhelming majority of law firms with large corporate clients (71%) have already embraced legal technology. This is an exciting time for law firms. They stand to benefit immensely from the power of artificial intelligence (AI). These aren’t just buzzwords; these technologies are now crucial tools that promise to transform how we manage and interpret data, ensuring precision while saving precious time. Why Data Accuracy is a Big Deal in Legal Document Management Documents are immensely important in any legal practice because they’re crammed with facts, case histories, client data, and vital insights. However, the sheer volume of information in these documents can be overwhelming, making data extraction feel like looking for a needle in a haystack. The twist is that the needle must be found. A slight slip-up or oversight can lead to serious ramifications, influencing the course of legal proceedings. Imagine you’re reviewing a contract. A missed clause or an overlooked obligation can lead to breaches, reputational harm, or unwelcome financial penalties. In other words, the process of data extraction isn’t just about speed; it’s about reliability and precision, too. Furthermore, accurate data extraction helps legal practices in other ways. It feeds into predictive analytics and trend forecasting, enabling better strategic decision-making. If the data is inaccurate, strategies may falter, and forecasting can be skewed, leading to setbacks in the firm’s growth. So, it’s clear that accuracy in legal document management is paramount. While manual methods and traditional data management systems have served us well, they’re prone to human error and may lack the finesse required for comprehensive and accurate extraction. But don’t worry—this is not a dead-end situation. There are AI-driven data extraction tools available now that are tailored to match the complexity of the task at hand, promising better accuracy and greater efficiency. These tools aren’t just a minor upgrade; they are set to redefine how the legal industry deals with data. Unleashing the Potential of AI-Driven Automated Data Extraction in Legal Practice The future of legal data management is already here, personified by AI-driven automated data extraction tools. These sophisticated systems employ AI, machine learning (ML), and natural language processing (NLP) to decipher, dissect, and classify vast quantities of data, delving deeper into layers of legal text to extract not just raw data but also the invaluable context it encompasses. These tools can swiftly navigate through hundreds of pages of complex legal documents, and perform tasks at a fraction of the time a human would need. Even more impressive, they do this with incredible precision that minimizes oversight or misinterpretation risks. For instance, the due diligence process in mergers and acquisitions (MA). Traditionally, it’s a labor-intensive task, requiring the review of hundreds, if not thousands, of documents. However, an AI-driven tool simplifies this intricate task, swiftly navigating the data labyrinth to pinpoint crucial details such as contractual obligations, liabilities, or intellectual property rights. Similarly, during litigation’s discovery stages, AI can proficiently review and categorize electronic documents, identifying key themes, inconsistencies, or potential evidence. This heightened efficiency improves the entire process, making sure no important detail is overlooked. Beyond these specific tasks, AI-driven tools offer a significant advantage in terms of scalability. As law firms expand, the volume of data they handle escalates. Automated tools are adept at scaling alongside the firm, managing increased data loads without compromising speed or accuracy. Discerning Considerations in Selecting an Automated Legal Document Data Extraction Tool Choosing the right AI-driven data extraction tool for your law firm is a decision that warrants thoughtful consideration. As your firm journeys into the realm of AI, this tool should align with your firm’s distinct needs and operational structure. Let’s delve into six crucial factors that should guide your choice. Ease of Use: In the fast-paced world of law, user-friendly interfaces are essential. Legal professionals need to be able to access and understand the functionalities of a tool without spending excessive time decoding it. Imagine you’re preparing for a challenging antitrust case. A user-friendly extraction tool would allow you to swiftly navigate through vast amounts of market data and competitor information. The result? You’d spend less time wrestling with technology and more time honing your strategy. Integration Capabilities: A powerful extraction tool won’t be of much use if it doesn’t communicate well with other systems in your firm. Your chosen tool should seamlessly integrate with your existing case management software, databases, and other tech infrastructure. For instance, in a personal injury lawsuit, an integrated tool can quickly pull relevant medical records and accident reports from different databases, streamlining the preparation process. Data Security: The legal field carries a mandatory responsibility for confidentiality. Your chosen tool must adhere to stringent data security standards and regulations to ensure client information remains secure. If you’re handling a sensitive divorce case, robust security features would ensure that private financial and personal information remains confidential throughout the extraction process. Accuracy: Precision is crucial in legal document data extraction. The tool should be designed to minimize errors, ensuring high levels of data accuracy. In complex cases, such as intellectual property disputes, precise extraction of data from patent applications, court rulings, and similar documents could make a substantial difference in the case outcome. Scalability: As your firm grows and takes on more complex cases, your data extraction tool should be able to keep up. Scalability ensures that the tool can handle increasing volumes of data and more complex tasks. A rapidly expanding corporate law firm, for instance, needs a tool that can efficiently process an escalating number of contracts, corporate filings, and regulatory documents. Customer Support and Training: Choosing a tool with robust customer support and training resources is crucial. From initial training to troubleshooting, your law firm should have access to responsive and knowledgeable support. If a legal researcher is working late into the night and encounters an issue with the tool, immediate customer support can ensure minimal disruption. In essence, selecting the right AI-driven data extraction tool isn’t just about its immediate capabilities. It’s also about ensuring it fits seamlessly into your firm’s operations, contributes positively to your growth trajectory, and is supported by a customer-focused company that’s ready to assist when needed. Conclusion In a profession where every billable hour counts, the time saved is a significant advantage. Freed from hours of manual data management, legal professionals can channel their efforts towards more strategic tasks, client engagement, and courtroom preparation, bolstering both their productivity and the firm’s overall performance. As your firm steps into this transformative era, it’s important to align with a tool that can adeptly navigate legal data’s complexities. Innovative technology, when coupled with a profound understanding of legal nuances, can prove to be a powerful ally. Amidst this, solutions like LIKE.TG ReportMiner stand out, combining ease-of-use with robust data handling, a critical combination for enhancing precision and efficiency in your firm.
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					10 Benefits That Explain the Importance of CRM in Banking
10 Benefits That Explain the Importance of CRM in Banking
The banking industry is undergoing a digital transformation, and customer relationship management (CRM) systems are at the forefront of this change. By providing a centralised platform for customer data, interactions, and analytics, CRMs empower banks to deliver personalised and efficient services, fostering customer loyalty and driving business growth. We’ll look closer at the significance of CRM in banking, exploring its numerous benefits, addressing challenges in adoption, and highlighting future trends and innovations. Additionally, we present a compelling case study showcasing a successful CRM implementation in the banking sector. 10 Questions to Ask When Choosing a CRM in Banking When selecting a top CRM platform for your banking institution, it is necessary to carefully evaluate potential solutions to ensure they align with your specific requirements and objectives. Here are 10 key questions to ask during the selection process: 1. Does the CRM integrate with your existing, financial and banking organisation and systems? A seamless integration between your CRM and existing banking systems is essential to avoid data silos and ensure a holistic view of customer interactions. Look for a CRM that can easily integrate with your core banking system, payment platforms, and other relevant applications. 2. Can the CRM provide a 360-degree view of your customers? A CRM should offer a unified platform that consolidates customer data from various touchpoints, including online banking, mobile banking, branches, and contact centres. This enables bank representatives to access a complete customer profile, including account information, transaction history, and past interactions, resulting in more personalised and efficient customer service. 3. Does the CRM offer robust reporting and analytics capabilities? Leverage the power of data by selecting a CRM that provides robust reporting and analytics capabilities. This will allow you to analyse customer behaviour, identify trends, and gain actionable insights into customer needs and preferences. Look for a CRM that offers customisable reports, dashboards, and data visualisation tools to empower your bank with data-driven decision-making. 4. Is the CRM user-friendly and easy to implement? A user-friendly interface is essential for ensuring that your bank’s employees can effectively utilise the CRM. Consider the technical expertise of your team and opt for a CRM with an intuitive design, clear navigation, and minimal training requirements. Additionally, evaluate the implementation process to ensure it can be completed within your desired timeframe and budget. What is a CRM in the Banking Industry? Customer relationship management (CRM) is a crucial technology for banks to optimise customer service, improve operational efficiency, and drive business growth. A CRM system acts as a centralised platform that empowers banks to manage customer interactions, track customer information, and analyse customer data. By leveraging CRM capabilities, banks can also gain deeper insights and a larger understanding of their customers’ needs, preferences, and behaviours, enabling them to deliver personalised and exceptional banking experiences. CRM in banking fosters stronger customer relationships by facilitating personalised interactions. With a CRM system, banks can capture and store customer data, including personal information, transaction history, and communication preferences. This data enables bank representatives to have informed conversations with customers, addressing their specific needs and providing tailored financial solutions. Personalised interactions enhance customer satisfaction, loyalty, and overall banking experience. CRM enhances operational efficiency and productivity within banks. By automating routine tasks such as data entry, customer service ticketing, and report generation, banking CRM software streamlines workflows and reduces manual labour. This automation allows bank employees to focus on higher-value activities, such as customer engagement and financial advisory services. Furthermore, CRM provides real-time access to customer information, enabling employees to quickly retrieve and update customer data, thereby enhancing operational efficiency. Additionally, CRM empowers banks to analyse customer data and derive valuable insights. With robust reporting and analytics capabilities, banks can identify customer segments, analyse customer behaviour, and measure campaign effectiveness. This data-driven approach enables banks to make informed decisions, optimise marketing strategies, and develop targeted products and services that cater to specific customer needs. CRM also plays a vital role in risk management and compliance within the banking industry. By integrating customer data with regulatory requirements, banks can effectively monitor transactions, detect suspicious activities, and mitigate fraud risks. This ensures compliance with industry regulations and safeguards customer information. In summary, CRM is a transformative technology that revolutionises banking operations. By fostering personalised customer experiences and interactions, enhancing operational efficiency, enabling data-driven decision-making, and ensuring risk management, CRM empowers banks to deliver superior customer service, drive business growth, and maintain a competitive edge. The 10 Business Benefits of Using a Banking CRM 1. Streamlined Customer Interactions: CRMs enable banks to centralise customer data, providing a holistic view of each customer’s interactions with the bank. This allows for streamlined and personalised customer service, improving customer satisfaction and reducing the time and effort required to resolve customer queries. 2. Enhanced Data Management and Analytics: CRMs provide powerful data management capabilities, enabling banks to collect, store, and analyse customer data from various sources. This data can be leveraged to gain valuable insights into customer behaviour, preferences, and buying patterns. Banks can then use these insights to optimise their products, services, and marketing strategies. 3. Increased Sales and Cross-Selling Opportunities: CRMs help banks identify cross-selling and upselling opportunities by analysing customer data and identifying customer needs and preferences. By leveraging this information, banks can proactively recommend relevant products and services, increasing sales and revenue. 4. Improved Customer Retention and Loyalty: CRMs help banks build stronger customer relationships by enabling personalised interactions and providing excellent customer service. By understanding customer needs and preferences, banks can proactively address issues and provide tailored solutions, fostering customer loyalty and reducing churn. 5. Enhanced Regulatory Compliance and Risk Management: CRMs assist banks in complying with industry regulations and managing risks effectively. By centralising customer data and tracking customer interactions, banks can easily generate reports and demonstrate compliance with regulatory requirements. CRMs and other banking software programs also help in identifying and managing potential risks associated with customer transactions. 6. Improved Operational Efficiency: CRMs streamline various banking processes, including customer onboarding, loan processing, and account management. By automating repetitive tasks and providing real-time access to customer information, CRMs help banks improve operational efficiency and reduce costs. 7. Increased Employee Productivity: CRMs provide banking employees with easy access to customer data and real-time updates, enabling them to handle customer inquiries more efficiently. This reduces the time spent on administrative tasks and allows employees to focus on providing exceptional customer service. 8. Improved Decision-Making: CRMs provide banks with data-driven insights into customer behaviour and market trends. This information supports informed decision-making, enabling banks to develop and implement effective strategies for customer acquisition, retention, and growth. 9. Enhanced Customer Experience: CRMs help banks deliver a superior customer experience by providing personalised interactions, proactive problem resolution, and quick response to customer inquiries. This results in increased customer satisfaction and positive brand perception.10. Increased Profitability: By leveraging the benefits of CRM systems, banks can optimise their operations, increase sales, and reduce costs, ultimately leading to increased profitability and long-term success for financial service customers. Case studies highlighting successful CRM implementations in banking Several financial institutions have successfully implemented CRM systems to enhance their operations and customer service. Here are a few notable case studies: DBS Bank: DBS Bank, a leading financial institution in Southeast Asia, implemented a CRM system to improve customer service and cross-selling opportunities. The system provided a 360-degree view of customers, enabling the bank to tailor products and services to individual needs. As a result, DBS Bank increased customer retention by 15% and cross-selling opportunities by 20%. HDFC Bank: India’s largest private sector bank, HDFC Bank, implemented a CRM system to improve customer service and operational efficiency. The system integrated various customer touch points, such as branches, ATMs, and online banking, providing a seamless experience for customers. HDFC Bank achieved a 20% reduction in operating costs and a 15% increase in customer satisfaction. JPMorgan Chase: JPMorgan Chase, one of the largest banks in the United States, implemented a CRM system to improve customer interactions and data management. The system provided a centralised platform to track customer interactions and data, allowing the bank to gain insights into customer behaviour and preferences. As a result, JPMorgan Chase increased customer interactions by 15% and improved data accuracy by 20%. Bank of America: Bank of America, the second-largest bank in the United States, implemented a CRM system to improve sales and cross-selling opportunities. The system provided sales teams with real-time customer data, across sales and marketing efforts enabling them to tailor their pitches and identify potential cross-selling opportunities. Bank of America achieved a 10% increase in sales and a 15% increase in cross-selling opportunities.These case studies demonstrate the tangible benefits of CRM in the banking industry. By implementing CRM systems, banks can improve customer retention, customer service, cross-selling opportunities, operating costs, and marketing campaigns. Overcoming challenges to CRM adoption in banking While CRM systems offer numerous benefits to banks, their adoption can be hindered by certain challenges. One of the primary obstacles is resistance from employees who may be reluctant to embrace new technology or fear job displacement. Overcoming this resistance requires effective change management strategies, such as involving employees in the selection and implementation process, providing all-encompassing training, and addressing their concerns. Another challenge is the lack of proper training and support for employees using the CRM system. Insufficient training can lead to low user adoption and suboptimal utilisation of the system’s features. To address this, banks should invest in robust training programs that equip employees with the knowledge and skills necessary to effectively use the CRM system. Training should cover not only the technical aspects of the system but also its benefits and how it aligns with the bank’s overall goals. Integration challenges can also hinder the successful adoption of CRM software in banking. Banks often have complex IT systems and integrating a new CRM system can be a complex and time-consuming process. To overcome these challenges, banks should carefully plan the integration process, ensuring compatibility between the CRM system and existing systems. This may involve working with the CRM vendor to ensure a smooth integration process and providing adequate technical support to address any issues that arise. Data security is a critical concern for banks, and the adoption of a CRM system must address potential security risks. Banks must ensure that the CRM system meets industry standards and regulations for data protection. This includes implementing robust security measures, such as encryption, access controls, and regular security audits, to safeguard sensitive customer information. Finally, the cost of implementing and maintaining a CRM system can be a challenge for banks. CRM systems require significant upfront investment in software, hardware, and training. Banks should carefully evaluate the costs and benefits of CRM adoption, ensuring that the potential returns justify the investment. Additionally, banks should consider the ongoing costs associated with maintaining and updating the CRM system, as well as the cost of providing ongoing training and support to users. Future trends and innovations in banking CRM Navigating Evolving Banking Trends and Innovations in CRM The banking industry stands at the precipice of transformative changes, driven by a surge of innovative technologies and evolving customer expectations. Open banking, artificial intelligence (AI), blockchain technology, the Internet of Things (IoT), and voice-activated interfaces are shaping the future of banking CRM. Open banking is revolutionising the financial sphere by enabling banks to securely share customer data with third-party providers, with the customer’s explicit consent. This fosters a broader financial ecosystem, offering customers access to a varied range of products and services, while fostering healthy competition and innovation within the banking sector. AI has become an indispensable tool for banking institutions, empowering them to deliver exceptional customer experiences. AI-driven chatbots and virtual assistants provide round-the-clock support, assisting customers with queries, processing transactions, and ensuring swift problem resolution. Additionally, AI plays a pivotal role in fraud detection and risk management, safeguarding customers’ financial well-being. Blockchain technology, with its decentralised and immutable nature, offers a secure platform for financial transactions. By maintaining an incorruptible ledger of records, blockchain ensures the integrity and transparency of financial data, building trust among customers and enhancing the overall banking experience. The Internet of Things (IoT) is transforming banking by connecting physical devices to the internet, enabling real-time data collection and exchange. IoT devices monitor customer behaviour, track equipment status, and manage inventory, empowering banks to optimise operations, reduce costs, and deliver personalised services. Voice-activated interfaces and chatbots are revolutionising customer interactions, providing convenient and intuitive access to banking services. Customers can utilise voice commands or text-based chat to manage accounts, make payments, and seek assistance, enhancing their overall banking experience. These transformative trends necessitate banks’ ability to adapt and innovate continuously. By embracing these technologies and aligning them with customer needs, banks can unlock new opportunities for growth, strengthen customer relationships, and remain at the forefront of the industry. How LIKE.TG Can Help LIKE.TG is a leading provider of CRM solutions that can help banks achieve the benefits of CRM. With LIKE.TG, banks can gain a complete view of their customers, track interactions, deliver personalised experiences, and more. LIKE.TG offers a comprehensive suite of CRM tools that can be customised to meet the specific needs of banks. These tools include customer relationship management (CRM), sales and marketing automation, customer service, and analytics. By leveraging LIKE.TG, banks can improve customer satisfaction, increase revenue, and reduce costs. For example, one bank that implemented LIKE.TG saw a 20% increase in customer satisfaction, a 15% increase in revenue, and a 10% decrease in costs. Here are some specific examples of how LIKE.TG can help banks: Gain a complete view of customers: LIKE.TG provides a single, unified platform that allows banks to track all customer interactions, from initial contact to ongoing support. This information can be used to create a complete picture of each customer, which can help banks deliver more personalised and relevant experiences. Track interactions: LIKE.TG allows banks to track all interactions with customers, including phone calls, emails, chat conversations, and social media posts. This information can be used to identify trends and patterns, which can help banks improve their customer service and sales efforts. Deliver personalised experiences: LIKE.TG allows banks to create personalised experiences for each customer. This can be done by using customer data to tailor marketing campaigns, product recommendations, and customer service interactions. Increase revenue: LIKE.TG can help banks increase revenue by providing tools to track sales opportunities, manage leads, and forecast revenue. This information can be used to make informed decisions about which products and services to offer, and how to best target customers. Reduce costs: LIKE.TG can help banks reduce costs by automating tasks, streamlining processes, and improving efficiency. This can free up resources that can be used to focus on other areas of the business. Overall, LIKE.TG is a powerful CRM solution that can help banks improve customer satisfaction, increase revenue, and reduce costs. By leveraging LIKE.TG, banks can gain a competitive advantage in the rapidly changing financial services industry.

					10 Ecommerce Trends That Will Influence Online Shopping in 2024
10 Ecommerce Trends That Will Influence Online Shopping in 2024
Some ecommerce trends and technologies pass in hype cycles, but others are so powerful they change the entire course of the market. After all the innovations and emerging technologies that cropped up in 2023, business leaders are assessing how to move forward and which new trends to implement.Here are some of the biggest trends that will affect your business over the coming year. What you’ll learn: Artificial intelligence is boosting efficiency Businesses are prioritising data management and harmonisation Conversational commerce is getting more human Headless commerce is helping businesses keep up Brands are going big with resale Social commerce is evolving Vibrant video content is boosting sales Loyalty programs are getting more personalised User-generated content is influencing ecommerce sales Subscriptions are adding value across a range of industries Ecommerce trends FAQ 1. Artificial intelligence is boosting efficiency There’s no doubt about it: Artificial intelligence (AI) is changing the ecommerce game. Commerce teams have been using the technology for years to automate and personalise product recommendations, chatbot activity, and more. But now, generative and predictive AI trained on large language models (LLM) offer even more opportunities to increase efficiency and scale personalisation. AI is more than an ecommerce trend — it can make your teams more productive and your customers more satisfied. Do you have a large product catalog that needs to be updated frequently? AI can write and categorise individual descriptions, cutting down hours of work to mere minutes. Do you need to optimise product detail pages? AI can help with SEO by automatically generating meta titles and meta descriptions for every product. Need to build a landing page for a new promotion? Generative page designers let users of all skill levels create and design web pages in seconds with simple, conversational building tools. All this innovation will make it easier to keep up with other trends, meet customers’ high expectations, and stay flexible — no matter what comes next. 2. Businesses are prioritising data management and harmonisation Data is your most valuable business asset. It’s how you understand your customers, make informed decisions, and gauge success. So it’s critical to make sure your data is in order. The challenge? Businesses collect a lot of it, but they don’t always know how to manage it. That’s where data management and harmonisation come in. They bring together data from multiple sources — think your customer relationship management (CRM) and order management systems — to provide a holistic view of all your business activities. With harmonised data, you can uncover insights and act on them much faster to increase customer satisfaction and revenue. Harmonised data also makes it possible to implement AI (including generative AI), automation, and machine learning to help you market, serve, and sell more efficiently. That’s why data management and harmonisation are top priorities among business leaders: 68% predict an increase in data management investments. 32% say a lack of a complete view and understanding of their data is a hurdle. 45% plan to prioritise gaining a more holistic view of their customers. For businesses looking to take advantage of all the new AI capabilities in ecommerce, data management should be priority number one. 3. Conversational commerce is getting more human Remember when chatbot experiences felt robotic and awkward? Those days are over. Thanks to generative AI and LLMs, conversational commerce is getting a glow-up. Interacting with chatbots for service inquiries, product questions, and more via messaging apps and websites feels much more human and personalised. Chatbots can now elevate online shopping with conversational AI and first-party data, mirroring the best in-store interactions across all digital channels. Natural language, image-based, and data-driven interactions can simplify product searches, provide personalised responses, and streamline purchases for a smooth experience across all your digital channels. As technology advances, this trend will gain more traction. Intelligent AI chatbots offer customers better self-service experiences and make shopping more enjoyable. This is critical since 68% of customers say they wouldn’t use a company’s chatbot again if they had a bad experience. 4. Headless commerce is helping businesses keep up Headless commerce continues to gain steam. With this modular architecture, ecommerce teams can deliver new experiences faster because they don’t have to wait in the developer queue to change back-end systems. Instead, employees can update online interfaces using APIs, experience managers, and user-friendly tools. According to business leaders and commerce teams already using headless: 76% say it offers more flexibility and customisation. 72% say it increases agility and lets teams make storefront changes faster. 66% say it improves integration between systems. Customers reap the benefits of headless commerce, too. Shoppers get fresh experiences more frequently across all devices and touchpoints. Even better? Headless results in richer personalisation, better omni-channel experiences, and peak performance for ecommerce websites. 5. Brands are going big with resale Over the past few years, consumers have shifted their mindset about resale items. Secondhand purchases that were once viewed as stigma are now seen as status. In fact, more than half of consumers (52%) have purchased an item secondhand in the last year, and the resale market is expected to reach $70 billion by 2027. Simply put: Resale presents a huge opportunity for your business. As the circular economy grows in popularity, brands everywhere are opening their own resale stores and encouraging consumers to turn in used items, from old jeans to designer handbags to kitchen appliances. To claim your piece of the pie, be strategic as you enter the market. This means implementing robust inventory and order management systems with real-time visibility and reverse logistics capabilities. 6. Social commerce is evolving There are almost 5 billion monthly active users on platforms like Instagram, Facebook, Snapchat, and TikTok. More than two-thirds (67%) of global shoppers have made a purchase through social media this year. Social commerce instantly connects you with a vast global audience and opens up new opportunities to boost product discovery, reach new markets, and build meaningful connections with your customers. But it’s not enough to just be present on social channels. You need to be an active participant and create engaging, authentic experiences for shoppers. Thanks to new social commerce tools — like generative AI for content creation and integrations with social platforms — the shopping experience is getting better, faster, and more engaging. This trend is blurring the lines between shopping and entertainment, and customer expectations are rising as a result. 7. Vibrant video content is boosting sales Now that shoppers have become accustomed to the vibrant, attention-grabbing video content on social platforms, they expect the same from your brand’s ecommerce site. Video can offer customers a deeper understanding of your products, such as how they’re used, and what they look like from different angles. And video content isn’t just useful for ads or for increasing product discovery. Brands are having major success using video at every stage of the customer journey: in pre-purchase consultations, on product detail pages, and in post-purchase emails. A large majority (89%) of consumers say watching a video has convinced them to buy a product or service. 8. Loyalty programs are getting more personalised It’s important to attract new customers, but it’s also critical to retain your existing ones. That means you need to find ways to increase loyalty and build brand love. More and more, customers are seeking out brand loyalty programs — but they want meaningful rewards and experiences. So, what’s the key to a successful loyalty program? In a word: personalisation. Customers don’t want to exchange their data for a clunky, impersonal experience where they have to jump through hoops to redeem points. They want straightforward, exclusive offers. Curated experiences. Relevant rewards. Six out of 10 consumers want discounts in return for joining a loyalty program, and about one-third of consumers say they find exclusive or early access to products valuable. The brands that win customer loyalty will be those that use data-driven insights to create a program that keeps customers continually engaged and satisfied. 9. User-generated content is influencing ecommerce sales User-generated content (UGC) adds credibility, authenticity‌, and social proof to a brand’s marketing efforts — and can significantly boost sales and brand loyalty. In fact, one study found that shoppers who interact with UGC experience a 102.4% increase in conversions. Most shoppers expect to see feedback and reviews before making a purchase, and UGC provides value by showcasing the experiences and opinions of real customers. UGC also breaks away from generic item descriptions and professional product photography. It can show how to style a piece of clothing, for example, or how an item will fit across a range of body types. User-generated videos go a step further, highlighting the functions and features of more complex products, like consumer electronics or even automobiles. UGC is also a cost-effective way to generate content for social commerce without relying on agencies or large teams. By sourcing posts from hashtags, tagging, or concentrated campaigns, brands can share real-time, authentic, and organic social posts to a wider audience. UGC can be used on product pages and in ads, as well. And you can incorporate it into product development processes to gather valuable input from customers at scale. 10. Subscriptions are adding value across a range of industries From streaming platforms to food, clothing, and pet supplies, subscriptions have become a popular business model across industries. In 2023, subscriptions generated over $38 billion in revenue, doubling over the past four years. That’s because subscriptions are a win-win for shoppers and businesses: They offer freedom of choice for customers while creating a continuous revenue stream for sellers. Consider consumer goods brand KIND Snacks. KIND implemented a subscription service to supplement its B2B sales, giving customers a direct line to exclusive offers and flavours. This created a consistent revenue stream for KIND and helped it build a new level of brand loyalty with its customers. The subscription also lets KIND collect first-party data, so it can test new products and spot new trends. Ecommerce trends FAQ How do I know if an ecommerce trend is right for my business? If you’re trying to decide whether to adopt a new trend, the first step is to conduct a cost/benefit analysis. As you do, remember to prioritise customer experience and satisfaction. Look at customer data to evaluate the potential impact of the trend on your business. How costly will it be to implement the trend, and what will the payoff be one, two, and five years into the future? Analyse the numbers to assess whether the trend aligns with your customers’ preferences and behaviours. You can also take a cue from your competitors and their adoption of specific trends. While you shouldn’t mimic everything they do, being aware of their experiences can provide valuable insights and help gauge the viability of a trend for your business. Ultimately, customer-centric decision-making should guide your evaluation. Is ecommerce still on the rise? In a word: yes. In fact, ecommerce is a top priority for businesses across industries, from healthcare to manufacturing. Customers expect increasingly sophisticated digital shopping experiences, and digital channels continue to be a preferred purchasing method. Ecommerce sales are expected to reach $8.1 trillion by 2026. As digital channels and new technologies evolve, so will customer behaviours and expectations. Where should I start if I want to implement AI? Generative AI is revolutionising ecommerce by enhancing customer experiences and increasing productivity, conversions, and customer loyalty. But to reap the benefits, it’s critical to keep a few things in mind. First is customer trust. A majority of customers (68%) say advances in AI make it more important for companies to be trustworthy. This means businesses implementing AI should focus on transparency. Tell customers how you will use their data to improve shopping experiences. Develop ethical standards around your use of AI, and discuss them openly. You’ll need to answer tough questions like: How do you ensure sensitive data is anonymised? How will you monitor accuracy and audit for bias, toxicity, or hallucinations? These should all be considerations as you choose AI partners and develop your code of conduct and governance principles. At a time when only 13% of customers fully trust companies to use AI ethically, this should be top of mind for businesses delving into the fast-evolving technology. How can commerce teams measure success after adopting a new trend? Before implementing a new experience or ecommerce trend, set key performance indicators (KPIs) and decide how you’ll track relevant ecommerce metrics. This helps you make informed decisions and monitor the various moving parts of your business. From understanding inventory needs to gaining insights into customer behaviour to increasing loyalty, you’ll be in a better position to plan for future growth. The choice of metrics will depend on the needs of your business, but it’s crucial to establish a strategy that outlines metrics, sets KPIs, and measures them regularly. Your business will be more agile and better able to adapt to new ecommerce trends and understand customer buying patterns. Ecommerce metrics and KPIs are valuable tools for building a successful future and will set the tone for future ecommerce growth.

					10 Effective Sales Coaching Tips That Work
10 Effective Sales Coaching Tips That Work
A good sales coach unlocks serious revenue potential. Effective coaching can increase sales performance by 8%, according to a study by research firm Gartner.Many sales managers find coaching difficult to master, however — especially in environments where reps are remote and managers are asked to do more with less time and fewer resources.Understanding the sales coaching process is crucial in maximising sales rep performance, empowering reps, and positively impacting the sales organisation through structured, data-driven strategies.If you’re not getting the support you need to effectively coach your sales team, don’t despair. These 10 sales coaching tips are easy to implement with many of the tools already at your disposal, and are effective for both in-person and remote teams.1. Focus on rep wellbeingOne in three salespeople say mental health in sales has declined over the last two years, according to a recent LIKE.TG survey. One of the biggest reasons is the shift to remote work environments, which pushed sales reps to change routines while still hitting quotas. Add in the isolation inherent in virtual selling and you have a formula for serious mental and emotional strain.You can alleviate this in a couple of ways. First, create boundaries for your team. Set clear work hours and urge reps not to schedule sales or internal calls outside of these hours. Also, be clear about when reps should be checking internal messages and when they can sign off.Lori Richardson, founder of sales training company Score More Sales, advises managers to address this head-on by asking reps about their wellbeing during weekly one-on-ones. “I like to ask open-ended questions about the past week,” she said. “Questions like, ‘How did it go?’ and ‘What was it like?’ are good first steps. Then, you need to listen.”When the rep is done sharing their reflection, Richardson suggests restating the main points to ensure you’re on the same page. If necessary, ask for clarity so you fully understand what’s affecting their state of mind. Also, she urges: Don’t judge. The level of comfort required for sharing in these scenarios can only exist if you don’t jump to judgement.2. Build trust with authentic storiesFor sales coaching to work, sales managers must earn reps’ trust. This allows the individual to be open about performance challenges. The best way to start is by sharing personal and professional stories.These anecdotes should be authentic, revealing fault and weakness as much as success. There are two goals here: support reps with relatable stories so they know they’re not struggling alone, and let them know there are ways to address and overcome challenges.For example, a seasoned manager might share details about their first failed sales call as a cautionary tale – highlighting poor preparation, aggressive posturing, and lack of empathy during the conversation. This would be followed by steps the manager took to fix these mistakes, like call rehearsing and early-stage research into the prospect’s background, business, position, and pain points.3. Record and review sales callsSales coaching sessions, where recording and reviewing sales calls are key components aimed at improving sales call techniques, have become essential in today’s sales environment. Once upon a time, sales reps learned by shadowing tenured salespeople. While this is still done, it’s inefficient – and often untenable for virtual sales teams.To give sales reps the guidance and coaching they need to improve sales calls, deploy an intuitive conversation recording and analysis tool like Einstein Conversation Insights (ECI). You can analyse sales call conversations, track keywords to identify market trends, and share successful calls to help coach existing reps and accelerate onboarding for new reps. Curate both “best of” and “what not to do” examples so reps have a sense of where the guide rails are.4. Encourage self-evaluationWhen doing post-call debriefs or skill assessments – or just coaching during one-on-ones – it’s critical to have the salesperson self-evaluate. As a sales manager, you may only be with the rep one or two days a month. Given this disconnect, the goal is to encourage the sales rep to evaluate their own performance and build self-improvement goals around these observations.There are two important components to this. First, avoid jumping directly into feedback during your interactions. Relax and take a step back; let the sales rep self-evaluate.Second, be ready to prompt your reps with open-ended questions to help guide their self-evaluation. Consider questions like:What were your big wins over the last week/quarter?What were your biggest challenges and where did they come from?How did you address obstacles to sales closings?What have you learned about both your wins and losses?What happened during recent calls that didn’t go as well as you’d like? What would you do differently next time?Reps who can assess what they do well and where they can improve ultimately become more self-aware. Self-awareness is the gateway to self-confidence, which can help lead to more consistent sales.5. Let your reps set their own goalsThis falls in line with self-evaluation. Effective sales coaches don’t set focus areas for their salespeople; they let reps set this for themselves. During your one-on-ones, see if there’s an important area each rep wants to focus on and go with their suggestion (recommending adjustments as needed to ensure their goals align with those of the company). This creates a stronger desire to improve as it’s the rep who is making the commitment. Less effective managers will pick improvement goals for their reps, then wonder why they don’t get buy-in.For instance, a rep who identifies a tendency to be overly chatty in sales calls might set a goal to listen more. (Nine out of 10 salespeople say listening is more important than talking in sales today, according to a recent LIKE.TG survey.) To help, they could record their calls and review the listen-to-talk ratio. Based on industry benchmarks, they could set a clear goal metric and timeline – a 60/40 listen-to-talk ratio in four weeks, for example.Richardson does have one note of caution, however. “Reps don’t have all the answers. Each seller has strengths and gaps,” she said. “A strong manager can identify those strengths and gaps, and help reps fill in the missing pieces.”6. Focus on one improvement at a timeFor sales coaching to be effective, work with the rep to improve one area at a time instead of multiple areas simultaneously. With the former, you see acute focus and measurable progress. With the latter, you end up with frustrated, stalled-out reps pulled in too many directions.Here’s an example: Let’s say your rep is struggling with sales call openings. They let their nerves get the best of them and fumble through rehearsed intros. Over the course of a year, encourage them to practice different kinds of openings with other reps. Review their calls and offer insight. Ask them to regularly assess their comfort level with call openings during one-on-ones. Over time, you will see their focus pay off.7. Ask each rep to create an action planOpen questioning during one-on-ones creates an environment where a sales rep can surface methods to achieve their goals. To make this concrete, have the sales rep write out a plan of action that incorporates these methods. This plan should outline achievable steps to a desired goal with a clearly defined timeline. Be sure you upload it to your CRM as an attachment or use a tool like Quip to create a collaborative document editable by both the manager and the rep. Have reps create the plan after early-quarter one-on-ones and check in monthly to gauge progress (more on that in the next step).Here’s what a basic action plan might look like:Main goal: Complete 10 sales calls during the last week of the quarterSteps:Week 1: Identify 20-25 prospectsWeek 2: Make qualifying callsWeek 3: Conduct needs analysis (discovery) calls, prune list, and schedule sales calls with top prospectsWeek 4: Lead sales calls and close dealsThe power of putting pen to paper here is twofold. First, it forces the sales rep to think through their plan of action. Second, it crystallises their thinking and cements their commitment to action.8. Hold your rep accountableAs businessman Louis Gerstner, Jr. wrote in “Who Says Elephants Can’t Dance?”, “people respect what you inspect.” The effective manager understands that once the plan of action is in place, their role as coach is to hold the sales rep accountable for following through on their commitments. To support them, a manager should ask questions during one-on-ones such as:What measurable progress have you made this week/quarter?What challenges are you facing?How do you plan to overcome these challenges?You can also review rep activity in your CRM. This is especially easy if you have a platform that combines automatic activity logging, easy pipeline inspection, and task lists with reminders. If you need to follow up, don’t schedule another meeting. Instead, send your rep a quick note via email or a messaging tool like Slack to level-set.9. Offer professional development opportunitiesAccording to a study by LinkedIn, 94% of employees would stay at a company longer if it invested in their career. When companies make an effort to feed their employees’ growth, it’s a win-win. Productivity increases and employees are engaged in their work.Book clubs, seminars, internal training sessions, and courses are all great development opportunities. If tuition reimbursement or sponsorship is possible, articulate this up front so reps know about all available options.Richardson adds podcasts to the list. “Get all of your salespeople together to talk about a podcast episode that ties into sales,” she said. “Take notes, pull key takeaways and action items, and share a meeting summary the next day with the group. I love that kind of peer engagement. It’s so much better than watching a dull training video.”10. Set up time to share failures — and celebrationsAs Forbes Council member and sales vet Adam Mendler wrote of sales teams, successful reps and executives prize learning from failure. But as Richardson points out, a lot of coaches rescue their reps before they can learn from mistakes: “Instead of letting them fail, they try to save an opportunity,” she said. “But that’s not scalable and doesn’t build confidence in the rep.”Instead, give your reps the freedom to make mistakes and offer them guidance to grow through their failures. Set up a safe space where reps can share their mistakes and learnings with the larger team — then encourage each rep to toss those mistakes on a metaphorical bonfire so they can move on.By embracing failure as a learning opportunity, you also minimise the likelihood of repeating the same mistakes. Encourage your reps to document the circumstances that led to a missed opportunity or lost deal. Review calls to pinpoint where conversations go awry. Study failure, and you might be surprised by the insights that emerge.Also — and equally as important — make space for celebrating big wins. This cements best practices and offers positive reinforcement, which motivates reps to work harder to hit (or exceed) quota.Next steps for your sales coaching programA successful sales coach plays a pivotal role in enhancing sales rep performance and elevating the entire sales organisation. Successful sales coaching requires daily interaction with your team, ongoing training, and regular feedback, which optimises sales processes to improve overall sales performance. As Lindsey Boggs, global director of sales development at Quantum Metric, noted, it also requires intentional focus and a strategic approach to empower the sales team, significantly impacting the sales organisation.“Remove noise from your calendar so you can focus your day on what’s going to move the needle the most — coaching,” she said. Once that’s prioritised, follow the best practices above to help improve your sales reps’ performance, focusing on individual rep development as a key aspect of sales coaching. Remember: coaching is the key to driving sales performance.Steven Rosen, founder of sales management training company STAR Results, contributed to this article.
企业管理
AI translation apps: Benefits for your travels?
AI translation apps
Benefits for your travels?
This article explains the benefits of AI translation apps for travelers, which offer a practical and efficient solution worldwide.Despite the increasing accessibility of international travel, language barriers continue to pose a significant challenge. At LIKE.TG, our goal is to help you explore the world more easilyThe Revolution of AI in TranslationAI technology has revolutionized language translation, providing unprecedented accuracy and contextualization.These applications continuously learn, improving their ability to understand and translate linguistic and cultural nuances with each update.Benefits of AI Translation AppsTravel without language barriersImagine asking for directions, interacting with locals, or even resolving emergencies in a language you don’t speak.AI translation apps make it all possible, removing one of the biggest obstacles for travelers: language.Instant communicationImagine looking at a menu in an Italian restaurant and every dish sounds like a Harry Potter spell. This is where your AI translation app acts as your personal wand.Imagine having a magic button that allows you to instantly understand and speak any language. Well, in the real world, that “wand” fits in your pocket and is called an AI translation app.These apps are like having a personal mini translator with you 24/7, ready to help you order that strange dish on the menu without ending up eating something you can’t even pronounce.Whether you’re trying to unravel the mystery of a Japanese sign or want to know what the hell that road sign in Iceland means, the instant translation offered by some AI apps is your best friend.Cultural learning beyond wordsSome of these apps don’t just translate words for you; they immerse you in a pool of culture without the need for floats. Think of them as a bridge between you and the authentic native experiences that await you in every corner of the world.Suddenly you learn to say “thank you” in Italian so convincingly that even the “nonna” at the restaurant smiles at you.There are tools that not only teach you to speak like a native, but to understand their gestures, their jokes, and even prepare you to be the “King of Karaoke in Korea”.Gain independence and be the boss of your own trip.Need a tour guide? No way! With an AI translation app in your pocket, you become the hero of your own travel odyssey.These digital wonders give you the freedom to control your adventure, allowing you to discover those secret corners of Paris or navigate the back streets of Tokyo without becoming part of the scenery.They are your golden ticket to freedom, giving you the power to explore at your leisure without having to follow the pack like a duck in a line.It’s time to take the reins, blaze your own trail, and collect the epic stories everyone wants to hear.With these apps, independence isn’t just a word; it’s your new way of traveling.Improve your dining experienceHave you ever felt like a detective trying to solve the mystery of a foreign menu? With AI translation apps, the mystery is solved instantly.Imagine pointing your phone at a dish called “Risotto ai Funghi” and discovering that you’re not ordering a strange dessert, but a delicious rice with mushrooms.These apps are your personal Michelin guide, ensuring that every bite is an adventure for your taste buds and not an unwanted surprise.Makes using public transportation easierSay goodbye to the complicated signs and misunderstandings that get you around town.It’s like every traffic sign and schedule speaks your language, giving you a VIP pass to move around the city like a fish in water, ready to explain that the train leaves in 5 minutes, not 50.Suddenly, getting from point A to point B is as easy as ordering a pizza.Improve your personal safetyIn a pinch, these apps become your capeless hero. Whether it’s explaining a shellfish allergy or locating the nearest emergency exit, they help you communicate clearly and avoid those “lost in translation” moments no one wants to experience.Access real-time local information:See that poster about a local event? Yeah, the one that looks interesting but is in a language you don’t understand.With a quick scan, your translation app tells you all about that secret concert or food festival that only the locals go to.Congratulations! You’ve just upgraded your status from tourist to expert traveler.Flexibility and convenienceWant to change your plans and venture to a nearby town recommended by a local you met yesterday at the train station? Of course you can!With the confidence your translation app gives you, you can decide to follow that spontaneous advice and visit a nearby town without worrying about the language. Your trip, your rules.Choosing the best translation app for your travelsWhen choosing a translation app, it is important to consider the variety of languages available, the accuracy of the translation, and the additional features it offers.LIKE.TG apps, for example, stand out for their wide range of supported languages and innovative features that go beyond simple translation, such as real-time speech recognition and built-in language lessons.REMEMBER !!!You can downloadour available appsfor translating and learning languages correctly available for free on googleplay and applestores.Do not hesitate to visit ourLIKE.TG websiteand contact us with any questions or problems you may have, and of course, take a look at any ofour blog articles.
AI-based translation tools: Analysis and comparison of the best ones
AI-based translation tools
Analysis and comparison of the best ones
As globalization increases, companies and individuals are finding it necessary to communicate more frequently with people who speak different languages.As a result, the need for translation tools has become more pressing.The good news is that there are now AI-based translation tools that make the process of translating text and speech faster and more accurate than ever before.In this article, I will analyze and compare the best AI-based translation tools available, discussing their advantages, features and drawbacks.Introduction to AI-based translation toolsAI-based translation tools use artificial intelligence to translate text and speech from one language to another. These tools have become increasingly popular in recent years thanks to advances in machine learning and natural language processing. Such tools are faster, more accurate and can handle a higher volume of work.Benefits of using AI-based translation toolsOne of the main advantages of using AI-based translation tools is speed. These tools can translate large volumes of text in a matter of seconds, whereas it would take a human translator much longer to do the same job.They are less likely to make mistakes and can also be used to translate speeches in real time, which makes them very useful for international conferences or business meetings.Popular AI-based translation tools and their featuresThere are many AI-based translation tools, each with its own unique features. Here are some of the most popular ones and what they offer:1. Google TranslateGoogle Translate is one of the most well-known AI-based translation tools. It offers translations in over 100 languages and can be used to translate text, speech, and even images. Google Translate also offers a feature called “Conversation Mode,” which allows two people to have a conversation in different languages using the same device.2. Microsoft TranslatorMicrosoft Translator is another popular AI-based translation tool. It offers translations in over 60 languages and can be used to translate text, speech, and images. Microsoft Translator also offers a feature called “Live Feature,” which allows two people to have a conversation in different languages using their own devices.3. DeepLDeepL is a newer AI-based translation tool, but it has quickly gained popularity thanks to its high-quality translations. It offers translations in nine languages and can be used to translate text. DeepL uses deep learning algorithms to produce translations that are more accurate and natural-sounding than those produced by other translation tools.4. LIKE.TG TranslateLIKE.TG Translate is a relatively new AI-based translation tool that has gained popularity in recent years. It is available in over 125 languages and can translate text, voice and images. One of the unique features of LIKE.TG Translate is its ability to translate text within other apps.The best feature of these apps is that not only do they base their translation using AI but they have a team of native translators behind them constantly improving their applications to make them even better.Factors to consider when choosing an AI-based translation toolWhen choosing an AI-based translation tool, there are several factors to consider. The first is the languages you need to translate. Make sure the tool you choose supports the languages you need. The second factor is the type of translations you need. Do you need to translate text, speech, or images? Do you need real-time translation for conversations? The third factor is the accuracy of the translations. Consider the quality of the translations produced by each tool. Lastly, consider the cost of the tool. Some AI-based translation tools are free, while others require a subscription or payment per use.Pros and cons of using AI-based translation toolsLike any tool, AI-based translation tools have pros and cons. Here are some of the main advantages and drawbacks of using these tools:After a thorough analysis, I can faithfully describe to you some of the most characteristic pros and cons of these tools:PROSAccuracy: These tools are able to better understand the context and syntax of the language, which translates into greater translation accuracy.Speed: Translating large amounts of text can take a long time if done manually, whereas AI-based translation tools are able to process large amounts of text in a matter of seconds.Cost savings: AI-based translation tools are often less expensive than human translation services, especially for large projects.Integrations: Many of these tools integrate with other platforms and productivity tools, making them easy to use in different contexts.CONSLack of context: These tools often lack context, which can result in inaccurate or inconsistent translations. For example, a literal translation of a sentence in one language into another may not take into account cultural connotations or social context and result in a translation that makes no sense.Lack of accuracy: Although AI-based translation tools have improved significantly in recent years, they are still not as accurate as humans. Translations can be inaccurate or have grammatical and spelling errors, especially in more complex or technical languages.They cannot capture nuances or tones: Such translation tools cannot capture nuances or tones that are often important in human communication. For example, they may miss the sarcastic or ironic tone of a sentence and translate it literally.Language dependency: language dependent, meaning that they work best for translating between widely spoken and documented languages but do not represent less common languages or regional dialects well. .Cost: While there are some available for free, many of the high-quality tools are quite expensive.Lack of customization: AI-based translation tools cannot be customized to meet the specific needs of an individual or company. This can limit their usefulness especially when highly specialized or technical translation is required.Privacy and security: Some tools collect and store sensitive data, which can raise serious concerns about data privacy and security.In conclusion, AI-based translation tools offer a number of advantages in terms of speed, accuracy and cost, but it is important to be aware of their limitations and challenges when selecting a tool.How AI-based translation tools are changing the translation industryAI-based translation tools are changing the translation industry in several ways. The first is that the translation process is faster and more efficient. This allows translators to handle larger volumes of work and deliver projects faster. The second way in which they are changing the industry is that specialized translators are becoming more in demand, as human quality is irreplaceable and although they can do basic translations, they have problems with technical or specialized language.This means that specialized translators in certain areas are more in demand than ever.The future of AI-based translation toolsThe future of AI-based translation tools is bright. As technology continues to advance, these tools will become even more sophisticated and accurate. We may eventually see a tool capable of handling all forms of language, including slang and regional dialects. It is also possible that they will become more integrated into our daily lives, allowing us to communicate with people who speak different languages more easily than ever before, yet experts continue to warn that humans cannot be replaced.Conclusion and recommendations for the best AI-based translation toolsIn conclusion, AI-based translation tools offer many advantages over traditional methods. They are faster, more accurate and can handle a higher volume of work. However, it is important to consider the languages you need to translate, the type of translations you need, the accuracy of the translations and the cost of the tool when choosing an AI-based translation tool, because at the end of the day no AI can replace a human being, nor can it emulate the human quality that a human being can bring to us.Based on our analysis and comparison, we recommend Google Translate for its versatility and variety of features. However, if you need high quality translations, LIKE.TG Translate may be the best choice.REMEMBER !!!You can downloadour available appsfor translating and learning languages correctly available for free on googleplay and applestores.Do not hesitate to visit ourLIKE.TG websiteand contact us with any questions or problems you may have, and of course, take a look at any ofour blog articles.
Artificial intelligence (AI) in language teaching: Future perspectives and challenges
Artificial intelligence (AI) in language teaching
Future perspectives and challenges
In a world where educational technology is advancing by leaps and bounds, it is no surprise that artificial intelligence is revolutionizing the way we learn languages.The combination of machine learning in education and AI in language teaching has opened up a range of exciting possibilities and, at the same time, poses challenges that we must face to make the most of this innovation.What is Artificial Intelligence in Language Teaching?Artificial intelligence (AI) in language teaching refers to the use of algorithms and computer systems to facilitate the process of learning a new language.From mobile apps to online platforms, AI has been integrated into a variety of tools designed to help students improve their language skills efficiently and effectively.Advances in AI and its challenges in language learningArtificial intelligence (AI) is radically transforming the way we learn languages. With the emergence of AI-powered apps and platforms, students have access to innovative tools that personalize learning to their individual needs.These tools use machine learning algorithms to analyze student progress and deliver tailored content, from grammar exercises to conversation practice.Additionally, AI-powered translation has significantly improved in accuracy and speed. Apps like LIKE.TG Translate allow users to instantly translate between multiple languages ​​with just a few clicks, making multilingual communication easier.Artificial Intelligence offers unprecedented potential to improve the language learning process, providing students with personalized and efficient tools.Positive Perspectives of AI in Language TeachingOne of the main advantages of AI in language teaching is its ability to personalize learning. Through data analysis and machine learning, AI systems can adapt digital learning platforms, content and activities based on the needs and preferences of each student.This allows for a more individualized and effective approach to improving language skills.In addition, AI has also enabled the development of more accurate and faster real-time translation tools. With apps like LIKE.TG Translate, users can access instant translations in multiple languages ​​with just a few clicks.This facilitates communication in multilingual environments and expands opportunities for interaction and learning.AI in language teaching opens the doors to global communication without barriersChallenges and Future ChallengesDespite advances in AI applied to language teaching, there are still important challenges that we must overcome. One of the main challenges is to guarantee the quality and accuracy of the content generated by AI.While AI systems can be effective in providing feedback and practice exercises, there are still areas where human intervention is necessary to correct errors and provide high-quality teaching.Another important challenge is ensuring that AI in language teaching is accessible to everyone. As we move towards an increasingly digitalized future, it is crucial to ensure that all people, regardless of their geographic location or socioeconomic status, have access to AI language learning apps.This will require investment in technological infrastructure and digital literacy programs around the world.How Long Is It Possible to Learn a Language with Artificial Intelligence?With the help of artificial intelligence (AI), learning a new language can be more efficient than ever.Although the time required to master a language varies depending on various factors, such as the complexity of the language, the level of dedication of the learner, and the quality of the AI ​​tools used, many people have managed to acquire significant language skills in a relatively short period of time.Thanks to AI applications and platforms designed specifically for language learning, users can benefit from a personalized approach tailored to their individual needs.These tools use machine learning algorithms to identify areas for improvement and provide relevant content, speeding up the learning process.On average, some people have reported significant gains in their language proficiency in just a few months of consistent use of AI tools.However, it is important to keep in mind that learning a language is an ongoing process and that completing mastery can take years of constant practice and exposure to the language in real-world contexts.Ultimately, the time needed to learn a language with AI depends largely on the commitment and dedication of the student.“The journey to mastering a language with AI begins with small daily steps, but constant dedication is the key to achieving the desired fluency.”In conclusion, the integration of technology in education and artificial intelligence in language teaching offers exciting opportunities to improve the learning process and promote intercultural global communication.However, it also poses challenges that we must proactively address to ensure that everyone can benefit from this innovation in education.With a collaborative approach and a continued commitment to educational excellence, we can fully realize the potential of AI in language teaching and prepare for a multilingual and globalized future.Visit our website for more information and begin your journey towards mastering languages ​​​​with the best and most advanced technology.
海外工具
10个最好的网站数据实时分析工具
10个最好的网站数据实时分析工具
网络分析工具可以帮助你收集、预估和分析网站的访问记录,对于网站优化、市场研究来说,是个非常实用的工具。每一个网站开发者和所有者,想知道他的网站的完整的状态和访问信息,目前互联网中有很多分析工具,本文选取了20款最好的分析工具,可以为你提供实时访问数据。1.Google Analytics这是一个使用最广泛的访问统计分析工具,几周前,Google Analytics推出了一项新功能,可以提供实时报告。你可以看到你的网站中目前在线的访客数量,了解他们观看了哪些网页、他们通过哪个网站链接到你的网站、来自哪个国家等等。2. Clicky与Google Analytics这种庞大的分析系统相比,Clicky相对比较简易,它在控制面板上描供了一系列统计数据,包括最近三天的访问量、最高的20个链接来源及最高20个关键字,虽说数据种类不多,但可直观的反映出当前站点的访问情况,而且UI也比较简洁清新。3. WoopraWoopra将实时统计带到了另一个层次,它能实时直播网站的访问数据,你甚至可以使用Woopra Chat部件与用户聊天。它还拥有先进的通知功能,可让你建立各类通知,如电子邮件、声音、弹出框等。4. Chartbeat这是针对新闻出版和其他类型网站的实时分析工具。针对电子商务网站的专业分析功能即将推出。它可以让你查看访问者如何与你的网站进行互动,这可以帮助你改善你的网站。5. GoSquared它提供了所有常用的分析功能,并且还可以让你查看特定访客的数据。它集成了Olark,可以让你与访客进行聊天。6. Mixpane该工具可以让你查看访客数据,并分析趋势,以及比较几天内的变化情况。7. Reinvigorate它提供了所有常用的实时分析功能,可以让你直观地了解访客点击了哪些地方。你甚至可以查看注册用户的名称标签,这样你就可以跟踪他们对网站的使用情况了。8. Piwi这是一个开源的实时分析工具,你可以轻松下载并安装在自己的服务器上。9. ShinyStat该网站提供了四种产品,其中包括一个有限制的免费分析产品,可用于个人和非营利网站。企业版拥有搜索引擎排名检测,可以帮助你跟踪和改善网站的排名。10. StatCounter这是一个免费的实时分析工具,只需几行代码即可安装。它提供了所有常用的分析数据,此外,你还可以设置每天、每周或每月自动给你发送电子邮件报告。本文转载自:https://www.cifnews.com/search/article?keyword=工具
10款常用的SEO内容优化工具
10款常用的SEO内容优化工具
谷歌使用含有数百个加权因子的复杂算法,根据给定网页与给定关键词的相关性,对网页进行索引和排名。数字营销人员则通过实证测试试图弄清这个复杂算法背后的原理,并采用特定的方法来提高网页在搜索结果页中的排名,这一过程被叫做搜索引擎优化(SEO),这是数字营销人员必须掌握的重要技能。 如果没有优质SEO内容工具,优化网页内容将是一项冗长乏味的工作。为了帮助您节省大量时间和劳动力,本为会为您推荐10个最佳SEO内容创作工具,这些工具适用于内容创作过程的不同阶段。 1. Google Search Console 价格:网站所有者可免费使用 作用:Google Search Console是谷歌自己的工具,能够帮助提高网站在搜索引擎结果页面中的排名。它包括网站性能监视工具,页面加载时间监视工具。您还可以监控您的网站在Google搜索结果中的排名,了解哪些页面是针对特定关键词进行排名的。您还可以查看网页在搜索结果页面的展示次数和点击次数。它帮助您确定该优化哪些内容,以及接下来该定位哪些关键词。 2. Google Keyword Planner 价格:拥有Google Ads账户的人均可免费使用 作用:Google Keyword Planner是进行基本的关键词研究的最佳免费工具之一。您可以 1)发现新关键词:输入任何关键词来查看与其类似的关键词列表,以及它们的搜索量和相关指标,使得你很容易找到新的关键字优化目标;2)预测关键词趋势:监控趋势,以发现流行的搜索关键词。Kenny觉得这个工具只适合做SEM的小伙伴,如果你是做SEO的,那查找到的关键词数据不适合SEO。 3. WordStream 价格:免费 作用:WordStream 提供了一个精简版的Google Keyword Planner,它是免费的,易于使用。只需输入您选择的关键词,选择一个行业,并输入您的位置,然后单击Email All My Keywords按钮,您就可以获得关键词列表和它们在Google和Bing上的搜索量,以及每个关键词的平均每次点击成本(CPC) 4. SEMrush 价格:部分功能免费,订阅制99.95美元/月 作用:SEMrush 是最流行的工具之一,适用于所有类型的数字营销人员。它包含40多种不同的工具,可以帮助进行SEO、PPC和社交媒体管理。营销人员可以使用SEMrush分析反向链接、进行关键词研究、分析自己或竞争对手的网站性能和流量,并发现新的市场和机会。SEMrush还有一个SEO审计程序,可以帮助解决网站SEO的一些技术问题。 图片来源:SEMrush 5. BuzzSumo 价格:79美元/月 作用:BuzzSumo帮助营销人员有效分析网站内容,同时紧跟热门趋势。BuzzSumo能够找到用户在不同平台上最喜欢分享的内容。只需要输入网站链接,就能查看什么是该网站最热门的内容。您还可以分析过去一天内,一个月内以及一年内的趋势,并且按照作者或者平台过滤。 6. Answer the Public 价格:每天3次免费使用,无限使用99美元/月 作用:输入某一关键词,您可以查找到任何与之相联系的关键词,并获得可视化报告。这些关键字以您输入的关键词为中心,形成一个网状结构,展示它们之间的联系。借助Answer the Public,营销人员可以撰写针对性强的文章,使网页更有可能出现在Google Snippets中。 图片来源:Answer the Public 7. Yoast SEO 价格:基础版免费,高级版89美元/月 作用:Yoast SEO是一个WordPress插件。它可在您使用WordPress优化博客文章时,为您提供实时反馈,提供改进建议。它类似一个清单工具,实时告诉你撰写网站博文时还可以做哪些事来优化SEO。 8. Keyword Density Checker 价格:每月500次使用限制,如需解锁更多使用次数,可购买50美元/年的高级版 作用:关键字密度(Keyword density)是谷歌等搜索引擎用来对网页进行排名的重要因素。您应该确保目标关键词在每篇文章中被提到足够多的次数,同时还不能滥用关键词。keyword density checker可以计算出每个关键词在您的文章中被提及的次数。只要复制粘贴文本,您就能知道文章中出现频率最高的关键词列表。对于大多数内容而言,目标关键字的密度最好在2%到5%。 图片来源:Keyword Density Checker 9. Read-Able 价格:免费版可供使用,付费版4美元/月 作用:据统计,北美人的平均阅读水平在八年级左右。因此,如果北美人是您的目标受众,您应该撰写清晰易懂的句子和文章。如果您的目标受众受过大学教育,则可以使用较长的单词和复杂的句子。Read-able帮助您将文章写作水平与目标受众的阅读水平相匹配,为读者提供最佳体验。它提供阅读水平检查,语法和拼写检查等功能。 10. Grammarly Premium 价格:11.66美元/月 作用:搜索引擎将网站的拼写和语法纳入排名范围。如果网站内容包含许多拼写错误,它就不太可能获得一个高排名。Grammarly可以轻松创建语法正确且没有拼写错误的内容。您可以将Grammarly作为插件添加到浏览器,并在撰写电子邮件、社交媒体更新或博客文章时使用它。 从关键词研究到拼写检查和语法纠正,这10种工具涵盖了网站内容创建的每一个步骤。我们希望您在为网站编写内容时,可以使用其中一部分工具来节省时间和精力。如果您在实操上遇到困难,或者需要专业的咨询服务,一个专业的数字营销团队正是您需要的!Ara Analytics有丰富的搜索引擎优化经验,欢迎联系我们,我们将为您提供定制化的专业服务。 往期推荐: 支招!新网站引流SEO优化该怎么做? 十七招教你快速提升网站流量 | Google “SEO到底多久才可以见效啊?”-跨境电商提高自然流量必须知道的五个真相 【Google SEO】12款常用的免费谷歌SEO工具推荐- 助网站流量翻倍增长 (来源:Kenny出海推广) 以上内容属作者个人观点,不代表LIKE.TG立场!本文经原作者授权转载,转载需经原作者授权同意。​ 本文转载自:https://www.cifnews.com/search/article?keyword=工具
11大亚马逊数据工具,好用到尖叫!(黑五网一特惠福利)
11大亚马逊数据工具,好用到尖叫!(黑五网一特惠福利)
平台商家想要销量好,关键要选择有针对性的数据工具。本文将分享11款相关产品,帮助国内亚马逊卖家更好地解决日常销售中的问题。 这些工具可以帮助卖家找到一定需求的利基市场以及热销产品。 废话不多说,接着往下看吧! 1、 AmzChart (图片来源:AmzChart) AmzChart中的Amazon BSR图表工具涵盖9个国家,拥有超过数十万的产品分析。 如果你想在竞争中脱颖而出赢得竞品的市场份额,为企业带来财富的话,那么选择AmzChart准没错! 你可以选择AmzChart的理由: • Amazon BSR中可找到低竞争利基产品,助力销量增长至200%。 • 短短一分钟之内即可找到热销品类,帮助卖家深入更大的利润空间。 • 追踪竞争对手产品数据,并以电子邮件形式提供反馈。 • 反查对手ASIN功能可帮助商家分析竞争对手的关键词。 • 跟踪竞争对手的各项平台指标。 • 获取产品价格趋势,且可以轻松下载历史跟踪器插件,并安装自己的网站上。 • 通过分析报告和视频教程获得专业指导——在亚马逊经商之旅的各个阶段,你都不会孤立无援。 【点击此处】获取黑五网一福利:前3个月享5折优惠 2、 Jungle Scout (图片来源:Jungle Scout) 无论你是新手商家,或是已有经验的亚马逊老司机,Jungle Scout均可为你提供多方支持。 你可以选择Jungle Scout的理由: • 可使用筛选器从产品数据库中找到热销产品,快速又方便。 • 平台新手可通过量化数据做出决策,轻松推出产品。 • Jungel Scout可帮助商家精简业务流程,提高市场洞察能力。 • 大量的功能,如排名跟踪、listing搭建器、评价自动化、库存监管等。 3、Seller Labs Pro (图片来源:SellerLabs) 作为亚马逊智能关键字工具之一,SellerLabs能帮助商家提高自然排名和付费流量,以及一系列广泛工具。 无论是长尾关键词,还是PPC术语,你在这个工具中找到。专业版每个月49美元起价。年度计划更为划算,每月39美元起,共可节省120美元。 你可以选择Seller Labs Pro的理由: • 商家随时可监控流量、广告支出、转化率和下载报告,并将收到重要指标的通知。 • 实时通知可以帮助商家做出决策,避免缺货。 • 基于AI智能,为构建SEO策略提供详细建议。 • 访问优化工具,抓取热销产品关键字,节省运营时间。 4、 Helium 10 (图片来源:Helium 10) 作为一体化的亚马逊数据工具,Helium 10可轻松助力平台商家拓展业务。 你可以选择Helium 10 的理由: • 数据库中有4.5亿条ASIN数据,可帮助商家更快地找到产品。更直观进行分析和利润估算,以验证产品是否能够成功打入市场。 • 您可以探索关键字研究,如单字、反查对手ASIN、后端和低竞争度短语。 • 数百个关键字无缝编写listing,并让排名更靠前。 • 内置的安全工具能够避免安全威胁。可以使用警报和更新轻松地管理您的业务。 • 分析可以帮助做出强有力的决策,形成更好的产品排名。 • 可以轻松使用PPC管理和自动化以促进业务增长。 【点击此处】获取黑五限时特惠:购买两个月Diamond钻石套餐可享受5折优惠并获得额外福利。 5、AmaSuite 5 (图片来源:AmaSuite 5) AmaSuite 5具有强大的新功能,其中包括可以在Mac和Windows双系统完形成无缝工作流的Research桌面软件。 通过AmaSuite 5工具套件,商家可以发现利好关键字和产品,从而在亚马逊上赚到一笔。 你可以选择AmaSuite 5的理由: • 使用Ama Product Analyzer,可以找到各个品类的畅销产品。 • 可以通过输入主要产品关键字找到类似款式的畅销产品。 • 通过提取产品评论获得自有品牌产品想法,并可分析产品特点和优势,确保完成无风险销售行为。 • 访问亚马逊销售课程奖金,并学习如何在亚马逊开展规模化销售业务。其中的分步指南事无巨细地给予商家运营指导。 6、AMZBase (图片来源:AMZBase) AMZBase是一个免费的谷歌浏览器插件,以帮助亚马逊商家正确地选品。 你可以选择AMZBase 的理由: • 帮助获取亚马逊产品ASIN编码与listing标题描述。 • 免费访问CamelCamelCamel、阿里巴巴、全球速卖通、eBay和谷歌搜索。 • 可通过自动计算FBA费用确定预期利润。 • 一站式即时搜索工具,搜索谷歌及阿里巴巴上的相关产品。 • 只需选择关键字即可立即搜索。 • 使用AMZBase前,请将谷歌浏览器升级至最新版本。 7、Unicorn Smasher (图片来源:Unicorn Smasher) Unicorn Smasher是AmzTracker旗下产品,可以节省商家在亚马逊上的选品时间,帮助卖家更好地了解亚马逊上各个产品的定价、排名、评论和销售额。 你可以选择Unicorn Smasher的理由: • 简单、易操作的仪表盘界面,助力完成选品数据抓取。 • 根据亚马逊listing中的实时数据,获得每月的预估销售额。 • 保存商家或可节省511美元 8、Keepa (图片来源:Keepa) Keepa也是一个浏览器插件,也适用于其它所有主流浏览器。只需安装该插件,所有功能随即可全部免费使用。 你可以选择Keepa的理由: 一个免费的亚马逊产品搜索工具,具有深度数据筛选功能。 显示降价和可用性提醒的价格历史图表。 可在亚马逊上比较不同地区的价格。 可以依据价格高点下跌查询任一品类的近期交易。 可通过通知和愿望列表来进行数据跟踪。 9、ASINspector (图片来源:ASINspector) ASINspector是一个免费的谷歌插件,助力商家成为亚马逊上的专业人士。该工具不仅可以抓取利好产品信息,还能让商家以低价拿下供应商,从而获得较大利润。 你可以选择ASINspector的理由: 可提供预估销售和实时利润情况等数据。 使用AccuSales™数据分析引擎可节省选品时间。 挖掘利好产品想法,并可以红色、绿色和黄色进行标记。 用利润计算器查看决定产品是否存在合理利润空间。 与任一国家的任一亚马逊平台无缝衔接。 10、AMZScout AMZScout是卖家常用的亚马逊工具之一。 你可以选择AMZScout的理由: 访问产品数据库,查找热门新产品。 通过AMZSscout提供的培训课程提高销售技巧。 在任何国家/地区搜索国际供应商并以建立自己的品牌。 监控竞争对手的关键字、销售、定价等。 只需点击3次即可轻松安装,有中文版。 黑五福利:三五折优惠获完整工具集合,可节省511美元【点击此处】 11、 PickFu PickFu是一款亚马逊A/B测试工具,也是一个可以获取消费者问卷调查的平台。 你可以选择PickFu的理由: • 真实的美国消费者反馈 • 几分钟即可在线完成问卷调研 • 商品设计、图片、描述等及时反馈 • 精准的目标群众和属性划分 • 中文客服支持 【点击此处】获取网一福利:预购积分享8折 这11大效率型亚马逊工具已介绍完毕,相信你已经有了心仪的选择了!快去实践一下,试试看吧! (来源:AMZ实战) 以上内容仅代表作者本人观点,不代表LIKE.TG立场!如有关于作品内容、版权或其它问题请于作品发表后的30日内与LIKE.TG取得联系。 *上述文章存在营销推广内容(广告)本文转载自:https://www.cifnews.com/search/article?keyword=工具
全球峰会
1-4月美国电商支出3316亿美元,消费者转向低价商品
1-4月美国电商支出3316亿美元,消费者转向低价商品
AMZ123 获悉,日前,据外媒报道,Adobe Analytics 的数据显示,2024 年前四个月美国电商增长强劲,同比增长 7%,达到 3316 亿美元。据了解,Adobe Analytics 对美国在线交易数据进行了分析,涵盖美国零售网站的一万亿次访问、1 亿个 SKU 和 18 个产品类别。2024 年 1 月 1 日至 4 月 30 日,美国在线支出达 3316 亿美元,同比增长 7%,得益于电子产品、服装等非必需品的稳定支出以及在线杂货购物的持续激增。Adobe 预计,2024 年上半年在线支出将超过 5000 亿美元,同比增长 6.8%。今年前四个月,美国消费者在线上消费电子产品 618 亿美元(同比增长 3.1%),服装 525 亿美元(同比增长 2.6%)。尽管增幅较小,但这两个类别占电商总支出的 34.5%,帮助保持了营收增长。同时,杂货进一步推动了增长,在线支出达 388 亿美元,同比增长 15.7%。Adobe 预计,未来三年内,该类别将成为电商市场的主导力量,其收入份额与电子产品和服装相当。另一个在线支出费增长较快的类别是化妆品,该类别在 2023 年带来了 350 亿美元的在线消费,同比增长 15.6%。而这一上升趋势仍在继续,截至 4 月 30 日,2024 年美国消费者在化妆品上的在线支出为 132 亿美元,同比增长 8%。此外,数月持续的通货膨胀导致消费者在多个主要类别中购买更便宜的商品。Adobe 发现,个人护理(增长 96%)、电子产品(增长 64%)、服装(增长 47%)、家居/花园(增长 42%)、家具/床上用品(增长 42%)和杂货(增长 33%)等类别的低价商品份额均大幅增加。具体而言,在食品杂货等类别中,低通胀商品的收入增长 13.4%,而高通胀商品的收入下降 15.6%。在化妆品等类别中,影响相对较弱,低通胀商品的收入增长 3.06%,高通胀商品的收入仅下降 0.34%,主要由于消费者对自己喜欢的品牌表现出了更强的忠诚度。而体育用品(增长 28%)、家电(增长 26%)、工具/家装(增长 26%)和玩具(增长 25%)等类别的低价商品份额增幅均较小,这些类别的增幅也主要受品牌忠诚度影响,同时消费者更倾向于购买最高品质的此类产品。此外,“先买后付”(BNPL)支付方式在此期间也出现了持续增长。2024 年 1 月至 4 月,BNPL 推动了 259 亿美元的电商支出,较去年同期大幅增长 11.8%。Adobe 预计,BNPL 将在 2024 年全年推动 810 亿至 848 亿美元的支出,同比增长 8% 至 13%。
12月波兰社媒平台流量盘点,TikTok追赶Instagram
12月波兰社媒平台流量盘点,TikTok追赶Instagram
AMZ123 获悉,近日,市场分析机构 Mediapanel 公布了 2023 年 12 月波兰主流社交平台的最新用户统计数据。受 TikTok 的打击,Pinterest、Facebook 和 Instagram 的用户数量出现下降。根据 Mediapanel 的数据,截至 2023 年 12 月,TikTok 是波兰第三大社交媒体平台,拥有超过 1378 万用户,相当于波兰 46.45% 的互联网用户。排在 TikTok 之前的是 Facebook 和 Instagram,其中 Facebook 拥有超过 2435 万用户,相当于波兰 82.06% 的互联网用户;Instagram 则拥有超过 1409 万用户,相当于波兰 47.47% 的互联网用户。在用户使用时长方面,TikTok 排名第一。2023 年 12 月,TikTok 用户的平均使用时长为 17 小时 18 分钟 42 秒。Facebook 用户的平均使用时长为 15 小时 36 分钟 38 秒,位居第二。其次是 Instagram,平均使用时长为 5 小时 2 分钟 39 秒。与 11 月相比,12 月 Facebook 减少了 58.84 万用户(下降 2.4%),但其用户平均使用时间增加了 32 分钟 50 秒(增长 3.6%)。Instagram 流失了 25.9 万用户(下降 1.8%),但其用户平均使用时间增加了 15 分钟(增长 5.2%)。虽然 TikTok 的用户数量略有增长(增长 8.85 万,即 0.6%),但其用户平均使用时间减少了 47 分钟(减少 4.3%)。12 月份,波兰其他主流社交媒体平台的用户数据(与 11 月相比):X 增加了 39.64 万用户(增长 4.8%),用户平均使用时间增加了 6 分钟 19 秒(增长 9.3%);Pinterest 增加了 23.02 万用户(增长 3.5%),用户平均使用时间增加了 7 分钟 9 秒(增长 16.1%);Snapchat 则增加了 9.04 万用户(增长 1.8%),用户平均使用时间增加了 23 秒(增长 0.2%);LinkedIn 流失了 27.69 万用户(下降 6.2%),用户平均使用时间减少了 1 分钟 36 秒(下降 11.7%);Reddit 流失了 18.6 万用户(下降 7.1%),用户平均使用时间减少了 1 分钟 27 秒(下降 11.6%)。
178W应用、3700W注册开发者,图表详解苹果首个App Store数据透明度报告
178W应用、3700W注册开发者,图表详解苹果首个App Store数据透明度报告
近日,苹果发布 2022 年 App Store 透明度报告,展示了 App Store 在 175 个国家和地区运营的数据,包括在线/下架应用数量、提审被拒应用数量、每周访问量、搜索量等。为帮助开发者快速了解 App Store 新发布的各项数据情况,在本篇内容中,AppStare 拆解了各项数据,为开发者提供直观展示,可供参考。app 数据App Store 在线及下架 app 数量报告显示,2022 年,App Store 中在线 app 总数量超 178 万(1,783,232),从 App Store 下架的 app 数量超 18 万(186,195)。提交审核及被拒的 app 数量共有超 610 万(6,101,913)款 app 提交到 App Store 进行审核,其中近 168 万(1,679,694)款 app 提审被拒,占比 27.53%,审核拒绝的主要原因包括性能问题、违反当地法律、不符合设计规范等。此外,提审被拒后再次提交并通过审核的 app 数量超 25 万(253,466),占比 15.09%。不同原因提审被拒的 app 数量app 提审被 App Store 审核指南拒绝的原因包括 app 性能问题、违反当地法律、不符合设计规范、业务问题、存在安全风险及其他六大模块。从上图可见,性能问题是 app 提审被拒的最大原因,超 101 万(1,018,415)款 app 因此被 App Store 审核指南拒绝,占比达 50.98%。建议开发者在 app 提审前,针对 App Store 审核指南再做详细的自我审查,提升通过可能。从 App Store 下架的 app Top 10 分类2022 年,App Store 下架超 18 万(186,195)款 app,其中游戏类 app 是下架次数最多的应用类别,超 3.8 万(38,883)款,占比 20.88%,其次为 工具类 app,共下架 2 万(20,045)款,占比 10.77%。中国大陆下架 app 品类 top 10在中国大陆地区,下架 app 总计超 4 万(41,238)款。工具类 app 是下架数量最多的 app 子品类,达 9,077 款,占比 22.01%,其次为游戏类 app,下架 6,173 款,占比 14.97%。被下架后申诉的 app 数量在 175 个国家/地区中,被下架后申诉的 app 数量总计超 1.8 万(18,412)款。中国大陆下架后申诉的 app 数量最多,达 5,484 款,占比 29.78%。申诉后恢复上架的 app 数量申诉后恢复上架的 app 数量总计为 616 款,其中中国大陆申诉后恢复上架的 app 最多,为 169 款,占中国大陆下架后申诉 app 数量(5,484)的 3.08%。开发者数据注册苹果开发者总数近 3700 万(36,974,015),被终止开发者账户数量近 43 万(428,487),占比 1.16%。其中,开发者账户因违反开发者计划许可协议(DPLA)而被终止的主要原因分别有欺诈(428,249)、出口管制(238)等。被终止后申诉的开发者账户数量为 3,338,被终止后申诉并恢复的开发者账户数量为 159,占比 4.76%。用户数据在用户方面,平均每周访问 App Store 的用户数超 6.56 亿(656,739,889)。2022 年,App Store 终止用户账户数量超 2.82 亿(282,036,628)。值得注意的是,App Store 还阻止了金额超 $20.9亿($2,090,195,480)的欺诈交易。在用户 app 下载方面,平均每周下载 app 数量超 7.47 亿(747,873,877),平均每周重新下载 app 数量超 15.39 亿(1,539,274,266),是前者的 2 倍。因此,建议开发者多加重视对回访用户的唤醒,相关推广策略的制定可能起到较为理想的效果。在 app 更新方面,平均每周自动更新 app 数量超 408 亿(40,876,789,492),平均每周手动更新 app 数量超 5 亿(512,545,816)。可见,用户在 app 更新问题上更偏向依赖自动更新。搜索数据平均每周在 App Store 搜索的用户数超 3.73 亿(373,211,396),App Store 的高质流量有目共睹。在至少 1000 次搜索中出现在搜索结果前 10 名的 app 总数近 140 万(1,399,741),平均每周出现在至少 1000 次搜索结果前 10 名的 app 数量 近 20 万(197,430)。除了通过元数据优化等操作提升 app 的搜索排名外,Apple Search Ads 也是帮助开发者提升 app 曝光和下载的重要渠道。
全球大数据
   探索Discord注册的多重用途
探索Discord注册的多重用途
在当今数字化时代,社交网络平台是人们沟通、分享和互动的重要场所。而Discord作为一款功能强大的聊天和社交平台,正吸引着越来越多的用户。那么,Discord注册可以用来做什么呢?让我们来探索它的多重用途。 首先,通过Discord注册,您可以加入各种兴趣群组和社区,与志同道合的人分享共同的爱好和话题。不论是游戏、音乐、电影还是科技,Discord上有无数个群组等待着您的加入。您可以与其他成员交流、参与讨论、组织活动,结识新朋友并扩大自己的社交圈子。 其次,Discord注册也为个人用户和团队提供了一个协作和沟通的平台。无论您是在学校、工作场所还是志愿组织,Discord的群组和频道功能使得团队成员之间可以方便地分享文件、讨论项目、安排日程,并保持密切的联系。它的语音和视频通话功能还能让远程团队更好地协同工作,提高效率。 对于商业用途而言,Discord注册同样具有巨大潜力。许多品牌和企业已经认识到了Discord作为一个与年轻受众互动的渠道的重要性。通过创建自己的Discord服务器,您可以与客户和粉丝建立更紧密的联系,提供独家内容、产品促销和用户支持。Discord还提供了一些商业工具,如机器人和API,帮助您扩展功能并提供更好的用户体验。 总结起来,Discord注册不仅可以让您加入各种兴趣群组和社区,享受与志同道合的人交流的乐趣,还可以为个人用户和团队提供协作和沟通的平台。对于品牌和企业而言,Discord也提供了与受众互动、推广产品和提供用户支持的机会。所以,赶紧注册一个Discord账号吧,开启多重社交和商业可能性的大门! -->
  商海客discord群发软件:开启营销革命的利器
商海客discord群发软件
开启营销革命的利器
商海客discord群发软件作为一款前沿的营销工具,以其独特的特点和出色的功能,在商业领域掀起了一场营销革命。它不仅为企业带来了全新的营销方式,也为企业创造了巨大的商业价值。 首先,商海客discord群发软件以其高效的群发功能,打破了传统营销方式的束缚。传统营销常常面临信息传递效率低、覆盖范围有限的问题。而商海客discord群发软件通过其强大的群发功能,可以将信息迅速传递给大量的目标受众,实现广告的精准推送。不论是产品推广、品牌宣传还是促销活动,商海客discord群发软件都能帮助企业快速触达潜在客户,提高营销效果。 其次,商海客discord群发软件提供了丰富的营销工具和功能,为企业的营销活动增添了更多的可能性。商海客discord群发软件支持多种媒体形式的推送,包括文本、图片、音频和视频等。企业可以根据自身需求,定制个性化的消息内容和推广方案,以吸引目标受众的注意。此外,商海客discord群发软件还提供了数据分析和统计功能,帮助企业了解营销效果,进行精细化的调整和优化。 最后,商海客discord群发软件的用户体验和易用性也为企业带来了便利。商海客discord群发软件的界面简洁明了,操作简单易懂,即使对于非技术人员也能够快速上手。商海客discord群发软件还提供了稳定的技术支持和优质的客户服务,确保用户在使用过程中能够获得及时的帮助和解决问题。 -->
 Discord|海外社媒营销的下一个风口?
Discord|海外社媒营销的下一个风口?
Discord这个软件相信打游戏的各位多少都会有点了解。作为功能上和YY相类似的语音软件,已经逐渐成为各类游戏玩家的青睐。在这里你可以创建属于自己的频道,叫上三五个朋友一起开黑,体验线上五连坐的游戏体验。但Discord可不是我们口中说的美国版YY这么简单。 Discord最初是为了方便人们交流而创立的应用程序。游戏玩家、电影迷和美剧迷、包括NFT创作者和区块链项目都在Discord上装修起一个个属于自己的小家。而在互联网的不断发展中,Discord现如今已经发展成为一种高效的营销工具,其强大的社区的功能已远不止语音交谈这一单一功能了。本文我们将结合市场营销现有的一些概念,带你领略Discord背后的无穷价值。 初代海外社媒营销: 当我们谈及Marketing市场营销,我们大多能想到的就是广告,以广告投放去获得较为多的转化为最终目的。但随着公众利益的变化,市场营销的策略也在不断改变。社交媒体类别的营销是现在更多品牌更为看重的一块流量池。我们可以选择付费营销,当然也可以选择不付费,这正式大多数的品牌所处的阶段。如国内的微博,抖音。又好比海外的Facebook, Instagram等。 但是,当我们深入地了解这些社交媒体的算法时不难发现。人们经常会错过我们的内容,又或者在看到这是一个广告之后就选择离开,其推广的触达率并不显著。其原因其实和初代社交媒体的属性分不开。 我们来打个比方:当你在YouTube上看着喜爱的博主视频,YouTube突然暂停了你的视频,给你插入了品牌方的广告。试问你的心情如何?你会选择安心看完这个广告,对其推广的产品产生了兴趣。还是想尽一切办法去关掉这个烦人的广告?而在不付费的内容上:你更喜欢看那些能娱乐你,充实你生活的内容。还是选择去看一个可能和你毫不相干的品牌贴文?在大数据的加持下,品牌方可能绞尽脑汁的想去获得你这个用户。但选择权仍就在用户手上,用户选择社交媒体的原因更多是为了娱乐和社交。我们也不愿意和一个个客气的“品牌Logo”去对话。 Discord是如何改变营销世界的? Discord又有什么不一样呢?你觉的他的营销手段就像发Email一样,给你特定的社群发送一组消息?谈到Email,这里要插一嘴。其触达率表现也并不优异,你发送的重要通告,新闻稿,打折促销。都有可能在用户还未浏览收之前就已经进了垃圾箱,又或者是和其他数百封未读邮件中等待着缘分的到来。 其实Discord的频道属性很美妙的化解了社交媒体现在的窘境,我们再来打个比方:比如你很喜欢篮球,因此你进入到了这个Discord篮球频道。而在这个频道里又包含了中锋,前锋,后卫这些细分频道。后卫又细分到了控球后卫,得分后卫。但总的来说,这个频道的用户都是喜欢篮球的群体。Discord的属性也拉近了品牌和用户的距离,你们不再是用户和一个个官方的“品牌Logo”对话。取而代之的则是一个个亲近感十足的好兄弟。直播带货中的“家人们”好像就是这一形式哈哈。 因此在Discord 上你可以针对不同频道发送不同的公告消息,使目标用户能够及时获得你的任何更新。他可不像电子邮件一样,淹没在一堆未读邮件中,也不会像社媒贴文一样被忽视。更精准的去区分不同的目标受众这一独特性也注定了Discord Marketing的强大功能。 Discord拓展属性: 自Facebook更名Meta等一系列动作下,2021年被世人称为元宇宙元年。在这一大背景下,更多的社交媒体开始逐渐向元宇宙靠拢。Twitter逐渐成为各类项目方的首选宣发媒体。Discord的属性也被更多项目方所发现,现如今Discord已被广泛运用在区块链领域。Discord事实上已经成为加密货币社区的最大聚集地,学习使用Discord也已经成为了圈内最入门技能。随着未来大量的区块链项目的上线Discord也将获得更加直接的变现手段。 Discord的各类载体已经数不胜数,区块链、游戏开黑、公司办公软件、线上教课。Discord是否能成为海外社媒的下一个风口?还是他已经成为了?这个不是我们能说了算的,但甭管你是想做品牌推广,还是单纯的就想酣畅漓淋的和朋友一起开个黑。选择Discord都是一个不错的选择。 -->
社交媒体

                    100+ Instagram Stats You Need to Know in 2024
100+ Instagram Stats You Need to Know in 2024
It feels like Instagram, more than any other social media platform, is evolving at a dizzying pace. It can take a lot of work to keep up as it continues to roll out new features, updates, and algorithm changes. That‘s where the Instagram stats come in. There’s a lot of research about Instagram — everything from its users' demographics, brand adoption stats, and all the difference between micro and nano influencers. I use this data to inform my marketing strategies and benchmark my efforts. Read on to uncover more social media stats to help you get ideas and improve your Instagram posting strategy. 80+ Instagram Stats Click on a category below to jump to the stats for that category: Instagram's Growth Instagram User Demographics Brand Adoption Instagram Post Content Instagram Posting Strategy Instagram Influencer Marketing Statistics Instagram's Growth Usage 1. Instagram is expected to reach 1.44 billion users by 2025. (Statista) 2. The Instagram app currently has over 1.4 billion monthly active users. (Statista) 3. U.S. adults spend an average of 33.1 minutes per day on Instagram in 2024, a 3-minute increase from the year before. (Sprout Social) 4. Instagram ad revenue is anticipated to reach $59.61 billion in 2024. (Oberlo) 5. Instagram’s Threads has over 15 Million monthly active users. (eMarketer) 6. 53.7% of marketers plan to use Instagram reels for influencer marketing in 2024. (eMarketer) 7. 71% of marketers say Instagram is the platform they want to learn about most. (Skillademia) 8. There are an estimated 158.4 million Instagram users in the United States in 2024. (DemandSage) 9. As of January 2024, India has 362.9 million Instagram users, the largest Instagram audience in the world. (Statista) 10. As of January 2024, Instagram is the fourth most popular social media platform globally based on monthly active users. Facebook is first. YouTube and WhatsApp rank second and third. (Statista) https://youtu.be/EyHV8aZFWqg 11. Over 400 million Instagram users use the Stories feature daily. (Keyhole) 12. As of April 2024, the most-liked post on Instagram remains a carousel of Argentine footballer Lionel Messi and his teammates celebrating the 2022 FIFA World Cup win. (FIFA) 13. The fastest-growing content creator on Instagram in 2024 is influencer Danchmerk, who grew from 16k to 1.6 Million followers in 8 months. (Instagram) 14. The most-followed Instagram account as of March 2024 is professional soccer player Cristiano Ronaldo, with 672 million followers. (Forbes) 15. As of April 2024, Instagram’s own account has 627 million followers. (Instagram) Instagram User Demographics 16. Over half of the global Instagram population is 34 or younger. (Statista) 17. As of January 2024, almost 17% of global active Instagram users were men between 18 and 24. (Statista) 18. Instagram’s largest demographics are Millennials and Gen Z, comprising 61.8% of users in 2024. (MixBloom) 19. Instagram is Gen Z’s second most popular social media platform, with 75% of respondents claiming usage of the platform, after YouTube at 80%. (Later) 20. 37.74% of the world’s 5.3 billion active internet users regularly access Instagram. (Backlinko) 21. In January 2024, 55% of Instagram users in the United States were women, and 44% were men. (Statista) 22. Only 7% of Instagram users in the U.S. belong to the 13 to 17-year age group. (Statista) 23. Only 5.7% of Instagram users in the U.S. are 65+ as of 2024. (Statista) 24. Only 0.2% of Instagram users are unique to the platform. Most use Instagram alongside Facebook (80.8%), YouTube (77.4%), and TikTok (52.8%). (Sprout Social) 25. Instagram users lean slightly into higher tax brackets, with 47% claiming household income over $75,000. (Hootsuite) 26. Instagram users worldwide on Android devices spend an average of 29.7 minutes per day (14 hours 50 minutes per month) on the app. (Backlinko) 27. 73% of U.S. teens say Instagram is the best way for brands to reach them. (eMarketer) 28. 500 million+ accounts use Instagram Stories every day. (Facebook) 29. 35% of music listeners in the U.S. who follow artists on Facebook and Instagram do so to connect with other fans or feel like part of a community. (Facebook) 30. The average Instagram user spends 33 minutes a day on the app. (Oberlo) 31. 45% of people in urban areas use Instagram, while only 25% of people in rural areas use the app. (Backlinko) 32. Approximately 85% of Instagram’s user base is under the age of 45. (Statista) 33. As of January 2024, the largest age group on Instagram is 18-24 at 32%, followed by 30.6% between ages 25-34. (Statista) 34. Globally, the platform is nearly split down the middle in terms of gender, with 51.8% male and 48.2% female users. (Phyllo) 35. The numbers differ slightly in the U.S., with 56% of users aged 13+ being female and 44% male. (Backlinko) 36. As of January 2024, Instagram is most prevalent in India, with 358.55 million users, followed by the United States (158.45 million), Brazil (122.9 million), Indonesia (104.8 million), and Turkey (56.7 million). (Backlinko) 37. 49% of Instagram users are college graduates. (Hootsuite) 38. Over 1.628 Billion Instagram users are reachable via advertising. (DataReportal) 39. As of January 2024, 20.3% of people on Earth use Instagram. (DataReportal) Brand Adoption 40. Instagram is the top platform for influencer marketing, with 80.8% of marketers planning to use it in 2024. (Sprout Social) 41. 29% of marketers plan to invest the most in Instagram out of any social media platform in 2023. (Statista) 42. Regarding brand safety, 86% of marketers feel comfortable advertising on Instagram. (Upbeat Agency) 43. 24% of marketers plan to invest in Instagram, the most out of all social media platforms, in 2024. (LIKE.TG) 44. 70% of shopping enthusiasts turn to Instagram for product discovery. (Omnicore Agency) 45. Marketers saw the highest engagement rates on Instagram from any other platform in 2024. (Hootsuite) 46. 29% of marketers say Instagram is the easiest platform for working with influencers and creators. (Statista) 47. 68% of marketers reported that Instagram generates high levels of ROI. (LIKE.TG) 48. 21% of marketers reported that Instagram yielded the most significant ROI in 2024. (LIKE.TG) 49. 52% of marketers plan to increase their investment in Instagram in 2024. (LIKE.TG) 50. In 2024, 42% of marketers felt “very comfortable” advertising on Instagram, and 40% responded “somewhat comfortable.” (LIKE.TG) 51. Only 6% of marketers plan to decrease their investment in Instagram in 2024. (LIKE.TG) 52. 39% of marketers plan to leverage Instagram for the first time in 2024. (LIKE.TG) 53. 90% of people on Instagram follow at least one business. (Instagram) 54. 50% of Instagram users are more interested in a brand when they see ads for it on Instagram. (Instagram) 55. 18% of marketers believe that Instagram has the highest growth potential of all social apps in 2024. (LIKE.TG) 56. 1 in 4 marketers say Instagram provides the highest quality leads from any social media platform. (LIKE.TG) 57. Nearly a quarter of marketers (23%) say that Instagram results in the highest engagement levels for their brand compared to other platforms. (LIKE.TG) 58. 46% of marketers leverage Instagram Shops. Of the marketers who leverage Instagram Shops, 50% report high ROI. (LIKE.TG) 59. 41% of marketers leverage Instagram Live Shopping. Of the marketers who leverage Instagram Live Shopping, 51% report high ROI. (LIKE.TG) 60. Education and Health and Wellness industries experience the highest engagement rates. (Hootsuite) 61. 67% of users surveyed have “swiped up” on the links of branded Stories. (LIKE.TG) 62. 130 million Instagram accounts tap on a shopping post to learn more about products every month. (Omnicore Agency) Instagram Post Content 63. Engagement for static photos has decreased by 44% since 2019, when Reels debuted. (Later) 64. The average engagement rate for photo posts is .059%. (Social Pilot) 65. The average engagement rate for carousel posts is 1.26% (Social Pilot) 66. The average engagement rate for Reel posts is 1.23% (Social Pilot) 67. Marketers rank Instagram as the platform with the best in-app search capabilities. (LIKE.TG) 68. The most popular Instagram Reel is from Samsung and has over 1 billion views. (Lifestyle Asia) 69. Marketers rank Instagram as the platform with the most accurate algorithm, followed by Facebook. (LIKE.TG) 70. A third of marketers say Instagram offers the most significant ROI when selling products directly within the app. (LIKE.TG) 71. Instagram Reels with the highest engagement rates come from accounts with fewer than 5000 followers, with an average engagement rate of 3.79%. (Social Pilot) 72. A third of marketers say Instagram offers the best tools for selling products directly within the app. (LIKE.TG) 73. Over 100 million people watch Instagram Live every day. (Social Pilot) 74. 70% of users watch Instagram stories daily. (Social Pilot) 75. 50% of people prefer funny Instagram content, followed by creative and informative posts. (Statista) 76. Instagram Reels are the most popular post format for sharing via DMs. (Instagram) 77. 40% of Instagram users post stories daily. (Social Pilot) 78. An average image on Instagram gets 23% more engagement than one published on Facebook. (Business of Apps) 79. The most geo-tagged city in the world is Los Angeles, California, and the tagged location with the highest engagement is Coachella, California. (LIKE.TG) Instagram Posting Strategy 80. The best time to post on Instagram is between 7 a.m. and 9 a.m. on weekdays. (Social Pilot) 81. Posts with a tagged location result in 79% higher engagement than posts without a tagged location. (Social Pilot) 82. 20% of users surveyed post to Instagram Stories on their business account more than once a week. (LIKE.TG) 83. 44% of users surveyed use Instagram Stories to promote products or services. (LIKE.TG) 84. One-third of the most viewed Stories come from businesses. (LIKE.TG) 85. More than 25 million businesses use Instagram to reach and engage with audiences. (Omnicore Agency) 86. 69% of U.S. marketers plan to spend most of their influencer budget on Instagram. (Omnicore Agency) 87. The industry that had the highest cooperation efficiency with Instagram influencers was healthcare, where influencer posts were 4.2x more efficient than brand posts. (Emplifi) 88. Instagram is now the most popular social platform for following brands. (Marketing Charts) Instagram Influencer Marketing Statistics 89. Instagram is the top platform for influencer marketing, with 80.8% of marketers planning to use the platform for such purposes in 2024 (Oberlo) 90. Nano-influencers (1,000 to 10,000 followers) comprise most of Instagram’s influencer population, at 65.4%. (Statista) 91. Micro-influencers (10,000 to 50,000 followers) account for 27.73% (Socially Powerful) 92. Mid-tier influencers (50,000 to 500,000 followers) account for 6.38% (Socially Powerful) 93. Nano-influencers (1,000 to 10,000 followers) have the highest engagement rate at 5.6% (EmbedSocial) 94. Mega-influencers and celebrities with more than 1 million followers account for 0.23%. (EmbedSocial) 95. 77% of Instagram influencers are women. (WPBeginner) 96. 30% of markers say that Instagram is their top channel for ROI in influencer marketing (Socially Powerful) 97. 25% of sponsored posts on Instagram are related to fashion (Socially Powerful) 98. The size of the Instagram influencer marketing industry is expected to reach $22.2 billion by 2025. (Socially Powerful) 99. On average, Instagram influencers charge $418 for a sponsored post in 2024, approximately 15.17%​​​​​​​ higher than in 2023. (Collabstr) 100. Nano-influencers charge between $10-$100 per Instagram post. (ClearVoice) 101. Celebrities and macro influencers charge anywhere from $10,000 to over $1 million for a single Instagram post in 2024. (Shopify) 102. Brands can expect to earn $4.12 of earned media value for each $1 spent on Instagram influencer marketing. (Shopify) The landscape of Instagram is vast and ever-expanding. However, understanding these key statistics will ensure your Instagram strategy is well-guided and your marketing dollars are allocated for maximum ROI. There’s more than just Instagram out there, of course. So, download the free guide below for the latest Instagram and Social Media trends.

                    130 Instagram Influencers You Need To Know About in 2022
130 Instagram Influencers You Need To Know About in 2022
In 2021, marketers that used influencer marketing said the trend resulted in the highest ROI. In fact, marketers have seen such success from influencer marketing that 86% plan to continue investing the same amount or increase their investments in the trend in 2022. But, if you’ve never used an influencer before, the task can seem daunting — who’s truly the best advocate for your brand? Here, we’ve cultivated a list of the most popular influencers in every industry — just click on one of the links below and take a look at the top influencers that can help you take your business to the next level: Top Food Influencers on Instagram Top Travel Influencers on Instagram Top Fashion Style Influencers on Instagram Top Photography Influencers on Instagram Top Lifestyle Influencers on Instagram Top Design Influencers on Instagram Top Beauty Influencers on Instagram Top Sport Fitness Influencers on Instagram Top Influencers on Instagram Top Food Influencers on Instagram Jamie Oliver (9.1M followers) ladyironchef (620k followers) Megan Gilmore (188k followers) Ashrod (104k followers) David Chang (1.7M followers) Ida Frosk (299k followers) Lindsey Silverman Love (101k followers) Nick N. (60.5k followers) Molly Tavoletti (50.1k followers) Russ Crandall (39.1k followers) Dennis the Prescott (616k followers) The Pasta Queen (1.5M followers) Thalia Ho (121k followers) Molly Yeh (810k followers) C.R Tan (59.4k followers) Michaela Vais (1.2M followers) Nicole Cogan (212k followers) Minimalist Baker (2.1M followers) Yumna Jawad (3.4M followers) Top Travel Influencers on Instagram Annette White (100k followers) Matthew Karsten (140k followers) The Points Guy (668k followers) The Blonde Abroad (520k followers) Eric Stoen (330k followers) Kate McCulley (99k followers) The Planet D (203k followers) Andrew Evans (59.9k followers) Jack Morris (2.6M followers) Lauren Bullen (2.1M followers) The Bucket List Family (2.6M followers) Fat Girls Traveling (55K followers) Tara Milk Tea (1.3M followers) Top Fashion Style Influencers on Instagram Alexa Chung (5.2M followers) Julia Berolzheimer (1.3M followers) Johnny Cirillo (719K followers) Chiara Ferragni (27.2M followers) Jenn Im (1.7M followers) Ada Oguntodu (65.1k followers) Emma Hill (826k followers) Gregory DelliCarpini Jr. (141k followers) Nicolette Mason (216k followers) Majawyh (382k followers) Garance Doré (693k followers) Ines de la Fressange (477k followers) Madelynn Furlong (202k followers) Giovanna Engelbert (1.4M followers) Mariano Di Vaio (6.8M followers) Aimee Song (6.5M followers) Danielle Bernstein (2.9M followers) Gabi Gregg (910k followers) Top Photography Influencers on Instagram Benjamin Lowy (218k followers) Michael Yamashita (1.8M followers) Stacy Kranitz (101k followers) Jimmy Chin (3.2M followers) Gueorgui Pinkhassov (161k followers) Dustin Giallanza (5.2k followers) Lindsey Childs (31.4k followers) Edith W. Young (24.9k followers) Alyssa Rose (9.6k followers) Donjay (106k followers) Jeff Rose (80.1k followers) Pei Ketron (728k followers) Paul Nicklen (7.3M followers) Jack Harries (1.3M followers) İlhan Eroğlu (852k followers) Top Lifestyle Influencers on Instagram Jannid Olsson Delér (1.2 million followers) Oliver Proudlock (691k followers) Jeremy Jacobowitz (434k followers) Jay Caesar (327k followers) Jessie Chanes (329k followers) Laura Noltemeyer (251k followers) Adorian Deck (44.9k followers) Hind Deer (547k followers) Gloria Morales (146k followers) Kennedy Cymone (1.6M followers) Sydney Leroux Dwyer (1.1M followers) Joanna Stevens Gaines (13.6M followers) Lilly Singh (11.6M followers) Rosanna Pansino (4.4M followers) Top Design Influencers on Instagram Marie Kondo (4M followers) Ashley Stark Kenner (1.2M followers) Casa Chicks (275k followers) Paulina Jamborowicz (195k followers) Kasia Będzińska (218k followers) Jenni Kayne (500k followers) Will Taylor (344k followers) Studio McGee (3.3M followers) Mandi Gubler (207k followers) Natalie Myers (51.6k followers) Grace Bonney (840k followers) Saudah Saleem (25.3k followers) Niña Williams (196k followers) Top Beauty Influencers on Instagram Michelle Phan (1.9M followers) Shaaanxo (1.3M followers) Jeffree Star (13.7M followers) Kandee Johnson (2M followers) Manny Gutierrez (4M followers) Naomi Giannopoulos (6.2M followers) Samantha Ravndahl (2.1M followers) Huda Kattan (50.5M followers) Wayne Goss (703k followers) Zoe Sugg (9.3M followers) James Charles (22.9M followers) Shayla Mitchell (2.9M followers) Top Sport Fitness Influencers on Instagram Massy Arias (2.7M followers) Eddie Hall (3.3M followers) Ty Haney (92.6k followers) Hannah Bronfman (893k followers) Kenneth Gallarzo (331k followers) Elisabeth Akinwale (113k followers) Laura Large (75k followers) Akin Akman (82.3k followers) Sjana Elise Earp (1.4M followers) Cassey Ho (2.3M followers) Kayla Itsines (14.5M followers) Jen Selter (13.4M followers) Simeon Panda (8.1M followers) Top Instagram InfluencersJamie OliverDavid ChangJack Morris and Lauren BullenThe Bucket List FamilyChiara FerragniAlexa ChungJimmy ChinJannid Olsson DelérGrace BonneyHuda KattanZoe SuggSjana Elise EarpMassy Arias 1. Jamie Oliver Jamie Oliver, a world-renowned chef and restaurateur, is Instagram famous for his approachable and delicious-looking cuisine. His page reflects a mix of food pictures, recipes, and photos of his family and personal life. His love of beautiful food and teaching others to cook is clearly evident, which must be one of the many reasons why he has nearly seven million followers. 2. David Chang Celebrity chef David Chang is best known for his world-famous restaurants and big personality. Chang was a judge on Top Chef and created his own Netflix show called Ugly Delicious, both of which elevated his popularity and likely led to his huge followership on Instagram. Most of his feed is filled with food videos that will make you drool. View this post on Instagram 3. Jack Morris and Lauren Bullen Travel bloggers Jack Morris (@jackmorris) and Lauren Bullen (@gypsea_lust)have dream jobs -- the couple travels to some of the most beautiful places around the world and documents their trips on Instagram. They have developed a unique and recognizable Instagram aesthetic that their combined 4.8 million Instagram followers love, using the same few filters and posting the most striking travel destinations. View this post on Instagram 4. The Bucket List Family The Gee family, better known as the Bucket List Family, travel around the world with their three kids and post videos and images of their trips to YouTube and Instagram. They are constantly sharing pictures and stories of their adventures in exotic places. This nomad lifestyle is enjoyed by their 2.6 million followers. View this post on Instagram 5. Chiara Ferragni Chiara Ferragni is an Italian fashion influencer who started her blog The Blonde Salad to share tips, photos, and clothing lines. Ferragni has been recognized as one of the most influential people of her generation, listed on Forbes’ 30 Under 30 and the Bloglovin’ Award Blogger of the Year. 6. Alexa Chung Model and fashion designer Alexa Chung is Instagram famous for her elegant yet charming style and photos. After her modeling career, she collaborated with many brands like Mulberry and Madewell to create her own collection, making a name for herself in the fashion world. Today, she shares artistic yet fun photos with her 5.2 million Instagram followers. 7. Jimmy Chin Jimmy Chin is an award-winning professional photographer who captures high-intensity shots of climbing expeditions and natural panoramas. He has won multiple awards for his work, and his 3.2 million Instagram followers recognize him for his talent. 8. Jannid Olsson Delér Jannid Olsson Delér is a lifestyle and fashion blogger that gathered a huge social media following for her photos of outfits, vacations, and her overall aspirational life. Her 1.2 million followers look to her for travel and fashion inspirations. 9. Grace Bonney Design*Sponge is a design blog authored by Grace Bonney, an influencer recognized by the New York Times, Forbes, and other major publications for her impact on the creative community. Her Instagram posts reflect her elegant yet approachable creative advice, and nearly a million users follow her account for her bright and charismatic feed. 10. Huda Kattan Huda Kattan took the beauty world by storm -- her Instagram began with makeup tutorials and reviews and turned into a cosmetics empire. Huda now has 1.3 million Instagram followers and a company valued at $1.2 billion. Her homepage is filled with makeup videos and snaps of her luxury lifestyle. View this post on Instagram 11. Zoe Sugg Zoe Sugg runs a fashion, beauty, and lifestyle blog and has nearly 10 million followers on Instagram. She also has an incredibly successful YouTube channel and has written best-selling books on the experience of viral bloggers. Her feed consists mostly of food, her pug, selfies, and trendy outfits. View this post on Instagram 12. Sjana Elise Earp Sjana Elise Earp is a lifestyle influencer who keeps her Instagram feed full of beautiful photos of her travels. She actively promotes yoga and healthy living to her 1.4 million followers, becoming an advocate for an exercise program called SWEAT. 13. Massy Arias Personal trainer Massy Arias is known for her fitness videos and healthy lifestyle. Her feed aims to inspire her 2.6 million followers to keep training and never give up on their health. Arias has capitalized on fitness trends on Instagram and proven to both herself and her followers that exercise can improve all areas of your life. View this post on Instagram

                    24 Stunning Instagram Themes (& How to Borrow Them for Your Own Feed)
24 Stunning Instagram Themes (& How to Borrow Them for Your Own Feed)
Nowadays, Instagram is often someone's initial contact with a brand, and nearly half of its users shop on the platform each week. If it's the entryway for half of your potential sales, don't you want your profile to look clean and inviting? Taking the time to create an engaging Instagram feed aesthetic is one of the most effective ways to persuade someone to follow your business's Instagram account or peruse your posts. You only have one chance to make a good first impression — so it's critical that you put effort into your Instagram feed. Finding the perfect place to start is tough — where do you find inspiration? What color scheme should you use? How do you organize your posts so they look like a unit? We know you enjoy learning by example, so we've compiled the answers to all of these questions in a list of stunning Instagram themes. We hope these inspire your own feed's transformation. But beware, these feeds are so desirable, you'll have a hard time choosing just one. What is an Instagram theme?An instagram theme is a visual aesthetic created by individuals and brands to achieve a cohesive look on their Instagram feeds. Instagram themes help social media managers curate different types of content into a digital motif that brings a balanced feel to the profile. Tools to Create Your Own Instagram Theme Creating a theme on your own requires a keen eye for detail. When you’re editing several posts a week that follow the same theme, you’ll want to have a design tool handy to make that workflow easier. Pre-set filters, color palettes, and graphic elements are just a few of the features these tools use, but if you have a sophisticated theme to maintain, a few of these tools include advanced features like video editing and layout previews. Here are our top five favorite tools to use when editing photos for an Instagram theme. 1. VSCO Creators look to VSCO when they want to achieve the most unique photo edits. This app is one of the top-ranked photo editing tools among photographers because it includes advanced editing features without needing to pull out all the stops in Photoshop. If you’re in a hurry and want to create an Instagram theme quickly, use one of the 200+ VSCO presets including name-brand designs by Kodak, Agfa, and Ilford. If you’ll be including video as part of your content lineup on Instagram, you can use the same presets from the images so every square of content blends seamlessly into the next no matter what format it’s in. 2. FaceTune2 FaceTune2 is a powerful photo editing app that can be downloaded on the App Store or Google Play. The free version of the app includes all the basic editing features like brightness, lighting, cropping, and filters. The pro version gives you more detailed control over retouching and background editing. For video snippets, use FaceTune Video to make detailed adjustments right from your mobile device — you’ll just need to download the app separately for that capability. If you’re starting to test whether an Instagram theme is right for your brand, FaceTune2 is an affordable tool worth trying. 3. Canva You know Canva as a user-friendly and free option to create graphics, but it can be a powerful photo editing tool to curate your Instagram theme. For more abstract themes that mix imagery with graphic art, you can add shapes, textures, and text to your images. Using the photo editor, you can import your image and adjust the levels, add filters, and apply unique effects to give each piece of content a look that’s unique to your brand. 4. Adobe Illustrator Have you ever used Adobe Illustrator to create interesting overlays and tints for images? You can do the same thing to develop your Instagram theme. Traditionally, Adobe Illustrator is the go-to tool to create vectors and logos, but this software has some pretty handy features for creating photo filters and designs. Moreover, you can layout your artboards in an Instagram-style grid to see exactly how each image will appear in your feed. 5. Photoshop Photoshop is the most well-known photo editing software, and it works especially well for creating Instagram themes. If you have the capacity to pull out all the stops and tweak every detail, Photoshop will get the job done. Not only are the editing, filter, and adjustment options virtually limitless, Photoshop is great for batch processing the same edits across several images in a matter of seconds. You’ll also optimize your workflow by using photoshop to edit the composition, alter the background, and remove any unwanted components of an image without switching to another editing software to add your filter. With Photoshop, you have complete control over your theme which means you won’t have to worry about your profile looking exactly like someone else’s. Instagram ThemesTransitionBlack and WhiteBright ColorsMinimalistOne ColorTwo ColorsPastelsOne ThemePuzzleUnique AnglesText OnlyCheckerboardBlack or White BordersSame FilterFlatlaysVintageRepetitionMix-and-match Horizontal and Vertical BordersQuotesDark ColorsRainbowDoodleTextLinesAnglesHorizontal Lines 1. Transition If you aren’t set on one specific Instagram theme, consider the transition theme. With this aesthetic, you can experiment with merging colors every couple of images. For example, you could start with a black theme and include beige accents in every image. From there, gradually introduce the next color, in this case, blue. Eventually, you’ll find that your Instagram feed will seamlessly transition between the colors you choose which keeps things interesting without straying from a cohesive look and feel. 2. Black and White A polished black and white theme is a good choice to evoke a sense of sophistication. The lack of color draws you into the photo's main subject and suggests a timeless element to your business. @Lisedesmet's black and white feed, for instance, focuses the user’s gaze on the image's subject, like the black sneakers or white balloon. 3. Bright Colors If your company's brand is meant to imply playfulness or fun, there's probably no better way than to create a feed full of bright colors. Bright colors are attention-grabbing and lighthearted, which could be ideal for attracting a younger audience. @Aww.sam's feed, for instance, showcases someone who doesn't take herself too seriously. 4. Minimalist For an artsier edge, consider taking a minimalist approach to your feed, like @emwng does. The images are inviting and slightly whimsical in their simplicity, and cultivate feelings of serenity and stability. The pup pics only add wholesomeness to this minimalist theme. Plus, minimalist feeds are less distracting by nature, so it can be easier to get a true sense of the brand from the feed alone, without clicking on individual posts. 5. One Color One of the easiest ways to pick a theme for your feed is to choose one color and stick to it — this can help steer your creative direction, and looks clean and cohesive from afar. It's particularly appealing if you choose an aesthetically pleasing and calm color, like the soft pink used in the popular hashtag #blackwomeninpink. 6. Two Colors If you're interested in creating a highly cohesive feed but don't want to stick to the one-color theme, consider trying two. Two colors can help your feed look organized and clean — plus, if you choose branded colors, it can help you create cohesion between your other social media sites the website itself. I recommend choosing two contrasting colors for a punchy look like the one shown in @Dreaming_outloud’s profile. 7. Pastels Similar to the one-color idea, it might be useful to choose one color palette for your feed, like @creativekipi's use of pastels. Pastels, in particular, often used for Easter eggs or cupcake decorations, appear childlike and cheerful. Plus, they're captivating and unexpected. 8. One Subject As evident from @mustdoflorida's feed (and username), it's possible to focus your feed on one singular object or idea — like beach-related objects and activities in Florida. If you're aiming to showcase your creativity or photography skills, it could be compelling to create a feed where each post follows one theme. 9. Puzzle Creating a puzzle out of your feed is complicated and takes some planning, but can reap big rewards in terms of uniqueness and engaging an audience. @Juniperoats’ posts, for instance, make the most sense when you look at it from the feed, rather than individual posts. It's hard not to be both impressed and enthralled by the final result, and if you post puzzle piece pictures individually, you can evoke serious curiosity from your followers. 10. Unique Angles Displaying everyday items and activities from unexpected angles is sure to draw attention to your Instagram feed. Similar to the way lines create a theme, angles use direction to create interest. Taking an image of different subjects from similar angles can unite even the most uncommon photos into a consistent theme. 11. Text Only A picture is worth a thousand words, but how many pictures is a well-designed quote worth? Confident Woman Co. breaks the rules of Instagram that say images should have a face in them to get the best engagement. Not so with this Instagram theme. The bright colors and highlighted text make this layout aesthetically pleasing both in the Instagram grid format and as a one-off post on the feed. Even within this strict text-only theme, there’s still room to break up the monotony with a type-treated font and textured background like the last image does in the middle row. 12. Checkerboard If you're not a big fan of horizontal or vertical lines, you might try a checkerboard theme. Similar to horizontal lines, this theme allows you to alternate between content and images or colors as seen in @thefemalehustlers’ feed. 13. Black or White Borders While it is a bit jarring to have black or white borders outlining every image, it definitely sets your feed apart from everyone else's. @Beautifulandyummy, for instance, uses black borders to draw attention to her images, and the finished feed looks both polished and sophisticated. This theme will likely be more successful if you're aiming to sell fashion products or want to evoke an edgier feel for your brand. 14. Same Filter If you prefer uniformity, you'll probably like this Instagram theme, which focuses on using the same filter (or set of filters) for every post. From close up, this doesn't make much difference on your images, but from afar, it definitely makes the feed appear more cohesive. @marianna_hewitt, for example, is able to make her posts of hair, drinks, and fashion seem more refined and professional, simply by using the same filter for all her posts. 15. Flatlays If your primary goal with Instagram is to showcase your products, you might want a Flatlay theme. Flatlay is an effective way to tell a story simply by arranging objects in an image a certain way and makes it easier to direct viewers' attention to a product. As seen in @thedailyedited's feed, a flatlay theme looks fresh and modern. 16. Vintage If it aligns with your brand, vintage is a creative and striking aesthetic that looks both artsy and laid-back. And, while "vintage" might sound a little bit vague, it's easy to conjure. Simply try a filter like Slumber or Aden (built into Instagram), or play around with a third-party editing tool to find a soft, hazy filter that makes your photos look like they were taken from an old polaroid camera. 17. Repetition In @girleatworld's Instagram account, you can count on one thing to remain consistent throughout her feed: she's always holding up food in her hand. This type of repetition looks clean and engaging, and as a follower, it means I always recognize one of her posts as I'm scrolling through my own feed. Consider how you might evoke similar repetition in your own posts to create a brand image all your own. 18. Mix-and-match Horizontal and Vertical Borders While this admittedly requires some planning, the resulting feed is incredibly eye-catching and unique. Simply use the Preview app and choose two different white borders, Vela and Sole, to alternate between horizontal and vertical borders. The resulting feed will look spaced out and clean. 19. Quotes If you're a writer or content creator, you might consider creating an entire feed of quotes, like @thegoodquote feed, which showcases quotes on different mediums, ranging from paperback books to Tweets. Consider typing your quotes and changing up the color of the background, or handwriting your quotes and placing them near interesting objects like flowers or a coffee mug. 20. Dark Colors @JackHarding 's nature photos are nothing short of spectacular, and he highlights their beauty by filtering with a dark overtone. To do this, consider desaturating your content and using filters with cooler colors, like greens and blues, rather than warm ones. The resulting feed looks clean, sleek, and professional. 21. Rainbow One way to introduce color into your feed? Try creating a rainbow by slowly progressing your posts through the colors of the rainbow, starting at red and ending at purple (and then, starting all over again). The resulting feed is stunning. 22. Doodle Most people on Instagram stick to photos and filters, so to stand out, you might consider adding drawings or cartoon doodles on top of (or replacing) regular photo posts. This is a good idea if you're an artist or a web designer and want to draw attention to your artistic abilities — plus, it's sure to get a smile from your followers, like these adorable doodles shown below by @josie.doodles. 23. Content Elements Similar elements in your photos can create an enticing Instagram theme. In this example by The Container Store Custom Closets, the theme uses shelves or clothes in each image to visually bring the feed together. Rather than each photo appearing as a separate room, they all combine to create a smooth layout that displays The Container Store’s products in a way that feels natural to the viewer. 24. Structural Lines Something about this Instagram feed feels different, doesn’t it? Aside from the content focusing on skyscrapers, the lines of the buildings in each image turn this layout into a unique theme. If your brand isn’t in the business of building skyscrapers, you can still implement a theme like this by looking for straight or curved lines in the photos your capture. The key to creating crisp lines from the subjects in your photos is to snap them in great lighting and find symmetry in the image wherever possible. 25. Horizontal Lines If your brand does well with aligning photography with content, you might consider organizing your posts in a thoughtful way — for instance, creating either horizontal or vertical lines, with your rows alternating between colors, text, or even subject distance. @mariahb.makeup employs this tactic, and her feed looks clean and intriguing as a result. How to Create an Instagram Theme 1. Choose a consistent color palette. One major factor of any Instagram theme is consistency. For instance, you wouldn't want to regularly change your theme from black-and-white to rainbow — this could confuse your followers and damage your brand image. Of course, a complete company rebrand might require you to shift your Instagram strategy, but for the most part, you want to stay consistent with the types of visual content you post on Instagram. For this reason, you'll need to choose a color palette to adhere to when creating an Instagram theme. Perhaps you choose to use brand colors. LIKE.TG's Instagram, for instance, primarily uses blues, oranges, and teal, three colors prominently displayed on LIKE.TG's website and products. Alternatively, maybe you choose one of the themes listed above, such as black-and-white. Whatever the case, to create an Instagram theme, it's critical you stick to a few colors throughout all of your content. 2. Use the same filter for each post, or edit each post similarly. As noted above, consistency is a critical element in any Instagram theme, so you'll want to find your favorite one or two filters and use them for each of your posts. You can use Instagram's built-in filters, or try an editing app like VSCO or Snapseed. Alternatively, if you're going for a minimalist look, you might skip filters entirely and simply use a few editing features, like contrast and exposure. Whatever you choose, though, you'll want to continue to edit each of your posts similarly to create a cohesive feed. 3. Use a visual feed planner to plan posts far in advance. It's vital that you plan your Instagram posts ahead of time for a few different reasons, including ensuring you post a good variety of content and that you post it during a good time of day. Additionally, when creating an Instagram theme, you'll need to plan posts in advance to figure out how they fit together — like puzzle pieces, your individual pieces of content need to reinforce your theme as a whole. To plan posts far in advance and visualize how they reinforce your theme, you'll want to use a visual Instagram planner like Later or Planoly. Best of all, you can use these apps to preview your feed and ensure your theme is looking the way you want it to look before you press "Publish" on any of your posts. 4. Don't lock yourself into a theme you can't enjoy for the long haul. In middle school, I often liked to change my "look" — one day I aimed for preppy, and the next I chose a more athletic look. Of course, as I got older, I began to understand what style I could stick with for the long haul and started shopping for clothes that fit my authentic style so I wasn't constantly purchasing new clothes and getting sick of them a few weeks later. Similarly, you don't want to choose an Instagram theme you can't live with for a long time. Your Instagram theme should be an accurate reflection of your brand, and if it isn't, it probably won't last. Just because rainbow colors sound interesting at the get-go doesn't mean it's a good fit for your company's social media aesthetic as a whole. When in doubt, choose a more simple theme that provides you the opportunity to get creative and experiment without straying too far off-theme. How to Use an Instagram Theme on Your Profile 1. Choose what photos you want to post before choosing your theme. When you start an Instagram theme, there are so many options to choose from. Filters, colors, styles, angles — the choices are endless. But it’s important to keep in mind that these things won’t make your theme stand out. The content is still the star of the show. If the images aren’t balanced on the feed, your theme will look like a photo dump that happens to have the same filter on it. To curate the perfect Instagram theme, choose what photos you plan to post before choosing a theme. I highly recommend laying these photos out in a nine-square grid as well so you can see how the photos blend together. 2. Don’t forget the captions. Sure, no one is going to see the captions of your Instagram photos when they’re looking at your theme in the grid-view, but they will see them when you post each photo individually. There will be times when an image you post may be of something abstract, like the corner of a building, an empty suitcase, or a pair of sunglasses. On their own, these things might not be so interesting, but a thoughtful caption that ties the image to your overall theme can help keep your followers engaged when they might otherwise check out and keep scrolling past your profile. If you’re having a bit of writer’s block, check out these 201 Instagram captions for every type of post. 3. Switch up your theme with color blocks. Earlier, we talked about choosing a theme that you can commit to for the long haul. But there’s an exception to that rule — color transitions. Some of the best themes aren’t based on a specific color at all. Rather than using the same color palette throughout the Instagram feed, you can have colors blend into one another with each photo. This way, you can include a larger variety of photos without limiting yourself to specific hues. A Cohesive Instagram Theme At Your Fingertips Instagram marketing is more than numbers. As the most visual social media platform today, what you post and how it looks directly affects engagement, followers, and how your brand shows up online. A cohesive Instagram theme can help your brand convey a value proposition, promote a product, or execute a campaign. Colors and filters make beautiful themes, but there are several additional ways to stop your followers mid-scroll with a fun, unified aesthetic. Editor's note: This post was originally published in August 2018 and has been updated for comprehensiveness.
全球代理
 Why do SEO businesses need bulk IP addresses?
Why do SEO businesses need bulk IP addresses?
Search Engine Optimisation (SEO) has become an integral part of businesses competing on the internet. In order to achieve better rankings and visibility in search engine results, SEO professionals use various strategies and techniques to optimise websites. Among them, bulk IP addressing is an important part of the SEO business. In this article, we will delve into why SEO business needs bulk IP addresses and how to effectively utilise bulk IP addresses to boost your website's rankings and traffic.First, why does SEO business need bulk IP address?1. Avoid search engine blocking: In the process of SEO optimisation, frequent requests to search engines may be identified as malicious behaviour, resulting in IP addresses being blocked. Bulk IP addresses can be used to rotate requests to avoid being blocked by search engines and maintain the stability and continuity of SEO activities.2. Geo-targeting optimisation: Users in different regions may search through different search engines or search for different keywords. Bulk IP address can simulate different regions of the user visit, to help companies geo-targeted optimisation, to improve the website in a particular region of the search rankings.3. Multiple Keyword Ranking: A website is usually optimised for multiple keywords, each with a different level of competition. Batch IP address can be used to optimise multiple keywords at the same time and improve the ranking of the website on different keywords.4. Website content testing: Bulk IP address can be used to test the response of users in different regions to the website content, so as to optimise the website content and structure and improve the user experience.5. Data collection and competition analysis: SEO business requires a lot of data collection and competition analysis, and bulk IP address can help enterprises efficiently obtain data information of target websites.Second, how to effectively use bulk IP address for SEO optimisation?1. Choose a reliable proxy service provider: Choose a proxy service provider that provides stable and high-speed bulk IP addresses to ensure the smooth progress of SEO activities.2. Formulate a reasonable IP address rotation strategy: Formulate a reasonable IP address rotation strategy to avoid frequent requests to search engines and reduce the risk of being banned.3. Geo-targeted optimisation: According to the target market, choose the appropriate geographical location of the IP address for geo-targeted optimisation to improve the search ranking of the website in a particular region.4. Keyword Optimisation: Optimise the ranking of multiple keywords through bulk IP addresses to improve the search ranking of the website on different keywords.5. Content Optimisation: Using bulk IP addresses for website content testing, to understand the reaction of users in different regions, optimise website content and structure, and improve user experience.Third, application Scenarios of Bulk IP Address in SEO Business1. Data collection and competition analysis: SEO business requires a large amount of data collection and competition analysis, through bulk IP address, you can efficiently get the data information of the target website, and understand the competitors' strategies and ranking.2. Website Geo-targeting Optimisation: For websites that need to be optimised in different regions, bulk IP addresses can be used to simulate visits from users in different regions and improve the search rankings of websites in specific regions.3. Multi-keyword Ranking Optimisation: Bulk IP addresses can be used to optimise multiple keywords at the same time, improving the ranking of the website on different keywords.4. Content Testing and Optimisation: Bulk IP addresses can be used to test the response of users in different regions to the content of the website, optimise the content and structure of the website, and improve the user experience.Conclusion:In today's competitive Internet environment, SEO optimisation is a key strategy for companies to improve their website ranking and traffic. In order to achieve effective SEO optimisation, bulk IP addresses are an essential tool. By choosing a reliable proxy service provider, developing a reasonable IP address rotation strategy, geo-targeting optimisation and keyword optimisation, as well as conducting content testing and optimisation, businesses can make full use of bulk IP addresses to boost their website rankings and traffic, and thus occupy a more favourable position in the Internet competition.
1. Unlocking the Power of IP with Iproyal: A Comprehensive Guide2. Discovering the World of IP Intelligence with Iproyal3. Boosting Online Security with Iproyal's Cutting-Edge IP Solutions4. Understanding the Importance of IP Management: Exploring
1. Unlocking the Power of IP with Iproyal
A Comprehensive Guide2. Discovering the World of IP Intelligence with Iproyal3. Boosting Online Security with Iproyal's Cutting-Edge IP Solutions4. Understanding the Importance of IP Management
All You Need to Know About IPRoyal - A Reliable Proxy Service ProviderBenefits of Using IPRoyal:1. Enhanced Online Privacy:With IPRoyal, your online activities remain anonymous and protected. By routing your internet traffic through their secure servers, IPRoyal hides your IP address, making it virtually impossible for anyone to track your online behavior. This ensures that your personal information, such as banking details or browsing history, remains confidential.2. Access to Geo-Restricted Content:Many websites and online services restrict access based on your geographical location. IPRoyal helps you overcome these restrictions by providing proxy servers located in various countries. By connecting to the desired server, you can browse the internet as if you were physically present in that location, granting you access to region-specific content and services.3. Improved Browsing Speed:IPRoyal's dedicated servers are optimized for speed, ensuring a seamless browsing experience. By utilizing their proxy servers closer to your location, you can reduce latency and enjoy faster page loading times. This is particularly useful when accessing websites or streaming content that may be slow due to network congestion or geographical distance.Features of IPRoyal:1. Wide Range of Proxy Types:IPRoyal offers different types of proxies to cater to various requirements. Whether you need a datacenter proxy, residential proxy, or mobile proxy, they have you covered. Each type has its advantages, such as higher anonymity, rotational IPs, or compatibility with mobile devices. By selecting the appropriate proxy type, you can optimize your browsing experience.2. Global Proxy Network:With servers located in multiple countries, IPRoyal provides a global proxy network that allows you to choose the location that best suits your needs. Whether you want to access content specific to a particular country or conduct market research, their extensive network ensures reliable and efficient proxy connections.3. User-Friendly Dashboard:IPRoyal's intuitive dashboard makes managing and monitoring your proxy usage a breeze. From here, you can easily switch between different proxy types, select the desired server location, and view important usage statistics. The user-friendly interface ensures that even those with limited technical knowledge can make the most of IPRoyal's services.Conclusion:In a world where online privacy and freedom are increasingly threatened, IPRoyal provides a comprehensive solution to protect your anonymity and enhance your browsing experience. With its wide range of proxy types, global network, and user-friendly dashboard, IPRoyal is suitable for individuals, businesses, and organizations seeking reliable and efficient proxy services. Say goodbye to restrictions and safeguard your online presence with IPRoyal's secure and trusted proxy solutions.
1. Unveiling the World of Proxies: An In-Depth Dive into their Uses and Benefits2. Demystifying Proxies: How They Work and Why You Need Them3. The Power of Proxies: Unlocking a World of Online Possibilities4. Exploring the Role of Proxies in Data S
1. Unveiling the World of Proxies
An In-Depth Dive into their Uses and Benefits2. Demystifying Proxies
Title: Exploring the Role of Proxies in Ensuring Online Security and PrivacyDescription: In this blog post, we will delve into the world of proxies and their significance in ensuring online security and privacy. We will discuss the different types of proxies, their functionalities, and their role in safeguarding our online activities. Additionally, we will explore the benefits and drawbacks of using proxies, and provide recommendations for choosing the right proxy service.IntroductionIn today's digital age, where our lives have become increasingly interconnected through the internet, ensuring online security and privacy has become paramount. While we may take precautions such as using strong passwords and enabling two-factor authentication, another valuable tool in this endeavor is the use of proxies. Proxies play a crucial role in protecting our online activities by acting as intermediaries between our devices and the websites we visit. In this blog post, we will explore the concept of proxies, their functionalities, and how they contribute to enhancing online security and privacy.Understanding Proxies Proxies, in simple terms, are intermediate servers that act as connectors between a user's device and the internet. When we access a website through a proxy server, our request to view the webpage is first routed through the proxy server before reaching the website. This process helps ensure that our IP address, location, and other identifying information are not directly visible to the website we are accessing.Types of Proxies There are several types of proxies available, each with its own purpose and level of anonymity. Here are three common types of proxies:1. HTTP Proxies: These proxies are primarily used for accessing web content. They are easy to set up and can be used for basic online activities such as browsing, but they may not provide strong encryption or complete anonymity.2. SOCKS Proxies: SOCKS (Socket Secure) proxies operate at a lower level than HTTP proxies. They allow for a wider range of internet usage, including applications and protocols beyond just web browsing. SOCKS proxies are popular for activities such as torrenting and online gaming.Benefits and Drawbacks of Using Proxies Using proxies offers several advantages in terms of online security and privacy. Firstly, proxies can help mask our real IP address, making it difficult for websites to track our online activities. This added layer of anonymity can be particularly useful when accessing websites that may track or collect user data for advertising or other purposes.Moreover, proxies can also help bypass geolocation restrictions. By routing our internet connection through a proxy server in a different country, we can gain access to content that may be blocked or restricted in our actual location. This can be particularly useful for accessing streaming services or websites that are limited to specific regions.However, it is important to note that using proxies does have some drawbacks. One potential disadvantage is the reduced browsing speed that can occur when routing internet traffic through a proxy server. Since the proxy server acts as an intermediary, it can introduce additional latency, resulting in slower webpage loading times.Another potential concern with using proxies is the potential for malicious or untrustworthy proxy servers. If we choose a proxy service that is not reputable or secure, our online activities and data could be compromised. Therefore, it is crucial to research and select a reliable proxy service provider that prioritizes user security and privacy.Choosing the Right Proxy Service When selecting a proxy service, there are certain factors to consider. Firstly, it is essential to evaluate the level of security and encryption provided by the proxy service. Look for services that offer strong encryption protocols such as SSL/TLS to ensure that your online activities are protected.Additionally, consider the speed and availability of proxy servers. Opt for proxy service providers that have a wide network of servers in different locations to ensure optimal browsing speed and access to blocked content.Lastly, read user reviews and consider the reputation of the proxy service provider. Look for positive feedback regarding their customer support, reliability, and commitment to user privacy.Conclusion In an era where online security and privacy are of utmost importance, proxies offer a valuable tool for safeguarding our digital lives. By understanding the different types of proxies and their functionalities, we can make informed choices when it comes to selecting the right proxy service. While proxies provide enhanced privacy and security, it is crucial to be mindful of the potential drawbacks and choose reputable proxy service providers to ensure a safe online experience.
云服务
2018年,中小电商企业需要把握住这4个大数据趋势
2018年,中小电商企业需要把握住这4个大数据趋势
新的一年意味着你需要做出新的决定,这当然不仅限于发誓要减肥或者锻炼。商业和技术正飞速发展,你的公司需要及时跟上这些趋势。以下这几个数字能帮你在2018年制定工作规划时提供一定的方向。 人工智能(AI)在过去的12到18个月里一直是最热门的技术之一。11月,在CRM 软件服务提供商Salesforce的Dreamforce大会上,首席执行官Marc Benioff的一篇演讲中提到:Salesforce的人工智能产品Einstein每天都能在所有的云计算中做出了4.75亿次预测。 这个数字是相当惊人的。Einstein是在一年多前才宣布推出的,可现在它正在疯狂地“吐出”预测。而这仅仅是来自一个拥有15万客户的服务商。现在,所有主要的CRM服务商都有自己的人工智能项目,每天可能会产生超过10亿的预测来帮助公司改善客户交互。由于这一模式尚处于发展初期,所以现在是时候去了解能够如何利用这些平台来更有效地吸引客户和潜在客户了。 这一数字来自Facebook于2017年底的一项调查,该调查显示,人们之前往往是利用Messenger来与朋友和家人交流,但现在有越来越多人已经快速习惯于利用该工具与企业进行互动。 Facebook Messenger的战略合作伙伴关系团队成员Linda Lee表示,“人们提的问题有时会围绕特定的服务或产品,因为针对这些服务或产品,他们需要更多的细节或规格。此外,有时还会涉及到处理客户服务问题——或许他们已经购买了一个产品或服务,随后就会出现问题。” 当你看到一个3.3亿人口这个数字时,你必须要注意到这一趋势,因为在2018年这一趋势将很有可能会加速。 据Instagram在11月底发布的一份公告显示,该平台上80%的用户都关注了企业账号,每天有2亿Instagram用户都会访问企业的主页。与此相关的是,Instagram上的企业账号数量已经从7月的1500万增加到了2500万。 根据该公司的数据显示,Instagram上三分之一的小企业表示,他们已经通过该平台建立起了自己的业务;有45%的人称他们的销售额增加了;44%的人表示,该平台帮助了他们在其他城市、州或国家销售产品。 随着视频和图片正在吸引越多人们的注意力,像Instagram这样的网站,对B2C和B2B公司的重要性正在与日俱增。利用Instagram的广泛影响力,小型企业可以用更有意义的方式与客户或潜在客户进行互动。 谈到亚马逊,我们可以列出很多吸引眼球的数字,比如自2011年以来,它向小企业提供了10亿美元的贷款。而且在2017年的网络星期一,亚马逊的当天交易额为65.9亿美元,成为了美国有史以来最大的电商销售日。同时,网络星期一也是亚马逊平台卖家的最大销售日,来自全世界各地的顾客共从这些小企业订购了近1.4亿件商品。 亚马逊表示,通过亚马逊app订购的手机用户数量增长了50%。这也意味着,有相当数量的产品是通过移动设备销售出的。 所有这些大数据都表明,客户与企业的互动在未来将会发生巨大的变化。有些发展会比其他的发展更深入,但这些数字都说明了该领域的变化之快,以及技术的加速普及是如何推动所有这些发展的。 最后,希望这些大数据可以对你的2018年规划有一定的帮助。 (编译/LIKE.TG 康杰炜)
2020 AWS技术峰会和合作伙伴峰会线上举行
2020 AWS技术峰会和合作伙伴峰会线上举行
2020年9月10日至11日,作为一年一度云计算领域的大型科技盛会,2020 AWS技术峰会(https://www.awssummit.cn/) 正式在线上举行。今年的峰会以“构建 超乎所见”为主题,除了展示AWS最新的云服务,探讨前沿云端技术及企业最佳实践外,还重点聚焦垂直行业的数字化转型和创新。AWS宣布一方面加大自身在垂直行业的人力和资源投入,组建行业团队,充分利用AWS的整体优势,以更好的发掘、定义、设计、架构和实施针对垂直行业客户的技术解决方案和场景应用;同时携手百家中国APN合作伙伴发布联合解决方案,重点覆盖金融、制造、汽车、零售与电商、医疗与生命科学、媒体、教育、游戏、能源与电力九大行业,帮助这些行业的客户实现数字化转型,进行数字化创新。峰会期间,亚马逊云服务(AWS)还宣布与毕马威KPMG、神州数码分别签署战略合作关系,推动企业上云和拥抱数字化。 亚马逊全球副总裁、AWS大中华区执董事张文翊表示,“AWS一直致力于不断借助全球领先的云技术、广泛而深入的云服务、成熟和丰富的商业实践、全球的基础设施覆盖,安全的强大保障以及充满活力的合作伙伴网络,加大在中国的投入,助力中国客户的业务创新、行业转型和产业升级。在数字化转型和数字创新成为‘新常态’的今天,我们希望通过AWS技术峰会带给大家行业的最新动态、全球前沿的云计算技术、鲜活的数字创新实践和颇具启发性的文化及管理理念,推动中国企业和机构的数字化转型和创新更上层楼。” 构建场景应用解决方案,赋能合作伙伴和客户 当前,传统企业需要上云,在云上构建更敏捷、更弹性和更安全的企业IT系统,实现数字化转型。同时,在实现上云之后,企业又迫切需要利用现代应用开发、大数据、人工智能与机器学习、容器技术等先进的云技术,解决不断涌现的业务问题,实现数字化创新,推动业务增长。 亚马逊云服务(AWS)大中华区专业服务总经理王承华表示,为了更好的提升行业客户体验,截至目前,AWS在中国已经发展出了数十种行业应用场景及相关的技术解决方案。 以中国区域部署的数字资产管理和云上会议系统两个应用场景解决方案为例。其中,数字资产盘活机器人让客户利用AWS云上资源低成本、批处理的方式标记数字资产,已经在银行、证券、保险领域率先得到客户青睐;AWS上的BigBlueButton,让教育机构或服务商可以在AWS建一套自己的在线会议系统,尤其适合当前急剧增长的在线教育需求。 这些行业应用场景解决方案经过客户验证成熟之后,AWS把它们转化为行业解决方案,赋能APN合作伙伴,拓展给更多的行业用户部署使用。 发布百家APN合作伙伴联合解决方案 打造合作伙伴社区是AWS服务企业客户的一大重点,也是本次峰会的亮点。AWS通过名为APN(AWS合作伙伴网络)的全球合作伙伴计划,面向那些利用AWS为客户构建解决方案的技术和咨询企业,提供业务支持、技术支持和营销支持,从而赋能这些APN合作伙伴,更好地满足各行各业、各种规模客户地需求。 在于9月9日举行的2020 AWS合作伙伴峰会上,AWS中国区生态系统及合作伙伴部总经理汪湧表示,AWS在中国主要从四个方面推进合作伙伴网络的构建。一是加快AWS云服务和功能落地,从而使合作伙伴可以利用到AWS全球最新的云技术和服务来更好地服务客户;二是推动跨区域业务扩展,帮助合作伙伴业务出海,也帮助全球ISV落地中国,同时和区域合作伙伴一起更好地服务国内各区域市场的客户;三是与合作伙伴一起着力传统企业上云迁移;四是打造垂直行业解决方案。 一直以来,AWS努力推动将那些驱动中国云计算市场未来、需求最大的云服务优先落地中国区域。今年上半年,在AWS中国区域已经落地了150多项新服务和功能,接近去年的全年总和。今年4月在中国落地的机器学习服务Amazon SageMaker目前已经被德勤、中科创达、东软、伊克罗德、成都潜在(行者AI)、德比软件等APN合作伙伴和客户广泛采用,用以创新以满足层出不穷的业务需求,推动增长。 联合百家APN合作伙伴解决方案打造垂直行业解决方案是AWS中国区生态系统构建的战略重点。 以汽车行业为例,东软集团基于AWS构建了云原生的汽车在线导航业务(NOS),依托AWS全球覆盖的基础设施、丰富的安全措施和稳定可靠的云平台,实现车规级的可靠性、应用程序的持续迭代、地图数据及路况信息的实时更新,服务中国车企的出海需求。 上海速石科技公司构建了基于AWS云上资源和用户本地算力的一站式交付平台,为那些需要高性能计算、海量算力的客户,提供一站式算力运营解决方案,目标客户涵盖半导体、药物研发、基因分析等领域。利用云上海量的算力,其客户在业务峰值时任务不用排队,极大地提高工作效率,加速业务创新。 外研在线在AWS上构建了Unipus智慧教学解决方案,已经服务于全国1700多家高校、1450万师生。通过将应用部署在AWS,实现SaaS化的交付模式,外研在线搭建了微服务化、自动伸缩的架构,可以自动适应教学应用的波峰波谷,提供稳定、流畅的体验,并且节省成本。 与毕马威KPMG、神州数码签署战略合作 在2020AWS技术峰会和合作伙伴峰会上,AWS还宣布与毕马威、神州数码签署战略合作关系,深化和升级合作。 AWS与毕马威将在中国开展机器学习、人工智能和大数据等领域的深入合作,毕马威将基于AWS云服务,结合其智慧之光系列数字化解决方案,为金融服务、制造业、零售、快消、以及医疗保健和生命科学等行业客户,提供战略规划、风险管理、监管与合规等咨询及实施服务。AWS将与神州数码将在赋能合作伙伴上云转型、全生命周期管理及助力全球独立软件开发商(ISV)落地中国方面展开深入合作,助力中国企业和机构的数字化转型与创新。
2021re:Invent全球大会圆满落幕 亚马逊云科技致敬云计算探路者
2021re
Invent全球大会圆满落幕 亚马逊云科技致敬云计算探路者
本文来源:LIKE.TG 作者:Ralf 全球最重磅的云计算大会,2021亚马逊云科技re:Invent全球大会已圆满落幕。re:Invent大会是亚马逊云科技全面展示新技术、产品、功能和服务的顶级行业会议,今年更是迎来十周年这一里程碑时刻。re:Invent,中文意为重塑,是亚马逊云科技一直以来坚持的“精神内核”。 作为Andy Jassy和新CEO Adam Selipsky 交接后的第一次re:Invent大会,亚马逊云科技用诸多新服务和新功能旗帜鲜明地致敬云计算探路者。 致敬云计算探路者 亚马逊云科技CEO Adam Selipsky盛赞云上先锋客户为“探路者”,他说,“这些客户都有巨大的勇气和魄力通过上云做出改变。他们勇于探索新业务、新模式,积极重塑自己和所在的行业。他们敢于突破边界,探索未知领域。有时候,我们跟客户共同努力推动的这些工作很艰难,但我们喜欢挑战。我们把挑战看作探索未知、发现新机遇的机会。回过头看,每一个这样的机构都是在寻找一条全新的道路。他们是探路者。” Adam 认为,探路者具有三个特征:创新不息,精进不止(Constant pursuit of a better way);独识卓见,领势而行(Ability to see what others don’t);授人以渔,赋能拓新(Enable others to forge their own paths)。 十五年前,亚马逊云科技缔造了云计算概念,彼时IT和基础设施有很大的局限。不仅贵,还反应慢、不灵活,大大限制了企业的创新。亚马逊云科技意识到必须探索一条新的道路,重塑企业IT。 从2006年的Amazon S3开始,IT应用的基础服务,存储、计算、数据库不断丰富。亚马逊云科技走过的15年历程 也是云计算产业发展的缩影。 目前,S3现在存储了超过100万亿个对象,EC2每天启用超过6000万个新实例。包括S3和EC2,亚马逊云科技已经提供了200大类服务,覆盖了计算、存储、网络、安全、数据库、数据分析、人工智能、物联网、混合云等各个领域,甚至包括最前沿的量子计算服务和卫星数据服务 (图:亚马逊全球副总裁、亚马逊云科技大中华区执行董事张文翊) 对于本次大会贯穿始终的探路者主题,亚马逊全球副总裁、亚马逊云科技大中华区执行董事张文翊表示:“大家对这个概念并不陌生,他们不被规则所限,从不安于现状;他们深入洞察,开放视野;还有一类探路者,他们不断赋能他人。我们周围有很多鲜活的例子,无论是科研人员发现新的治疗方案挽救生命,还是为身处黑暗的人带去光明; 无论是寻找新的手段打破物理边界,还是通过云进行独特的创新,探路源源不断。” 技术升级创新不断 本次re:Invent大会,亚马逊云科技发布涵盖计算、物联网、5G、无服务器数据分析、大机迁移、机器学习等方向的多项新服务和功能,为业界带来大量重磅创新服务和产品技术更新,包括发布基于新一代自研芯片Amazon Graviton3的计算实例、帮助大机客户向云迁移的Amazon Mainframe Modernization、帮助企业构建移动专网的Amazon Private 5G、四个亚马逊云科技分析服务套件的无服务器和按需选项以及为垂直行业构建的云服务和解决方案,如构建数字孪生的服务Amazon IoT TwinMaker和帮助汽车厂商构建车联网平台的Amazon IoT FleetWise。 (图:亚马逊云科技大中华区产品部总经理顾凡) 亚马逊云科技大中华区产品部总经理顾凡表示,新一代的自研ARM芯片Graviton3性能有显著提升。针对通用的工作负载,Graviton3比Graviton2的性能提升25%,而专门针对高性能计算里的科学类计算,以及机器学习等这样的负载会做更极致的优化。针对科学类的计算负载,Graviton3的浮点运算性能比Graviton2提升高达2倍;像加密相关的工作负载产生密钥加密、解密,这部分性能比Graviton2会提升2倍,针对机器学习负载可以提升高达3倍。Graviton3实例可以减少多达60%的能源消耗。 新推出的Amazon Private 5G,让企业可以轻松部署和扩展5G专网,按需配置。Amazon Private 5G将企业搭建5G专网的时间从数月降低到几天。客户只需在亚马逊云科技的控制台点击几下,就可以指定想要建立移动专网的位置,以及终端设备所需的网络容量。亚马逊云科技负责交付、维护、建立5G专网和连接终端设备所需的小型基站、服务器、5G核心和无线接入网络(RAN)软件,以及用户身份模块(SIM卡)。Amazon Private 5G可以自动设置和部署网络,并按需根据额外设备和网络流量的增长扩容。 传统工业云化加速 在亚马逊云科技一系列新服务和新功能中,针对传统工业的Amazon IoT TwinMaker和Amazon IoT FleetWise格外引人关注。 就在re:Invent大会前一天。工业和信息化部发布《“十四五”信息化和工业化深度融合发展规划》(《规划》),《规划》明确了到2025年发展的分项目标,其中包括工业互联网平台普及率达45%。 亚马逊云科技布局物联网已经有相当长的时间。包括工业互联网里的绿色产线的维护、产线的质量监控等,在数字孪生完全构建之前,已经逐步在实现应用的实体里面。亚马逊云科技大中华区产品部计算与存储总监周舸表示,“在产线上怎么自动化地去发现良品率的变化,包括Amazon Monitron在产线里面可以直接去用,这些传感器可以监测震动、温度等,通过自动的建模去提早的预测可能会出现的问题,就不用等到灾难发生,而是可以提早去换部件或者加点机油解决潜在问题。” 周舸认为工业互联的场景在加速。但很多中小型的工厂缺乏技术能力。“Amazon IoT TwinMaker做数字孪生的核心,就是让那些没有那么强的能力自己去构建或者去雇佣非常专业的构建的公司,帮他们搭建数字孪生,这个趋势是很明确的,我们也在往这个方向努力。” 对于汽车工业,特别是新能源汽车制造。数据的收集管理已经变得越来越重要。Amazon IoT FleetWise,让汽车制造商更轻松、经济地收集、管理车辆数据,同时几乎实时上传到云端。通过Amazon IoT FleetWise,汽车制造商可以轻松地收集和管理汽车中任何格式的数据(无论品牌、车型或配置),并将数据格式标准化,方便在云上轻松进行数据分析。Amazon IoT FleetWise的智能过滤功能,帮助汽车制造商近乎实时地将数据高效上传到云端,为减少网络流量的使用,该功能也允许开发人员选择需要上传的数据,还可以根据天气条件、位置或汽车类型等参数来制定上传数据的时间规则。当数据进入云端后,汽车制造商就可以将数据应用于车辆的远程诊断程序,分析车队的健康状况,帮助汽车制造商预防潜在的召回或安全问题,或通过数据分析和机器学习来改进自动驾驶和高级辅助驾驶等技术。
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1210保税备货模式是什么?1210跨境电商中找到适合的第三方支付接口平台
1210保税备货模式是什么?1210跨境电商中找到适合的第三方支付接口平台
  1210保税备货模式是一种跨境电商模式,它允许电商平台在境外仓库存储商品,以便更快、更便宜地满足国内消费者的需求。这种模式的名称“1210”代表了其核心特点,即1天出货、2周入仓、10天达到终端用户。它是中国跨境电商行业中的一种创新模式,为消费者提供了更快速、更便宜的购物体验,同时也促进了国际贸易的发展。   在1210保税备货模式中,电商平台会在国外建立仓库,将商品直接从生产国或供应商处运送到境外仓库进行存储。   由于商品已经在国内仓库存储,当消费者下单时,可以更快速地发货,常常在1天内出货,大大缩短了交付时间。   1210模式中,商品已经进入国内仓库,不再需要跨越国际海运、海关清关等环节,因此物流成本较低。   由于商品直接从生产国或供应商处运送到境外仓库,不需要在国内仓库大量储备库存,因此降低了库存成本。   1210模式可以更精确地控制库存,减少滞销和过期商品,提高了库存周转率。   在实施1210保税备货模式时,选择合适的第三方支付接口平台也是非常重要的,因为支付环节是电商交易中不可或缺的一环。   确保第三方支付接口平台支持国际信用卡支付、外币结算等功能,以便国际消费者能够顺利完成支付。   提供多种支付方式,以满足不同消费者的支付习惯。   第三方支付接口平台必须具备高度的安全性,包含数据加密、反欺诈措施等,以保护消费者的支付信息和资金安全。   了解第三方支付接口平台的跨境结算机制,确保可以顺利将国际销售收入转换为本地货币,并减少汇率风险。   选择一个提供良好技术支持和客户服务的支付接口平台,以应对可能出现的支付问题和故障。   了解第三方支付接口平台的费用结构,包含交易费率、结算费用等,并与自身业务规模和盈利能力相匹配。   确保第三方支付接口平台可以与电商平台进行顺畅的集成,以实现订单管理、库存控制和财务管理的无缝对接。   考虑未来业务扩展的可能性,选择一个具有良好扩展性的支付接口平台,以适应不断增长的交易量和新的市场需求。   在选择适合的第三方支付接口平台时,需要考虑到以上支付功能、安全性、成本、技术支持等因素,并与自身业务需求相匹配。 本文转载自:https://www.ipaylinks.com/
2023年德国VAT注册教程有吗?增值税注册注意的事及建议
2023年德国VAT注册教程有吗?增值税注册注意的事及建议
  作为欧洲的经济大国,德国吸引了许多企业在该地区抢占市场。在德国的商务活动涉及增值税(VAT)难题是在所难免的。   1、决定是否务必注册VAT   2023年,德国的增值税注册门槛是前一年销售额超过17500欧。对在德国有固定经营场所的外国企业,不管销售状况怎样,都应开展增值税注册。   2、备好所需的材料   企业注册证实   业务地址及联络信息   德国银行帐户信息   预估销售信息   公司官方文件(依据公司类型可能有所不同)   3、填写申请表   要访问德国税务局的官网,下载并递交增值税注册申请表。确保填好精确的信息,由于不准确的信息可能会致使申请被拒或审计耽误。   4、提交申请   填写申请表后,可以经过电子邮箱把它发给德国税务局,或在某些地区,可以网上申请申请。确保另附全部必须的文件和信息。   5、等待审批   递交了申请,要耐心地等待德国税务局的准许。因为税务局的工作负荷和个人情况,准许时长可能会有所不同。一般,审计可能需要几周乃至几个月。   6、得到VAT号   假如申请获得批准,德国税务局可能授于一个增值税号。这个号码应当是德国增值税申报和支付业务视频的关键标示。   7、逐渐申报和付款   获得了增值税号,你应该根据德国的税收要求逐渐申报和付款。根据规定时间表,递交增值税申请表并缴纳相应的税款。   注意的事和提议   填写申请表时,确保信息精确,避免因错误报告导致审批耽误。   假如不强化对德国税制改革的探索,提议寻求专业税务顾问的支持,以保障申请和后续申报合规。   储存全部申请及有关文件的副本,用以日后的审查和审计。 本文转载自:https://www.ipaylinks.com/
2023年注册代理英国VAT的费用
2023年注册代理英国VAT的费用
  在国际贸易和跨境电商领域,注册代理英国增值税(VAT)是一项关键且必要的步骤。2023年,许多企业为了遵守英国的税务法规和合规要求,选择注册代理VAT。   1. 注册代理英国VAT的背景:   英国是一个重要的国际贸易和电商市场,许多企业选择在英国注册VAT,以便更好地服务英国客户,并利用英国的市场机会。代理VAT是指经过一个英国境内的注册代理公司进行VAT申报和纳税,以简化税务流程。   2. 费用因素:   注册代理英国VAT的费用取决于多个因素,包括但不限于:   业务规模: 企业的业务规模和销售额可能会影响注册代理VAT的费用。常常来说,销售额较大的企业可能需要支付更高的费用。   代理公司选择: 不同的注册代理公司可能收取不同的费用。选择合适的代理公司很重要,他们的费用结构可能会因公司而异。   服务范围: 代理公司可能提供不同的服务范围,包括申报、纳税、咨询等。你选择的服务范围可能会影响费用。   附加服务: 一些代理公司可能提供附加服务,如法律咨询、报告生成等,这些服务可能会增加费用。   复杂性: 如果的业务涉及复杂的税务情况或特殊需求,可能需要额外的费用。   3. 典型费用范围:   2023年注册代理英国VAT的费用范围因情况而异,但常常可以在几百英镑到数千英镑之间。对小规模企业,费用可能较低,而对大规模企业,费用可能较高。   4. 寻求报价:   如果计划在2023年注册代理英国VAT,建议与多家注册代理公司联系,获得费用报价。这样可以比较不同公司的费用和提供的服务,选择最适合你需求的代理公司。   5. 其他费用考虑:   除了注册代理VAT的费用,你还应考虑其他可能的费用,如VAT申报期限逾期罚款、税务咨询费用等。保持合规和及时申报可以避免这些额外费用。   6. 合理预算:   在注册代理英国VAT时,制定合理的预算非常重要。考虑到不同因素可能会影响费用,确保有足够的资金来支付这些费用是必要的。   2023年注册代理英国VAT的费用因多个因素而异。了解这些因素,与多家代理公司沟通,获取费用报价,制定合理的预算,会有助于在注册VAT时做出聪明的决策。确保业务合规,并寻求专业税务顾问的建议,以保障一切顺利进行。 本文转载自:https://www.ipaylinks.com/
广告投放
2021年B2B外贸跨境获客催化剂-行业案例之测控
2021年B2B外贸跨境获客催化剂-行业案例之测控
随着时间的推移,数字化已经在中国大量普及,越来越多的B2B企业意识到数字营销、内容营销、社交传播可以帮助业务加速推进。但是在和大量B2B出海企业的合作过程中,我们分析发现在实际的营销中存在诸多的瓶颈和痛点。 例如:传统B2B营销方式获客难度不断增大、获客受众局限、询盘成本高但质量不高、询盘数量增长不明显、线下展会覆盖客户的流失等,这些都是每天考验着B2B营销人的难题。 说到这些痛点和瓶颈,就不得不提到谷歌广告了,对比其他推广平台,Google是全球第一大搜索引擎,全球月活跃用户高达50亿人,覆盖80%全球互联网用户。受众覆盖足够的前提下,谷歌广告( Google Ads)还包括多种广告形式:搜索广告、展示广告(再营销展示广告、竞对广告)、视频广告、发现广告等全方位投放广告,关键字精准定位投放国家的相关客户,紧跟采购商的采购途径,增加获客。可以完美解决上面提到的痛点及瓶颈。 Google 360度获取优质流量: Google线上营销产品全方位助力: 营销网站+黄金账户诊断报告+定期报告=效果。 Google Ads为太多B2B出海企业带来了红利,这些红利也并不是简简单单就得来的,秘诀就是贵在坚持。多年推广经验总结:即使再好的平台,也有部分企业运营效果不好的时候,那应该怎么办?像正处在这种情况下的企业就应该放弃吗? 答案是:不,我们应该继续优化,那为什么这么说呢?就是最近遇到一个很典型的案例一家测控行业的企业,仅仅投放2个月的Google Ads,就因为询盘数量不多(日均150元,3-4封/月),投资回报率不成正比就打算放弃。 但其实2个月不足以说明什么,首先谷歌推广的探索期就是3个月,2个月基本处于平衡稳定的阶段。 其次对于刚刚做谷歌广告的新公司来说,国外客户是陌生的,即使看到广告进到网站也并不会第一时间就留言,货比三家,也会增加采购商的考虑时间,一直曝光在他的搜索结果页产生熟悉度,总会增加一些决定因素。 再有日预算150元,不足以支撑24小时点击,有时在搜索量较大的时候却没有了预算,导致了客户的流失。 最后不同的行业账户推广形式及效果也不一样,即使行业一样但是网站、公司实力等因素就不可能一模一样,即使一模一样也会因为流量竞争、推广时长等诸多因素导致效果不一样。 成功都是摸索尝试出来的,这个企业账户也一样,经过我们进一步的沟通分析决定再尝试一次, 这一次深度的分析及账户的优化后,最终效果翻了2-3倍,做到了从之前的高成本、低询盘量到现在低成本、高询盘的过渡。 这样的一个操作就是很好地开发了这个平台,通过充分利用达到了企业想要的一个效果。所以说啊,当谷歌广告做的不好的时候不应该放弃,那我们就来一起看一下这个企业是如何做到的。 2021年B2B外贸跨境获客催化剂-行业案例之测控(上) 一、主角篇-雷达液位测量仪 成立时间:2010年; 业务:微波原理的物料雷达液位测量与控制仪器生产、技术研发,雷达开发; 产业规模:客户分布在11个国家和地区,包括中国、巴西、马来西亚和沙特阿拉伯; 公司推广目标:低成本获得询盘,≤200元/封。 本次分享的主角是测控行业-雷达液位测量仪,目前预算250元/天,每周6-7封有效询盘,广告形式以:搜索广告+展示再营销为主。 过程中从一开始的控制预算150/天以搜索和展示再营销推广形式为主,1-2封询盘/周,询盘成本有时高达1000/封,客户预期是100-300的单个询盘成本,对于公司来说是能承受的价格。 以增加询盘数量为目的尝试过竞对广告和Gmail广告的推广,但投放过程中的转化不是很明显,一周的转化数据只有1-2个相比搜索广告1:5,每天都会花费,因为预算问题客户计划把重心及预算放在搜索广告上面,分析后更改账户广告结构还是以搜索+再营销为主,所以暂停这2种广告的推广。 账户调整后大约2周数据表现流量稳定,每周的点击、花费及转化基本稳定,平均为588:1213:24,询盘提升到了3-5封/周。 账户稳定后新流量的获取方法是现阶段的目标,YouTube视频广告,几万次的展示曝光几天就可以完成、单次观看价格只有几毛钱,传达给客户信息建议后,达成一致,因为这正是该客户一直所需要的低成本获取流量的途径; 另一个计划投放视频广告的原因是意识到想要增加网站访客进而增加获客只靠文字和图片已经没有太多的竞争力了,同时换位思考能够观看到视频也能提升采购商的购买几率。 所以就有了这样的后期的投放规划:搜索+展示再营销+视频广告300/天的推广形式,在谷歌浏览器的搜索端、B2B平台端、视频端都覆盖广告,实现尽可能多的客户数量。 关于具体的关于YouTube视频广告的介绍我也在另一篇案例里面有详细说明哦,指路《YouTube视频广告助力B2B突破瓶颈降低营销成本》,邀请大家去看看,干货满满,绝对让你不虚此行~ 二、方向转变篇-推广产品及国家重新定位 下面我就做一个账户实际转变前后的对比,这样大家能够更清楚一些: 最关键的来了,相信大家都想知道这个转变是怎么来的以及谷歌账户做了哪些调整把效果做上来的。抓住下面几点,相信你也会有所收获: 1. 产品投放新定位 因为企业是专门研发商用雷达,所以只投放这类的测量仪,其中大类主要分为各种物料、料位、液位测量仪器,其他的不做。根据关键字规划师查询的产品关键字在全球的搜索热度,一开始推广的只有雷达液位计/液位传感器/液位测量作为主推、无线液位变送器作为次推,产品及图片比较单一没有太多的竞争力。 后期根据全球商机洞察的行业产品搜索趋势、公司计划等结合统计结果又添加了超声波传感器、射频/电容/导纳、无线、制导雷达液位传感器、高频雷达液位变送器、无接触雷达液位计,同时增加了图片及详情的丰富性,做到了行业产品推广所需的多样性丰富性。像静压液位变送器、差压变送器没有他足够的搜索热度就没有推广。 2. 国家再筛选 转变前期的国家选取是根据海关编码查询的进口一直处在增长阶段的国家,也参考了谷歌趋势的国家参考。2018年全球进口(采购量)200.58亿美金。 采购国家排名:美国、德国、日本、英国、法国、韩国、加拿大、墨西哥、瑞典、荷兰、沙特阿拉伯。这些国家只能是参考切记跟风投放,疫情期间,实际的询盘国家还要靠数据和时间积累,做到及时止损即可。 投放过程不断摸索,经过推广数据总结,也根据实际询盘客户所在地暂停了部分国家,例如以色列、日本、老挝、摩纳哥、卡塔尔等国家和地区,加大力度投放巴西、秘鲁、智利、俄罗斯等国家即提高10%-20%的出价,主要推广地区还是在亚洲、南美、拉丁美洲、欧洲等地。 发达国家像英美加、墨西哥由于采购商的参考层面不同就单独拿出来给一小部分预算,让整体的预算花到发展中国家。通过后期每周的询盘反馈及时调整国家出价,有了现在的转变: 转变前的TOP10消耗国家: 转变后的TOP10消耗国家: 推广的产品及国家定下来之后,接下来就是做账户了,让我们继续往下看。 三、装备篇-账户投放策略 说到账户投放,前提是明确账户投放策略的宗旨:确保投资回报率。那影响投资回报率的效果指标有哪些呢?其中包含账户结构 、效果再提升(再营销、视频、智能优化等等)、网站着陆页。 那首先说明一下第一点:账户的结构,那账户结构怎么搭建呢?在以产品营销全球为目标的广告投放过程中,该客户在3个方面都有设置:预算、投放策略、搜索+再营销展示广告组合拳,缺一不可,也是上面转变后整体推广的总结。 账户结构:即推广的广告类型主要是搜索广告+再营销展示广告,如下图所示,下面来分别说明一下。 1、搜索广告结构: 1)广告系列 创建的重要性:我相信有很大一部分企业小伙伴在创建广告系列的时候都在考虑一个大方向上的问题:广告系列是针对所有国家投放吗?还是说不同的广告系列投放不同的国家呢? 实操规则:其实建议选择不同广告系列投放不同的国家,为什么呢?因为每个国家和每个国家的特点不一样,所以说在广告投放的时候应该区分开,就是着重性的投放。所以搜索广告系列的结构就是区分开国家,按照大洲划分(投放的国家比较多的情况下,这样分配可以观察不同大洲的推广数据以及方便对市场的考察)。 优化技巧:这样操作也方便按照不同大洲的上班时间调整广告投放时间,做到精准投放。 数据分析:在数据分析方面更方便观察不同大洲的数据效果,从而调整国家及其出价;进而能了解到不同大洲对于不同产品的不同需求,从而方便调整关键字。 这也引出了第二个重点调整对象—关键字,那关键字的选取是怎么去选择呢? 2)关键字 分为2部分品牌词+产品关键字,匹配形式可以采用广泛带+修饰符/词组/完全。 精准投放关键字: 品牌词:品牌词是一直推广的关键字,拓展品牌在海外的知名度应为企业首要的目的。 广告关键词:根据投放1个月数据发现:该行业里有一部分是大流量词(如Sensors、water level controller、Ultrasonic Sensor、meter、transmitter),即使是关键字做了完全匹配流量依然很大,但是实际带来的转化却很少也没有带来更多的询盘,这些词的调整过程是从修改匹配形式到降低出价再到暂停,这种就属于无效关键字了,我们要做到的是让预算花费到具体的产品关键字上。 其次流量比较大的词(如+ultrasound +sensor)修改成了词组匹配。还有一类词虽然搜索量不大但是有效性(转化次数/率)较高(例如:SENSOR DE NIVEL、level sensor、capacitive level sensor、level sensor fuel),针对这些关键字再去投放的时候出价可以相对高一些,1-3元即可。调整后的关键字花费前后对比,整体上有了大幅度的变化: 转变前的TOP10热力关键字: 转变后的TOP10热力关键字: PS: 关键字状态显示“有效”—可以采用第一种(防止错失账户投放关键字以外其他的也适合推广的该产品关键字)、如果投放一周后有花费失衡的状态可以把该关键字修改为词组匹配,观察一周还是失衡状态可改为完全匹配。 关键字状态显示“搜索量较低”—广泛匹配观察一个月,如果依然没有展示,建议暂停,否则会影响账户评级。 3)调整关键字出价 次推产品的出价都降低到了1-2元,主推产品也和实际咨询、平均每次点击费用做了对比调整到了3-4元左右(这些都是在之前高出价稳定排名基础后调整的)。 4)广告系列出价策略 基本包含尽可能争取更多点击次数/每次点击费用人工出价(智能)/目标每次转化费用3种,那分别什么时候用呢? 当账户刚刚开始投放的时候,可以选择第一/二种,用来获取更多的新客,当账户有了一定的转化数据的时候可以把其中转化次数相对少一些的1-2个广告系列的出价策略更改为“目标每次转化费用”出价,用来增加转化提升询盘数量。转化次数多的广告系列暂时可以不用更换,等更改出价策略的广告系列的转化次数有增加后,可以尝试再修改。 5)广告 1条自适应搜索广告+2条文字广告,尽可能把更多的信息展示客户,增加点击率。那具体的广告语的侧重点是什么呢? 除了产品本身的特点优势外,还是着重于企业的具体产品分类和能够为客户做到哪些服务,例如:专注于各种物体、料位、液位测量仪器生产与研发、为客户提供一体化测量解决方案等。这样进到网站的也基本是寻找相关产品的,从而也进一步提升了转化率。 6)搜索字词 建议日均花费≥200元每周筛选一次,<200元每2周筛选一次。不相关的排除、相关的加到账户中,减少无效点击和花费,这样行业关键字才会越来越精准,做到精准覆盖意向客户。 7)账户广告系列预算 充足的账户预算也至关重要,200-300/天的预算,为什么呢?预算多少其实也就代表着网站流量的多少,之前150/天的预算,账户到下午6点左右就花完了,这样每天就会流失很大一部分客户。广告系列预算可以根据大洲国家的数量分配。数量多的可以分配多一些比如亚洲,预算利用率不足时可以共享预算,把多余的预算放到花费高的系列中。 说完了搜索广告的结构后,接下来就是再营销展示广告了。 2、效果再提升-再营销展示广告结构 因为广告投放覆盖的是曾到达过网站的客户,所以搜索广告的引流精准了,再营销会再抓取并把广告覆盖到因某些原因没有选择我们的客户,做到二次营销。(详细的介绍及操作可以参考文章《精准投放再营销展示广告,就抓住了提升Google营销效果的一大步》) 1)广告组:根据在GA中创建的受众群体导入到账户中。 2)图片: 选择3种产品,每种产品的图片必须提供徽标、横向图片、纵向图片不同尺寸至少1张,最多5张,横向图片可以由多张图片合成一张、可以添加logo和产品名称。 图片设计:再营销展示广告的图片选取从之前的直接选用网站上的产品图,到客户根据我给出的建议设计了独特的产品图片,也提升了0.5%的点击率。 PS: 在广告推广过程中,该客户做过2次产品打折促销活动,信息在图片及描述中曝光,转化率上升1%,如果企业有这方面的计划,可以尝试一下。 YouTube视频链接:如果有YouTube视频的话,建议把视频放在不同的产品页面方便客户实时查看视频,增加真实性,促进询盘及成单,如果视频影响网站打开速度,只在网站标头和logo链接即可。 智能优化建议:谷歌账户会根据推广的数据及状态给出相应的智能优化建议,优化得分≥80分为健康账户分值,每条建议可根据实际情况采纳。 3、网站着陆页 这也是沟通次数很多的问题了,因为即使谷歌为网站引来再多的有质量的客户,如果到达网站后没有看到想要或更多的信息,也是无用功。网站也是企业的第二张脸,做好网站就等于成功一半了。 转变前产品图片模糊、数量少、缺少实物图、工厂库存等体现实力及真实性的图片;产品详情也不是很多,没有足够的竞争力。多次沟通积极配合修改调整后上面的问题全部解决了。网站打开速度保持在3s内、网站的跳出率从之前的80%降到了70%左右、平均页面停留时间也增加了30%。 FAQ:除了正常的网站布局外建议在关于我们或产品详情页添加FAQ,会减少采购商的考虑时间,也会减少因时差导致的与客户失联。如下图所示: 四、账户效果反馈分享篇 1、效果方面 之前每周只有1-2封询盘,现在达到了每周3-5封询盘,确实是提高了不少。 2、询盘成本 从当初的≥1000到现在控制在了100-300左右。 3、转化率 搜索广告+再营销展示广告让网站访客流量得到了充分的利用,增加了1.3%转化率。 就这样,该客户的谷歌账户推广效果有了新的转变,询盘稳定后,又开启了Facebook付费广告,多渠道推广产品,全域赢为目标,产品有市场,这样的模式肯定是如虎添翼。 到此,本次的测控案例就分享完了到这里了,其实部分行业的推广注意事项大方向上都是相通的。催化剂并不难得,找到适合自己的方法~谷歌广告贵在坚持,不是说在一个平台上做的不好就不做了,效果不理想可以改进,改进就能做好。 希望本次的测控案例分享能在某些方面起到帮助作用,在当今大环境下,助力企业增加网站流量及询盘数量,2021祝愿看到这篇文章的企业能够更上一层楼!
2022 年海外社交媒体15 个行业的热门标签
2022 年海外社交媒体15 个行业的热门标签
我们可以在社交媒体上看到不同行业,各种类型的品牌和企业,这些企业里有耳熟能详的大企业,也有刚建立的初创公司。 海外社交媒体也与国内一样是一个广阔的平台,作为跨境企业和卖家,如何让自己的品牌在海外社媒上更引人注意,让更多人看到呢? 在社交媒体上有一个功能,可能让我们的产品、内容被看到,也能吸引更多人关注,那就是标签。 2022年海外社交媒体中不同行业流行哪些标签呢?今天为大家介绍十五个行业超过140多个热门标签,让你找到自己行业的流量密码。 1、银行业、金融业 据 Forrester咨询称,银行业目前已经是一个数万亿的行业,估值正以惊人的速度飙升。银行业正在加速创新,准备加大技术、人才和金融科技方面的投资。 Z世代是金融行业的积极追随者,他们希望能够赶上投资机会。 案例: Shibtoken 是一种去中心化的加密货币,它在社交媒体上分享了一段关于诈骗的视频,受到了很大的关注度,视频告诉观众如何识别和避免陷入诈骗,在短短 20 小时内收到了 1.2K 条评论、3.6K 条转发和 1.14 万个赞。 银行和金融的流行标签 2、娱乐行业 娱乐行业一直都是有着高热度的行业,OTT (互联网电视)平台则进一步提升了娱乐行业的知名度,让每个家庭都能享受到娱乐。 案例: 仅 OTT 视频收入就达 246 亿美元。播客市场也在创造价值 10 亿美元的广告收入。 Netflix 在 YouTube 上的存在则非常有趣,Netflix会发布最新节目预告,进行炒作。即使是非 Netflix 用户也几乎可以立即登录该平台。在 YouTube 上,Netflix的订阅者数量已达到 2220 万。 3、新型微交通 目前,越来越多的人开始关注绿色出行,选择更环保的交通工具作为短距离的出行工具,微型交通是新兴行业,全球市场的复合年增长率为 17.4%,预计到2030 年将达到 195.42 美元。 Lime 是一项倡导游乐设施对人类和环境更安全的绿色倡议。他们会使用#RideGreen 的品牌标签来刺激用户发帖并推广Lime倡议。他们已经通过定期发帖吸引更多人加入微交通,并在社交媒体形成热潮。 4、时尚与美容 到 2025 年,时尚产业将是一个万亿美元的产业,数字化会持续加快这一进程。96% 的美容品牌也将获得更高的社交媒体声誉。 案例: Zepeto 在推特上发布了他们的人物风格,在短短六个小时内就有了自己的品牌人物。 5、旅游业 如果疫情能够有所缓解,酒店和旅游业很快就能从疫情的封闭影响下恢复,酒店业的行业收入可以超过 1900 亿美元,一旦疫情好转,将实现跨越式增长。 案例: Amalfiwhite 在ins上欢迎大家到英国选择他们的酒店, 精彩的Instagram 帖子吸引了很多的关注。 6.健康与健身 健康和健身品牌在社交媒体上发展迅速,其中包括来自全球行业博主的DIY 视频。到 2022 年底,健身行业的价值可以达到 1365.9 亿美元。 案例: Dan The Hinh在 Facebook 页面 发布了锻炼视频,这些健身视频在短短几个小时内就获得了 7300 次点赞和 11000 次分享。 健康和健身的热门标签 #health #healthylifestyle #stayhealthy #healthyskin #healthcoach #fitness #fitnessfreak #fitnessfood #bodyfitness #fitnessjourney 7.食品饮料业 在社交媒体上经常看到的内容类型就是食品和饮料,这一细分市场有着全网超过30% 的推文和60% 的 Facebook 帖子。 案例: Suerte BarGill 在社交媒体上分享调酒师制作饮品的视频,吸引人的视频让观看的人都很想品尝这种饮品。 食品和饮料的热门标签 #food #foodpics #foodies #goodfood #foodgram #beverages #drinks #beverage #drink #cocktails 8. 家居装饰 十年来,在线家居装饰迎来大幅增长,该利基市场的复合年增长率为4%。家居市场现在发展社交媒体也是最佳时机。 案例: Home Adore 在推特上发布家居装饰创意和灵感,目前已经有 220 万粉丝。 家居装饰的流行标签 #homedecor #myhomedecor #homedecorinspo #homedecors #luxuryhomedecor #homedecorlover #home #interiordesign #interiordecor #interiordesigner 9. 房地产 美国有超过200 万的房地产经纪人,其中70% 的人活跃在社交媒体上,加入社交媒体,是一个好机会。 案例: 房地产专家Sonoma County在推特上发布了一篇有关加州一所住宅的豪华图。房地产经纪人都开始利用社交媒体来提升销售额。 房地产的最佳标签 #realestate #realestatesales #realestateagents #realestatemarket #realestateforsale #realestategoals #realestateexperts #broker #luxuryrealestate #realestatelife 10. 牙科 到 2030年,牙科行业预计将飙升至6988 亿美元。 案例: Bridgewater NHS 在推特上发布了一条客户推荐,来建立患者对牙医服务的信任。突然之间,牙科似乎没有那么可怕了! 牙科的流行标签 #dental #dentist #dentistry #smile #teeth #dentalcare #dentalclinic #oralhealth #dentalhygiene #teethwhitening 11. 摄影 摄影在社交媒体中无处不在,持续上传作品可以增加作品集的可信度,当图片参与度增加一倍,覆盖范围增加三倍时,会获得更多的客户。 案例: 著名摄影师理查德·伯纳贝(Richard Bernabe)在推特上发布了他令人着迷的点击。这篇犹他州的帖子获得了 1900 次点赞和 238 次转发。 摄影的热门标签 #photography #photooftheday #photo #picoftheday #photoshoot #travelphotography #portraitphotography #photographylovers #iphonephotography #canonphotography 12. 技术 超过 55% 的 IT 买家会在社交媒体寻找品牌相关资料做出购买决定。这个数字足以说服这个利基市场中的任何人拥有活跃的社交媒体。 案例: The Hacker News是一个广受欢迎的平台,以分享直观的科技新闻而闻名。他们在 Twitter 上已经拥有 751K+ 的追随者。 最佳技术标签 #technology #tech #innovation #engineering #design #business #science #technew s #gadgets #smartphone 13.非政府组织 全球90% 的非政府组织会利用社交媒体向大众寻求支持。社交媒体会有捐赠、公益等组织。 案例: Mercy Ships 通过创造奇迹赢得了全世界的心。这是一篇关于他们的志愿麻醉师的帖子,他们在乌干达挽救了几条生命。 非政府组织的热门标签 #ngo #charity #nonprofit #support #fundraising #donation #socialgood #socialwork #philanthropy #nonprofitorganization 14. 教育 教育行业在过去十年蓬勃发展,借助社交媒体,教育行业有望达到新的高度。电子学习预计将在 6 年内达到万亿美元。 案例: Coursera 是一个领先的学习平台,平台会有很多世界一流大学额课程,它在社交媒体上的可以有效激励人们继续学习和提高技能。 最佳教育标签 #education #learning #school #motivation #students #study #student #children #knowledge #college 15. 医疗保健 疫情进一步证明了医疗保健行业的主导地位,以及挽救生命的力量。到 2022 年,该行业的价值将达到 10 万亿美元。 随着全球健康问题的加剧,医疗保健的兴起也将导致科技和制造业的增长。 案例: CVS Health 是美国领先的药房,积他们的官方账号在社交媒体上分享与健康相关的问题,甚至与知名运动员和著名人物合作,来提高对健康问题的关注度。 医疗保健的热门标签 #healthcare #health #covid #medical #medicine #doctor #hospital #nurse #wellness #healthylifestyle 大多数行业都开始尝试社交媒体,利用社交媒体可以获得更多的关注度和产品、服务的销量,在社交媒体企业和卖家,要关注标签的重要性,标签不仅能扩大帖子的覆盖范围,还能被更多人关注并熟知。 跨境企业和卖家可以通过使用流量高的标签了解当下人们词和竞争对手的受众都关注什么。 焦点LIKE.TG拥有丰富的B2C外贸商城建设经验,北京外贸商城建设、上海外贸商城建设、 广东外贸商城建设、深圳外贸商城建设、佛山外贸商城建设、福建外贸商城建设、 浙江外贸商城建设、山东外贸商城建设、江苏外贸商城建设...... 想要了解更多搜索引擎优化、外贸营销网站建设相关知识, 请拨打电话:400-6130-885。
2024年如何让谷歌快速收录网站页面?【全面指南】
2024年如何让谷歌快速收录网站页面?【全面指南】
什么是收录? 通常,一个网站的页面想要在谷歌上获得流量,需要经历如下三个步骤: 抓取:Google抓取你的页面,查看是否值得索引。 收录(索引):通过初步评估后,Google将你的网页纳入其分类数据库。 排名:这是最后一步,Google将查询结果显示出来。 这其中。收录(Google indexing)是指谷歌通过其网络爬虫(Googlebot)抓取网站上的页面,并将这些页面添加到其数据库中的过程。被收录的页面可以出现在谷歌搜索结果中,当用户进行相关搜索时,这些页面有机会被展示。收录的过程包括三个主要步骤:抓取(Crawling)、索引(Indexing)和排名(Ranking)。首先,谷歌爬虫会抓取网站的内容,然后将符合标准的页面加入索引库,最后根据多种因素对这些页面进行排名。 如何保障收录顺利进行? 确保页面有价值和独特性 确保页面内容对用户和Google有价值。 检查并更新旧内容,确保内容高质量且覆盖相关话题。 定期更新和重新优化内容 定期审查和更新内容,以保持竞争力。 删除低质量页面并创建内容删除计划 删除无流量或不相关的页面,提高网站整体质量。 确保robots.txt文件不阻止抓取 检查和更新robots.txt文件,确保不阻止Google抓取。 检查并修复无效的noindex标签和规范标签 修复导致页面无法索引的无效标签。 确保未索引的页面包含在站点地图中 将未索引的页面添加到XML站点地图中。 修复孤立页面和nofollow内部链接 确保所有页面通过站点地图、内部链接和导航被Google发现。 修复内部nofollow链接,确保正确引导Google抓取。 使用Rank Math Instant Indexing插件 利用Rank Math即时索引插件,快速通知Google抓取新发布的页面。 提高网站质量和索引过程 确保页面高质量、内容强大,并优化抓取预算,提高Google快速索引的可能性。 通过这些步骤,你可以确保Google更快地索引你的网站,提高搜索引擎排名。 如何加快谷歌收录你的网站页面? 1、提交站点地图 提交站点地图Sitemap到谷歌站长工具(Google Search Console)中,在此之前你需要安装SEO插件如Yoast SEO插件来生成Sitemap。通常当你的电脑有了SEO插件并开启Site Map功能后,你可以看到你的 www.你的域名.com/sitemap.xml的形式来访问你的Site Map地图 在谷歌站长工具中提交你的Sitemap 2、转发页面or文章至社交媒体或者论坛 谷歌对于高流量高权重的网站是会经常去爬取收录的,这也是为什么很多时候我们可以在搜索引擎上第一时间搜索到一些最新社媒帖文等。目前最适合转发的平台包括Facebook、Linkedin、Quora、Reddit等,在其他类型的论坛要注意转发文章的外链植入是否违背他们的规则。 3、使用搜索引擎通知工具 这里介绍几个搜索引擎通知工具,Pingler和Pingomatic它们都是免费的,其作用是告诉搜索引擎你提交的某个链接已经更新了,吸引前来爬取。是的,这相当于提交站点地图,只不过这次是提交给第三方。 4、在原有的高权重页面上设置内链 假设你有一些高质量的页面已经获得不错的排名和流量,那么可以在遵循相关性的前提下,适当的从这些页面做几个内链链接到新页面中去,这样可以快速让新页面获得排名
虚拟流量

                                 12个独立站增长黑客办法
12个独立站增长黑客办法
最近总听卖家朋友们聊起增长黑客,所以就给大家总结了一下增长黑客的一些方法。首先要知道,什么是增长黑客? 增长黑客(Growth Hacking)是营销人和程序员的混合体,其目标是产生巨大的增长—快速且经常在预算有限的情况下,是实现短时间内指数增长的最有效手段。增长黑客户和传统营销最大的区别在于: 传统营销重视认知和拉新获客增长黑客关注整个 AARRR 转换漏斗 那么,增长黑客方法有哪些呢?本文总结了12个经典增长黑客方法,对一些不是特别普遍的方法进行了延伸说明,建议收藏阅读。目 录1. SEO 2. 细分用户,低成本精准营销 3. PPC广告 4. Quora 流量黑客 5. 联合线上分享 6. 原生广告内容黑客 7. Google Ratings 8. 邮件营销 9. 调查问卷 10. 用户推荐 11. 比赛和赠送 12. 3000字文案营销1. SEO 查看 AdWords 中转化率最高的关键字,然后围绕这些关键字进行SEO策略的制定。也可以查看 Google Search Console 中的“搜索查询”报告,了解哪些关键字帮助你的网站获得了更多的点击,努力将关键词提升到第1页。用好免费的Google Search Console对于提升SEO有很大帮助。 使用Google Search Console可以在【Links】的部分看到哪个页面的反向连结 (Backlink)最多,从各个页面在建立反向连结上的优劣势。Backlink 的建立在 SEO 上来说是非常重要的! 在 【Coverage】 的部分你可以看到网站中是否有任何页面出现了错误,避免错误太多影响网站表现和排名。 如果担心Google 的爬虫程式漏掉一些页面,还可以在 Google Search Console 上提交网站的 Sitemap ,让 Google 的爬虫程式了解网站结构,避免遗漏页面。 可以使用XML-Sitemaps.com 等工具制作 sitemap,使用 WordPress建站的话还可以安装像Google XML Sitemaps、Yoast SEO 等插件去生成sitemap。2. 细分用户,低成本精准营销 针对那些看过你的产品的销售页面但是没有下单的用户进行精准营销,这样一来受众就会变得非常小,专门针对这些目标受众的打广告还可以提高点击率并大幅提高转化率,非常节约成本,每天经费可能都不到 10 美元。3. PPC广告PPC广告(Pay-per-Click):是根据点击广告或者电子邮件信息的用户数量来付费的一种网络广告定价模式。PPC采用点击付费制,在用户在搜索的同时,协助他们主动接近企业提供的产品及服务。例如Amazon和Facebook的PPC广告。4. Quora 流量黑客 Quora 是一个问答SNS网站,类似于国内的知乎。Quora的使用人群主要集中在美国,印度,英国,加拿大,和澳大利亚,每月有6亿多的访问量。大部分都是通过搜索词,比如品牌名和关键词来到Quora的。例如下图,Quora上对于痘痘肌修复的问题就排在Google搜索相关词的前列。 通过SEMrush + Quora 可以提高在 Google 上的自然搜索排名: 进入SEMrush > Domain Analytics > Organic Research> 搜索 quora.com点击高级过滤器,过滤包含你的目标关键字、位置在前10,搜索流量大于 100 的关键字去Quora在这些问题下发布回答5. 联合线上分享 与在你的领域中有一定知名度的影响者进行线上讲座合作(Webinar),在讲座中传递一些意义的内容,比如一些与你产品息息相关的干货知识,然后将你的产品应用到讲座内容提到的一些问题场景中,最后向用户搜集是否愿意了解你们产品的反馈。 但是,Webinar常见于B2B营销,在B2C领域还是应用的比较少的,而且成本较高。 所以大家在做海外营销的时候不妨灵活转换思维,和领域中有知名度的影响者合作YouTube视频,TikTok/Instagram等平台的直播,在各大社交媒体铺开宣传,是未来几年海外营销的重点趋势。6. 原生广告内容黑客 Native Advertising platform 原生广告是什么?从本质上讲,原生广告是放置在网页浏览量最多的区域中的内容小部件。 简单来说,就是融合了网站、App本身的广告,这种广告会成为网站、App内容的一部分,如Google搜索广告、Facebook的Sponsored Stories以及Twitter的tweet式广告都属于这一范畴。 它的形式不受标准限制,是随场景而变化的广告形式。有视频类、主题表情原生广告、游戏关卡原生广告、Launcher桌面原生广告、Feeds信息流、和手机导航类。7. Google Ratings 在 Google 搜索结果和 Google Ads 上显示产品评分。可以使用任何与Google能集成的电商产品评分应用,并将你网站上的所有评论导入Google系统中。每次有人在搜索结果中看到你的广告或产品页面时,他们都会在旁边看到评分数量。 8. 邮件营销 据外媒统计,80% 的零售行业人士表示电子邮件营销是留住用户的一个非常重要的媒介。一般来说,邮件营销有以下几种类型: 弃单挽回邮件产品补货通知折扣、刮刮卡和优惠券发放全年最优价格邮件通知9. 用户推荐 Refer激励现有用户推荐他人到你的独立站下单。举个例子,Paypal通过用户推荐使他们的业务每天有 7% 到 10%的增长。因此,用户推荐是不可忽视的增长办法。10. 调查问卷 调查问卷是一种快速有效的增长方式,不仅可以衡量用户满意度,还可以获得客户对你产品的期望和意见。调查问卷的内容包括产品体验、物流体验、UI/UX等任何用户购买产品过程中遇到的问题。调查问卷在AARRR模型的Refer层中起到重要的作用,只有搭建好和客户之间沟通的桥梁,才能巩固你的品牌在客户心中的地位,增加好感度。 11. 比赛和赠送 这个增长方式的成本相对较低。你可以让你的用户有机会只需要通过点击就可以赢得他们喜欢的东西,同时帮你你建立知名度并获得更多粉丝。许多电商品牌都以比赛和赠送礼物为特色,而这也是他们成功的一部分。赠送礼物是增加社交媒体帐户曝光和电子邮件列表的绝佳方式。如果您想增加 Instagram 粉丝、Facebook 页面点赞数或电子邮件订阅者,比赛和赠送会创造奇迹。在第一种情况下,你可以让你的受众“在 Instagram 上关注我们来参加比赛”。同样,您可以要求他们“输入电子邮件地址以获胜”。有许多内容可以用来作为赠送礼物的概念:新产品发布/预发售、摄影比赛、节假日活动和赞助活动。12. 3000字文案营销 就某一个主题撰写 3,000 字的有深度博客文章。在文章中引用行业影响者的名言并链接到他们的博文中,然后发邮件让他们知道你在文章中推荐了他们,促进你们之间的互动互推。这种增长办法广泛使用于B2B的服务类网站,比如Shopify和Moz。 DTC品牌可以用这样的增长办法吗?其实不管你卖什么,在哪个行业,展示你的专业知识,分享新闻和原创观点以吸引消费者的注意。虽然这可能不会产生直接的销售,但能在一定程度上影响他们购买的决定,不妨在你的独立站做出一个子页面或单独做一个博客,发布与你产品/服务相关主题的文章。 数据显示,在阅读了品牌网站上的原创博客内容后,60%的消费者对品牌的感觉更积极。如果在博客中能正确使用关键词,还可以提高搜索引擎优化及排名。 比如Cottonbabies.com就利用博文把自己的SEO做得很好。他们有一个针对“布料尿布基础知识”的页面,为用户提供有关“尿布:”主题的所有问题的答案。小贴士:记得要在博客文章末尾链接到“相关产品”哦~本文转载自:https://u-chuhai.com/?s=seo

                                 2021 Shopify独立站推广引流 获取免费流量方法
2021 Shopify独立站推广引流 获取免费流量方法
独立站的流量一般来自两个部分,一种是付费打广告,另外一种就是免费的自然流量,打广告带来的流量是最直接最有效的流量,免费流量可能效果不会那么直接,需要时间去积累和沉淀。但是免费的流量也不容忽视,第一,这些流量是免费的,第二,这些流量是长久有效的。下面分享几个免费流量的获取渠道和方法。 1.SNS 社交媒体营销 SNS 即 Social Network Services,国外最主流的 SNS 平台有 Facebook、Twitter、Linkedin、Instagram 等。SNS 营销就是通过运营这些社交平台,从而获得流量。 SNS 营销套路很多,但本质还是“眼球经济”,简单来说就是把足够“好”的内容,分享给足够“好”的人。好的内容就是足够吸引人的内容,而且这些内容确保不被人反感;好的人就是对你内容感兴趣的人,可能是你的粉丝,也可能是你潜在的粉丝。 如何把你想要发的内容发到需要的人呢?首先我们要确定自己的定位,根据不同的定位在社交媒体平台发布不同的内容,从而自己品牌的忠实粉丝。 1、如果你的定位是营销类的,一般要在社交媒体发布广告贴文、新品推送、优惠信息等。适合大多数电商产品,它的带货效果好,不过需要在短期内积累你的粉丝。如果想要在短期内积累粉丝就不可避免需要使用付费广告。 2、如果你的定位是服务类的,一般要在社交媒体分享售前售后的信息和服务,一般 B2B 企业使用的比较多。 3、如果你的定位是专业类科技产品,一般要在社交媒体分享产品开箱测评,竞品分析等。一般 3C 类的产品适合在社交媒体分享这些内容,像国内也有很多评测社区和网站,这类社区的粉丝一般购买力都比较强。 4、如果你的定位是热点类的,一般要在社交媒体分享行业热点、新闻资讯等内容。因为一般都是热点,所以会带来很多流量,利用这些流量可以快速引流,实现变现。 5、如果你的定位是娱乐类的:一般要在社交媒体分享泛娱乐内容,适合分享钓具、定制、改装类的内容。 2.EDM 邮件营销 很多人对邮件营销还是不太重视,国内一般都是使用在线沟通工具,像微信、qq 比较多,但是在国外,电子邮件则是主流的沟通工具,很多外国人每天使用邮箱的频率跟吃饭一样,所以通过电子邮件营销也是国外非常重要的营销方式。 定期制作精美有吸引力的邮件内容,发给客户,把邮件内容设置成跳转到网站,即可以给网站引流。 3.联盟营销 卖家在联盟平台上支付一定租金并发布商品,联盟平台的会员领取联盟平台分配的浏览等任务,如果会员对这个商品感兴趣,会领取优惠码购买商品,卖家根据优惠码支付给联盟平台一定的佣金。 二、网站SEO引流 SEO(Search Engine Optimization)搜索引擎优化,是指通过采用易于搜索引擎索引的合理手段,使网站各项基本要素适合搜索引擎的检索原则并且对用户更友好,从而更容易被搜索引擎收录及优先排序。 那 SEO 有什么作用嘛?简而言之分为两种,让更多的用户更快的找到他想要的东西;也能让有需求的客户首先找到你。作为卖家,更关心的是如何让有需求的客户首先找到你,那么你就要了解客户的需求,站在客户的角度去想问题。 1.SEO 标签书写规范 通常标签分为标题、关键词、描述这三个部分,首先你要在标题这个部分你要说清楚“你是谁,你干啥,有什么优势。”让人第一眼就了解你,这样才能在第一步就留住有效用户。标题一般不超过 80 个字符;其次,关键词要真实的涵盖你的产品、服务。一般不超过 100 个字符;最后在描述这里,补充标题为表达清楚的信息,一般不超过 200 个字符。 标题+描述 值得注意的是标题+描述,一般会成为搜索引擎检索结果的简介。所以标题和描述一定要完整表达你的产品和品牌的特点和优势。 关键词 关键词的设定也是非常重要的,因为大多数用户购买产品不会直接搜索你的商品,一般都会直接搜索想要购买产品的关键字。关键词一般分为以下四类。 建议目标关键词应该是品牌+产品,这样用户无论搜索品牌还是搜索产品,都能找到你的产品,从而提高命中率。 那如何选择关键词呢?拿我们最常使用的目标关键词举例。首先我们要挖掘出所有的相关关键词,并挑选出和网站自身直接相关的关键词,通过分析挑选出的关键词热度、竞争力,从而确定目标关键词。 注:一般我们都是通过关键词分析工具、搜索引擎引导词、搜索引擎相关搜索、权重指数以及分析同行网站的关键词去分析确定目标关键词。 几个比较常用的关键词分析工具: (免费)MozBar: https://moz.com (付费)SimilarWeb: https://www.similarweb.com/ 2.链接锚文本 什么是锚文本? 一个关键词,带上一个链接,就是一个链接锚文本。带链接的关键词就是锚文本。锚文本在 SEO 过程中起到本根性的作用。简单来说,SEO 就是不断的做锚文本。锚文本链接指向的页面,不仅是引导用户前来访问网站,而且告诉搜索引擎这个页面是“谁”的最佳途径。 站内锚文本 发布站内描文本有利于蜘蛛快速抓取网页、提高权重、增加用户体验减少跳出、有利搜索引擎判断原创内容。你在全网站的有效链接越多,你的排名就越靠前。 3 外部链接什么是外部链接? SEO 中的外部链接又叫导入链接,简称外链、反链。是由其他网站上指向你的网站的链接。 如何知道一个网站有多少外链? 1.Google Search Console 2.站长工具 3.MozBar 4.SimilarWeb 注:低权重、新上线的网站使用工具群发外链初期会得到排名的提升,但被搜索引擎发现后,会导致排名大幅度下滑、降权等。 如何发布外部链接? 通过友情链接 、自建博客 、软文 、论坛 、问答平台发布外链。以下几个注意事项: 1.一个 url 对应一个关键词 2.外链网站与自身相关,像鱼竿和鱼饵,假发和假发护理液,相关却不形成竞争是最好。 3.多找优质网站,大的门户网站(像纽约时报、BBC、WDN 新闻网) 4.内容多样性, 一篇帖子不要重复发 5.频率自然,一周两三篇就可以 6.不要作弊,不能使用隐藏链接、双向链接等方式发布外链 7.不要为了发外链去发外链,“好”的内容才能真正留住客户 4.ALT 标签(图片中的链接) 在产品或图片管理里去编辑 ALT 标签,当用户搜索相关图片时,就会看到图片来源和图片描述。这样能提高你网站关键词密度,从而提高你网站权重。 5.网页更新状态 网站如果经常更新内容的话,会加快这个页面被收录的进度。此外在网站上面还可以添加些“最新文章”版块及留言功能。不要只是为了卖产品而卖产品,这样一方面可以增加用户的粘性,另一方面也加快网站的收录速度。 6.搜索跳出率 跳出率越高,搜索引擎便越会认为你这是个垃圾网站。跳出率高一般有两个原因,用户体验差和广告效果差,用户体验差一般都是通过以下 5 个方面去提升用户体验: 1.优化网站打开速度 2.网站内容整洁、排版清晰合理 3.素材吸引眼球 4.引导功能完善 5.搜索逻辑正常、产品分类明确 广告效果差一般通过这两个方面改善,第一个就是真实宣传 ,确保你的产品是真实的,切勿挂羊头卖狗肉。第二个就是精准定位受众,你的产品再好,推给不需要的人,他也不会去看去买你的产品,这样跳出率肯定会高。本文转载自:https://u-chuhai.com/?s=seo

                                 2022,国际物流发展趋势如何?
2022,国际物流发展趋势如何?
受新冠疫情影响,从2020年下半年开始,国际物流市场出现大规模涨价、爆舱、缺柜等情况。中国出口集装箱运价综合指数去年12月末攀升至1658.58点,创近12年来新高。去年3月苏伊士运河“世纪大堵船”事件的突发,导致运力紧缺加剧,集运价格再创新高,全球经济受到影响,国际物流行业也由此成功出圈。 加之各国政策变化、地缘冲突等影响,国际物流、供应链更是成为近两年行业内关注的焦点。“拥堵、高价、缺箱、缺舱”是去年海运的关键词条,虽然各方也尝试做出了多种调整,但2022年“高价、拥堵”等国际物流特点仍影响着国际社会的发展。 总体上来看,由疫情带来的全球供应链困境会涉及到各行各业,国际物流业也不例外,将继续面对运价高位波动、运力结构调整等状况。在这一复杂的环境中,外贸人要掌握国际物流的发展趋势,着力解决当下难题,找到发展新方向。 国际物流发展趋势 由于内外部因素的影响,国际物流业的发展趋势主要表现为“运力供需矛盾依旧存在”“行业并购整合风起云涌”“新兴技术投入持续增长”“绿色物流加快发展”。 1.运力供需矛盾依旧存在 运力供需矛盾是国际物流业一直存在的问题,近两年这一矛盾不断加深。疫情的爆发更是成了运力矛盾激化、供需紧张加剧的助燃剂,使得国际物流的集散、运输、仓储等环节无法及时、高效地进行连接。各国先后实施的防疫政策,以及受情反弹和通胀压力加大影响,各国经济恢复程度不同,造成全球运力集中在部分线路与港口,船只、人员难以满足市场需求,缺箱、缺舱、缺人、运价飙升、拥堵等成为令物流人头疼的难题。 对物流人来说,自去年下半年开始,多国疫情管控政策有所放松,供应链结构加快调整,运价涨幅、拥堵等难题得到一定缓解,让他们再次看到了希望。2022年,全球多国采取的一系列经济恢复措施,更是缓解了国际物流压力。但由运力配置与现实需求之间的结构性错位导致的运力供需矛盾,基于纠正运力错配短期内无法完成,这一矛盾今年会继续存在。 2.行业并购整合风起云涌 过去两年,国际物流行业内的并购整合大大加快。小型企业间不断整合,大型企业和巨头则择机收购,如Easysent集团并购Goblin物流集团、马士基收购葡萄牙电商物流企业HUUB等,物流资源不断向头部靠拢。 国际物流企业间的并购提速,一方面,源于潜在的不确定性和现实压力,行业并购事件几乎成为必然;另一方面,源于部分企业积极准备上市,需要拓展产品线,优化服务能力,增强市场竞争力,提升物流服务的稳定性。与此同时,由疫情引发的供应链危机,面对供需矛盾严重,全球物流失控,企业需要打造自主可控的供应链。此外,全球航运企业近两年大幅增长的盈利也为企业发起并购增加了信心。 在经历两个年度的并购大战后,今年的国际物流行业并购会更加集中于垂直整合上下游以提升抗冲击能力方面。对国际物流行业而言,企业积极的意愿、充足的资本以及现实的诉求都将使并购整合成为今年行业发展的关键词。 3.新兴技术投入持续增长 受疫情影响,国际物流企业在业务开展、客户维护、人力成本、资金周转等方面的问题不断凸显。因而,部分中小微国际物流企业开始寻求改变,如借助数字化技术降低成本、实现转型,或与行业巨头、国际物流平台企业等合作,从而获得更好的业务赋能。电子商务、物联网、云计算、大数据、区块链、5G、人工智能等数字技术为突破这些困难提供了可能性。 国际物流数字化领域投融资热潮也不断涌现。经过近些年来的发展,处于细分赛道头部的国际物流数字化企业受到追捧,行业大额融资不断涌现,资本逐渐向头部聚集,如诞生于美国硅谷的Flexport在不到五年时间里总融资额高达13亿美元。另外,由于国际物流业并购整合的速度加快,新兴技术的应用就成了企业打造和维持核心竞争力的主要方式之一。因而,2022年行业内新技术的应用或将持续增长。 4.绿色物流加快发展 近年来全球气候变化显著,极端天气频繁出现。自1950年以来,全球气候变化的原因主要来自于温室气体排放等人类活动,其中,CO₂的影响约占三分之二。为应对气候变化,保护环境,各国政府积极开展工作,形成了以《巴黎协定》为代表的一系列重要协议。 而物流业作为国民经济发展的战略性、基础性、先导性产业,肩负着实现节能降碳的重要使命。根据罗兰贝格发布的报告,交通物流行业是全球二氧化碳排放的“大户”,占全球二氧化碳排放量的21%,当前,绿色低碳转型加速已成为物流业共识,“双碳目标”也成行业热议话题。 全球主要经济体已围绕“双碳”战略,不断深化碳定价、碳技术、能源结构调整等重点措施,如奥地利政府计划在2040年实现“碳中和/净零排放”;中国政府计划在2030年实现“碳达峰”,在2060年实现“碳中和/净零排放”。基于各国在落实“双碳”目标方面做出的努力,以及美国重返《巴黎协定》的积极态度,国际物流业近两年围绕“双碳”目标进行的适应性调整在今年将延续,绿色物流成为市场竞争的新赛道,行业内减少碳排放、推动绿色物流发展的步伐也会持续加快。 总之,在疫情反复、突发事件不断,运输物流链阶段性不畅的情况下,国际物流业仍会根据各国政府政策方针不断调整业务布局和发展方向。 运力供需矛盾、行业并购整合、新兴技术投入、物流绿色发展,将对国际物流行业的发展产生一定影响。对物流人来说,2022年仍是机遇与挑战并存的一年。本文转载自:https://u-chuhai.com/?s=seo
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LIKE.TG出海| 推荐出海人最好用的LINE营销系统-云控工具
LIKE.TG出海| 推荐出海人最好用的LINE营销系统-云控工具
在数字化营销的快速发展中,各种社交应用和浏览器为企业提供了丰富的营销系统。其中,LINE营销系统作为一种新兴的社交媒体营销手段,越来越受到企业的重视。同时,比特浏览器作为一种注重隐私和安全的浏览器,也为用户提供了更安全的上网体验。本文LIKE.TG将探讨这两者之间的相互作用,分析它们如何结合为企业带来更高效的营销效果。最好用的LINE营销系统:https://tool.like.tg/免费试用请联系LIKE.TG✈官方客服: @LIKETGAngel一、LINE营销系统概述LINE营销系统是指通过LINE平台开展的一系列营销活动。它利用LINE的即时通讯功能,帮助企业与客户建立紧密的联系。LINE营销系统的核心要素包括:1.群组和频道管理:企业可以创建和管理LINE群组与频道,实时与用户互动,分享产品信息、促销活动和品牌故事。2.用户数据分析:通过分析用户在LINE上的行为,企业能够获取市场洞察,优化产品与服务。3.自动化工具:利用LINE的API,企业可以创建自动化聊天机器人,提供24小时客户服务,提升用户体验。这种系统的优势在于其高效的沟通方式,使品牌能够快速响应客户需求,并通过个性化服务增强客户忠诚度。二、比特浏览器的特点比特浏览器是一款强调用户隐私和安全的浏览器,它在保护用户数据和提供优质上网体验方面具有明显优势。其特点包括:1.隐私保护:比特浏览器通过多重加密保护用户的浏览数据,防止个人信息泄露。2.去中心化特性:用户可以更自由地访问内容,而不受传统浏览器的限制。3.扩展功能:比特浏览器支持多种扩展,能够满足用户个性化的需求,比如广告拦截和隐私保护工具。比特浏览器的设计理念使得它成为那些关注隐私和安全用户的理想选择,这对企业在进行线上营销时,尤其是在数据保护方面提出了更高的要求。三、LINE营销系统与比特浏览器的互补作用 1.用户体验的提升 LINE营销系统的目标是通过即时通讯与用户建立良好的互动关系,而比特浏览器则为用户提供了一个安全的上网环境。当企业通过LINE进行营销时,用户使用比特浏览器访问相关内容,能够享受到更加安全、流畅的体验。这样的组合使得企业能够更好地满足用户的需求,从而提高客户的满意度和忠诚度。 2.数据安全的保障 在数字营销中,数据安全至关重要。企业在使用LINE营销系统收集用户数据时,面临着数据泄露的风险。比特浏览器提供的隐私保护功能能够有效降低这一风险,确保用户在访问企业页面时,个人信息不会被泄露。通过结合这两者,企业不仅能够进行有效的营销,还能够在用户中建立起良好的信任感。 3.营销活动的有效性 LINE营销系统可以帮助企业精准定位目标受众,而比特浏览器则使得用户在浏览营销内容时感受到安全感,这样的结合有助于提升营销活动的有效性。当用户对品牌产生信任后,他们更可能参与活动、购买产品,并进行二次传播,形成良好的口碑效应。四、实际案例分析 为了更好地理解LINE营销系统与比特浏览器的结合效果,我们可以考虑一个成功的案例。一家新兴的电商平台决定通过LINE进行一项促销活动。他们在LINE频道中发布了一系列关于新产品的宣传信息,并引导用户访问专门为此次活动设置的页面。 为了提升用户体验,该平台鼓励用户使用比特浏览器访问这些页面。用户通过比特浏览器访问时,能够享受到更安全的浏览体验,从而更加放心地参与活动。此外,平台还利用LINE的自动化工具,为用户提供实时的咨询和支持。 这一策略取得了显著的效果。通过LIKE.TG官方云控大师,LINE营销系统,电商平台不仅成功吸引了大量用户参与活动,转化率也显著提升。同时,用户反馈表明,他们在使用比特浏览器时感到非常安心,愿意继续关注该品牌的后续活动。五、营销策略的优化建议 尽管LINE营销系统和比特浏览器的结合能够带来诸多优势,但在实际应用中,企业仍需注意以下几点:1.用户教育:许多用户可能对LINE和比特浏览器的结合使用不够了解,因此企业应提供必要的教育和培训,让用户了解如何使用这两种工具进行安全的在线互动。2.内容的多样性:为了吸引用户的兴趣,企业需要在LINE营销中提供多样化的内容,包括视频、图文和互动问答等,使用户在使用比特浏览器时有更丰富的体验。3.持续的效果评估:企业应定期对营销活动的效果进行评估,了解用户在使用LINE和比特浏览器时的反馈,及时调整策略以提升活动的有效性。六、未来展望 随着数字营销的不断演进,LINE营销系统和比特浏览器的结合将会变得越来越重要。企业需要不断探索如何更好地利用这两者的优势,以满足日益增长的用户需求。 在未来,随着技术的发展,LINE营销系统可能会集成更多智能化的功能,例如基于AI的个性化推荐和精准广告投放。而比特浏览器也可能会进一步加强其隐私保护机制,为用户提供更为安全的上网体验。这些发展将为企业带来更多的营销机会,也将改变用户与品牌之间的互动方式。 在数字化营销的新时代,LINE营销系统和比特浏览器的结合为企业提供了一个全新的营销视角。通过优化用户体验、保障数据安全和提升营销活动的有效性,企业能够在激烈的市场竞争中占据优势。尽管在实施过程中可能面临一些挑战,但通过合理的策略,企业将能够充分利用这一结合,最终实现可持续的发展。未来,随着技术的不断进步,这一领域将继续为企业提供更多的机会与挑战。免费使用LIKE.TG官方:各平台云控,住宅代理IP,翻译器,计数器,号段筛选等出海工具;请联系LIKE.TG✈官方客服: @LIKETGAngel想要了解更多,还可以加入LIKE.TG官方社群 LIKE.TG生态链-全球资源互联社区。
LIKE.TG出海|kookeey:团队优选的住宅代理服务
LIKE.TG出海|kookeey
团队优选的住宅代理服务
在当今互联网时代, 住宅代理IP 已成为许多企业和团队绕不开的技术工具。为了确保这些代理的顺利运行,ISP白名单的设置显得尤为重要。通过将 住宅代理IP 添加至白名单,可以有效提升代理连接的稳定性,同时避免因网络限制而引发的不必要麻烦。isp whitelist ISP白名单(Internet Service Provider Whitelist)是指由网络服务提供商维护的一组信任列表,将信任的IP地址或域名标记为无需进一步检查或限制的对象。这对使用 住宅代理IP 的用户尤其重要,因为某些ISP可能对陌生或不常见的IP流量采取防护措施,从而影响网络访问的速度与体验。二、设置isp whitelist(ISP白名单)的重要性与优势将 住宅代理IP 添加到ISP白名单中,不仅能优化网络连接,还能带来以下显著优势:提升网络连接稳定性ISP白名单能够有效避免IP地址被错误标记为异常流量或潜在威胁,这对使用 住宅代理IP 的团队而言尤为重要。通过白名单设置,网络通信的中断率将显著降低,从而保证代理服务的连续性。避免验证环节在某些情况下,ISP可能会针对未知的IP地址触发额外的验证流程。这些验证可能导致操作延迟,甚至直接限制代理的功能。而通过将 住宅代理IP 纳入白名单,团队可以免除不必要的干扰,提升工作效率。增强数据传输的安全性白名单机制不仅可以优化性能,还能确保流量来源的可信度,从而降低网络攻击的风险。这对于依赖 住宅代理IP 处理敏感数据的企业来说,尤为重要。三、如何将住宅代理IP添加到ISP白名单添加 住宅代理IP 到ISP白名单通常需要以下步骤:确认代理IP的合法性在向ISP提交白名单申请前,确保代理IP来源合法,且服务商信誉良好。像 LIKE.TG 提供的住宅代理IP 就是一个值得信赖的选择,其IP资源丰富且稳定。联系ISP提供支持与ISP的技术支持团队联系,说明将特定 住宅代理IP 添加到白名单的需求。多数ISP会要求填写申请表格,并提供使用代理的具体场景。提交必要文档与信息通常需要提交代理服务的基本信息、IP范围,以及使用目的等细节。像 LIKE.TG 平台提供的服务,可以帮助用户快速获取所需的相关材料。等待审核并测试连接在ISP完成审核后,测试 住宅代理IP 的连接性能,确保其运行无异常。四、为何推荐LIKE.TG住宅代理IP服务当谈到住宅代理服务时, LIKE.TG 是业内的佼佼者,其提供的 住宅代理IP 不仅数量丰富,而且连接速度快、安全性高。以下是选择LIKE.TG的几大理由:全球覆盖范围广LIKE.TG的 住宅代理IP 覆盖全球多个国家和地区,无论是本地化业务需求,还是跨国访问,都能轻松满足。高效的客户支持无论在IP分配还是白名单设置中遇到问题,LIKE.TG都能提供及时的技术支持,帮助用户快速解决难题。灵活的定制服务用户可根据自身需求,选择合适的 住宅代理IP,并通过LIKE.TG的平台进行灵活配置。安全与隐私保障LIKE.TG对数据安全有严格的保护措施,其 住宅代理IP 服务采用先进的加密技术,确保传输过程中的隐私无忧。五、ISP白名单与住宅代理IP的完美结合将 住宅代理IP 纳入ISP白名单,是提升网络效率、保障数据安全的关键步骤。无论是出于业务需求还是隐私保护,选择优质的代理服务商至关重要。而 LIKE.TG 提供的住宅代理服务,以其卓越的性能和优质的用户体验,成为团队和企业的理想选择。如果您正在寻找稳定、安全的 住宅代理IP,并希望与ISP白名单功能完美结合,LIKE.TG无疑是值得信赖的合作伙伴。LIKE.TG海外住宅IP代理平台1.丰富的静/动态IP资源/双ISP资源提供大量可用的静态和动态IP,低延迟、独享使用,系统稳定性高达99%以上,确保您的网络体验流畅无忧。2.全球VPS服务器覆盖提供主要国家的VPS服务器,节点资源充足,支持低延迟的稳定云主机,为您的业务运行保驾护航。3.LIKE.TG全生态支持多平台多账号防关联管理。无论是海外营销还是账号运营,都能为您打造最可靠的网络环境。4.全天候技术支持真正的24小时人工服务,专业技术团队随时待命,为您的业务需求提供个性化咨询和技术解决方案。免费使用LIKE.TG官方:各平台云控,住宅代理IP,翻译器,计数器,号段筛选等出海工具;请联系LIKE.TG✈官方客服: @LIKETGAngel想要了解更多,还可以加入LIKE.TG官方社群 LIKE.TG生态链-全球资源互联社区/联系客服进行咨询领取官方福利哦!
LIKE.TG出海|Line智能云控拓客营销系统   一站式营销平台助您实现海外推广
LIKE.TG出海|Line智能云控拓客营销系统 一站式营销平台助您实现海外推广
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