An advertising message is an idea an advertiser wants to communicate to their target audience. Its goal is to convince people to perform a certain action, such as registering, purchasing something, or making a reservation. Why is an advertising message important? It allows you to skyrocket your brand awareness It helps keep your customers engaged It educates your prospects It helps you understand your audience It increases your sales An advertising message is a bridge between your brand and its clients and prospects. Communicating the right idea at the right time and to the right people allows brands to build trustful and long-lasting relationships with their audience. Therefore, developing a killer advertising message can provide businesses with a lot of tangible benefits. Check them out below. It allows you to skyrocket your brand awareness. Well-thought-out advertising campaigns go straight to the hearts of your clients. Companies that go beyond selling enjoy better brand awareness and recognition. For example, Canon launched a #selfieless campaign on Christmas Eve, which was symbolic. They encouraged people from all over the world to capture not themselves but their good deeds to share altruistic vibes and inspire. It helps keep your customers engaged. Clients can stop buying from you just because you don’t interact with them. Hence, they simply forget about your company. However, targeted and highly relevant promotional offers will definitely strike the right chord with your audience. They will appreciate that you know their problems and offer the best solutions. It educates your prospects. Leads are not ready to buy immediately. They may not fully understand the way your service works or how to make the most out of your products. Besides, they can get stuck at any stage of the sales funnel. Via an advertising message, you can offer them solutions to cope with their problems. As a result, you’ll be in the right place at the right time. It helps you understand your audience. Analyzing the way your leads and customers interact and respond to your messages will provide valuable insights. You’ll get to know which offers, channels, and tactics work better for your audience. It increases your sales. Each benefit listed above results in a sales increase. If you communicate your message to the right people via the channels they prefer, you can boost sales. Advertising during holidays, clearances, and other important occasions will work in your favor. Now you know the benefits you can get by using advertising messages. It’s time to move on and explore the components of a killer ad. Components of an Advertising Message It doesn’t matter whether you choose online or offline advertising. The structure will be the same because the nature of human perception predetermines it. Let’s break it down into five components. A headline. This is the first advertisement element a user notices. It should be eye-catching and informative since it is usually placed at the top of an ad. A headline conveys the main idea of an ad, so marketers need to do their best to create a selling headline. It can be presented as a promise, breaking news, a command, or a question. To make readers read further, it should be short and unambiguous. A sub-headline. The advertisers sometimes omit this element, but you can’t underestimate its power. A sub-headline can support and continue your headline. It’s usually placed right under it, so it’s difficult to leave it unnoticed. If your headline is a question, a sub-headline can be the answer. These two elements complement each other perfectly. A sub-headline may be longer than a headline — up to a sentence. Copy. An advertising message copy should answer all the possible questions a user may have after reading your headline and sub-headline. This is just the place to showcase your product value. Consider performing a thorough analysis of your target audience to sort out the most important benefits that can help solve your clients’ problems and sell your product. Images. This element grabs second place in attracting user attention to an ad after a headline. Images assist you in visualizing your product, demonstrating its benefits, creating associations, and keeping your customers interested. Call-to-action. CTA is an action you want users to take after reading your advertising message. It should tell them directly what they should do next to evaluate the benefits of your product. It can say “Buy”, “Visit a website”, “Book a seat”, “Reserve a ticket.” These are the main components each ad message contains. You can include more elements, but you have to ensure they don’t distract users’ attention from your message. The shorter — the better. Now, we’ll share some tips that will help you create a killer ad. How to Create an Advertising Message Creating a perfect headline and adding a nice image is not enough. If you create an ad for the first time, you should go through more steps to craft a highly relevant and targeted message. Follow them below. Analyze your target audience. You can create a great ad in terms of marketing, but it won’t pay off if you target it to the wrong person. Analyzing your audience will provide you with valuable insights. Find out who your target market is, get to know the gender and client’s average age, and learn their social status, education, income level, or place of living. Answering these general questions will make you familiar with your audience and uncover the best promotional channels. Identify your customers’ pains. Every client chooses your brand because they believe you’re the best choice to solve their problem. Consider conducting research to identify your customers’ pain points. You can do this with the help of interviews, polls, surveys, etc. Get to know which unique features they need and can’t find among your competitors’ offers. This technique may be the key to your success. Select the best advertising channel. Your target audience analysis will tell you about the channels they prefer so that you can invest in them without wasting your budget. You can create ads for social media, paid Google Ads, run retargeting campaigns to reach leads already interested in your product, collaborate with your partners to develop native advertising, and promote your product in YouTube videos. All of these formats are not free. We recommend that you start with email campaigns. LIKE.TG allows you to send promotional emails to 500 subscribers every month for free. You can educate your audience and showcase your product from the best angles via emails. Master each component of an advertising message. Avoid using clickbait headlines since they can be ambiguous and scare away your audience. Brainstorm all your product’s benefits and include the most attractive ones in your ad copy. Design them as a list so that your users can easily perceive them. Make high-quality photos of your product to add them to your message. Craft a clear call to action. It should be the final chord of your ad. Make sure the link works and is easy to click. Analyze the results and act on them. Track how users interact with your ads. For example, if you promote your product via email campaigns, a low open rate shows that you’ve failed to create an attractive subject line; low CTR indicates that your message doesn’t resonate with the audience or hasn’t met your subscribers’ expectations. These findings will help you improve your strategy. Now, let’s take a look at some excellent examples of advertising messages. Advertising Message Examples In this section, we’ll share some examples of advertising messages from different online channels to help you get inspired. Sephora Sephora ran an ad campaign on Facebook to target the users who visited their site and viewed products but left without buying. They focused on Americans interested in makeup and skincare. The brand collected the products a prospect likes according to their behavior on Sephora’s site. After a user taps a specific product, they are redirected to the corresponding site category. This way, Sephora had reached leads who had not been ready to purchase and increased their sales volume. Chain Reaction Cycles The brand offers its email subscribers to get free tires with several wheelsets. They add customers’ reviews to make people trust them. Subscribers can click “Shop now,” and they will be redirected to the necessary item on their site. Chelsea FC If you own a physical store, SMS marketing is just for you. Ask your clients to share their phone numbers to receive messages about hot deals, upcoming sales, and special offers for their birthday. This way, you’ll retain customers, increase their loyalty, and boost sales. Chelsea FC does that right. Check out below. Congrats, now you know pretty much everything about advertising online. Follow our tips, get inspired by the examples, and make use of LIKE.TG marketing tools that will help bring your business to the next level.

Advocacy advertising is an ad type that implies promoting an issue or interests of a group rather than products or services compared to commercial advertising. The goal of advocacy advertising is to draw the audience’s attention to the problem and increase awareness. Advocacy advertising is often focused on economic, political, social, and environmental issues. For example, you’ve definitely run across ads asking people to take part in campaigns devoted to reducing toxic waste dumped into a sea or river. Another famous example is raising funds for scientific research that aims to develop drugs to cure AIDS or cancer. Now you can be interested in people involved in creating advocacy advertising. They are mostly non-profits, corporations, and lobbyists, but they could be any interested advocacy groups. Sometimes several areas are engaged in advocacy advertising. For example, a local candidate can express their opinion supporting scientific research that aims to create drugs to combat AIDS. Afterward, advocacy groups turn to this candidate in order to receive some funding. You can often see this move during the elections, especially when opposing candidates look for extra techniques to win the votes. Now that you understand how advocacy advertising works, let’s discover its advantages. Advantages of Advocacy Advertising The most significant advantage is exposure. This type of ad allows everyone who is concerned about a certain issue to find like-minded people all around the world and motivate them to action. This, in its turn, enables advocacy groups to increase awareness. Traditional advocacy advertising channels include radio, newspapers, and magazines, but now they are outdated, less effective, and too expensive. Hence, people look for channels that require little investment. And these channels do exist. Social media channels contribute the most to promoting advocacy ads. They need little money, reach a wide audience immediately, and provide a huge choice of formats available. Facebook and Instagram are the most popular channels to start with. With email campaigns, you can send personalized messages to specific segments of people interested in a certain issue. LIKE.TG allows you to send 15,000 emails to 500 subscribers each month for free. Lastly, you can create your website or a landing page devoted to a particular problem. It will help you collect the email addresses of those who are interested and ask them for donations. With LIKE.TG, you can create a landing page or link page for your social media bio without any coding skills and for free. Now that you know about the benefits and channels that can assist you, get inspired with some examples. Examples of Advocacy Advertising The advocacy ad below encourages car drivers to mind people riding bikes on the road since this confrontation is eternal. The girl with tire marks on her face immediately draws attention and makes us think about the problem of bike safety. Being well-equipped doesn’t guarantee your safety on the road. This advocacy ad encourages people to respect the Earth by turning off the lights during Earth Hour. This campaign is run every year, and it makes people become more aware of their environmental responsibility. The last example of an advocacy ad deals with the problem of domestic violence. It encourages women not to keep silent. In fact, there are centers supporting women who suffer from domestic abuse in almost every town. They can just call and ask for help. Congrats, now you know how advocacy advertising works and have some examples that prove its importance. If you are concerned with a certain issue, there are definitely at least a hundred people in the world who are ready to support you and fight this problem together, so don’t keep silent!

Affiliate marketing is an online marketing system that allows any online business to affiliate themselves with website owners (affiliates) using affiliate programs. Affiliates make money from new leads and customers, and by generating sales and traffic for the business. Follow this step-by-step guide below to get started with affiliate marketing today. Merchants who sell goods and services online use one of the following commission payment models: Cost Per Sale Cost Per Lead Cost Per Click Sometimes, affiliate marketing is confused with referral marketing, because both systems are used to generate sales. With LIKE.TG, the system of affiliate marketing is not applicable, but we have a referral program that allows you to earn money when you invite your friends to use our service. Immediately after you become our subscriber you will receive a unique link. You can send it to people who are interested in using LIKE.TG. They’ll appreciate your offer because we have one platform for both email marketing and transactional messages. It is possible to send emails, webpush notifications, and SMS messages. If they agree to be partners you then will receive $50 off your first five payments and get a $50 bonus added to your account. This affiliate marketing program gives financial motivations to LIKE.TG’s subscribers. Don’t miss a chance to receive bonuses in your account. Invite your friends, colleagues, and partners to use LIKE.TG. Let’s get benefits together! Learn about this program here.

An affiliate program is a program that implies a company paying money to an opinion leader or another company to increase its website traffic and sales through social media posts, product integration, or some other types of content. An affiliate receives a commission from each purchase recorded through a unique affiliate link. In this article, we’ll uncover how affiliates make money and review types of affiliate programs. We’ll also provide a guide on creating an effective affiliate program and some great examples. How do affiliates make money? If you are considering an affiliate program for your business for the first time, you need to explore its peculiarities first. In this section, we’ll uncover how affiliates make money by promoting businesses and making sales. The revenue of every affiliate depends on the clicks and purchases of customers. A company provides an affiliate (another business or influencer) with a unique link called an affiliate link. With the help of this link and cookies, a business can monitor how many people have clicked the link and how many of them have completed the checkout process. After clicking an affiliate link, users should buy items during a specific timeframe given to an affiliate. An affiliate company receives a commission for every completed purchase. A brand and affiliate decide on the sum of money provided for every sale. It can be a predefined sum or a percentage from a sale. Businesses use cookies to track the activity of users. Each cookie has a specific length for monitoring the customer’s activity and actions. That’s why an affiliate should encourage a referral to purchase within this time. For instance, a cookie can monitor a user’s activity for 15 days. That’s why an affiliate should strive to complete sales until an affiliate link expires. Once this period ends, cookies can’t track customers’ actions any longer, so an affiliate can’t get paid. To receive proper commissions, affiliates should always operate within a given timeframe. Now that you know how affiliates make money, it’s time to explore the existing types of affiliate programs. Types of Affiliate Programs You can consider different types of affiliate programs depending on your key goals, target audience, and preferred communication channels. Before choosing a program, you need to identify platforms where your consumers prefer to spend their spare time. After conducting short research, you’ll be able to find out which type of content your audience reads most often. Afterward, you can identify the most relevant program. So, in this section, we’ll review four main types of programs that will be helpful for your product promotion. Cooperation with influencers. Nowadays you can find promoters in every industry. So, if you know some influencers with the same target audience, you can ask for a partnership. Since 61% of customers trust recommendations from opinion leaders, we can conclude that bloggers and social media influencers have a huge impact on people’s shopping decisions. For example, if you sell beauty products, an influencer can include your items in the next tutorial in Instagram stories as a recommended product for different skin types. As a result, viewers become interested in the product and click the link to explore available products, pricing, and their value. If prospects purchase, influencers receive revenue. Email marketing. Sending email campaigns to people who don’t need your product is useless. Yet bulk emails are effective when sent to the right people. To reach such an audience, consider various partnerships with appropriate businesses. For example, if you have a social media listening tool, you can cooperate with marketing agencies. When they send email campaigns to their customers, they can include a link to your product. If these users find your product helpful, they will use it to their advantage. As a result, your audience will expand. Coupon websites. Consider partnering with coupon websites when your product isn’t selling quickly within your market. This partnership lasts for a specific time and helps you promote your product. This way, you can get the first customers who will visit your website and become word-of-mouth promoters of your product. Search affiliates. This type of affiliate program implies partnering with freelancers who promote your brand in search results or other influential online advertising platforms. You should control whether this person or business complies with your advertising guidelines during this time. This partnership is more beneficial if your partner has a background in SEO and can launch an A/B test to identify ads that bring many referrals for your business. This step is essential for your partner since it helps determine ads with the highest ROI. Review websites. Costly products often require a special approach from brands and customers. When consumers want to buy an expensive product, they need to explore information, features, benefits, and reviews. Usually, such research needs more time. Consumers look for review sites to see if comments are positive. To bring more sales to your business, you need to contact review sites that have reviews of your product. Ask these businesses to cooperate with you by adding a referral link to the text about your product. As a result, both of you benefit. Now that you know the types, it’s time to proceed to the guide on developing an effective program. We’ll provide you with the steps you should take to obtain great results. How to create an effective affiliate program? After determining a relevant program for your company, it’s time to jump into its implementation. We’ll provide a step-by-step guide on how to do it right. Determine your goals. Before identifying your goals, make sure that you have a buyer persona. It will set the direction for your goals. Think of the people you want to reach and define the best approach to do it. Once you finish with the audience, proceed to measurable goals. Identify and set your KPIs to determine whether your program is effective and assess its benefits. You need to determine the sum of money you want your affiliates to bring each month, quarter, and year. Besides, set objectives for conversion rates. To track the effectiveness of the program, you should know how many people you want to convert into customers. Consider determining your expectations regarding clicks, average order value, leads, numbers of unique affiliate sales, and impressions. Look through rivals’ programs. Although you might know the strength and weaknesses of your competitors, you still need to do research focusing on affiliate programs. Search for at least a few rivals with the same audience and affiliate programs. Analyze what programs and how they implement them. This will help you set the direction of your future program and reach your target audience. Identify the structure of their affiliate commission (actions that trigger the payout, types of payments to affiliates, the amount of commissions, etc.). Afterward, see how competitors search for affiliates and what terms and conditions they provide. Once you figure everything out, your final step is a SWOT analysis. You’ll need to identify each affiliate program's strengths, weaknesses, opportunities, and threats. Define affiliate commission. The next step implies defining the sum of money you are willing to pay your affiliates, called commission. See competitors’ commissions to decide which commission will be enough for promoters. Make sure that the business or individual that will promote your product receives money for sales. There’s no need to pay for clicks or impressions since it won’t generate revenue. Make sure to negotiate these points. When determining a commission, you need to take care of your company’s interests first. Calculate customer acquisition costs, retention rate, and average customer lifetime value to find appropriate payment. You have two options to pay: set a specific sum of money for each sale or offer a percentage of a sale. Besides, you need to decide whether you are ready to pay in cash or store credit. Outline program structure. Once you are done with a commission, it’s time to determine your program structure. First, find the best ways for affiliates to distribute links to your product. You need to conduct research to identify the channels your prospects prefer the most. These will be the platforms for affiliates to share links. Secondly, identify the timeframe for the activity of your cookies. Thirdly, consider the terms and conditions that will apply to this program. You should also figure out the competitive advantage of your affiliate program that will help it stand out from the competition. Finally, decide whether you need an affiliate manager for this program and search for this person. Search for affiliates. To search for affiliates, you need to use an existing network of affiliates or create your own. With an existing network, you’ll be able to find an affiliate quickly but for a specific commission. This way, you’ll need to pay twice: finder’s free (up to 30%) and commission for the affiliate. Say you pay $10,000 per month to your affiliate, so you’ll need to pay an extra $3,000 to your affiliate network for their services. If this way is too costly for you, consider forming your own network of affiliates. You’ll need affiliate marketing software to monitor the performance of your affiliates. However, it’ll still cost less than an existing network. To find affiliates, write a list of bloggers, opinion leaders, and social media influencers that share the same audience and have a strong impact on people. Choose industry leaders with a good reputation and the trust of the masses. Contact these people and ask for partnership. Consider placing an open affiliate application so that potential affiliates contact you for more information. Monitor your program progress. Use affiliate program software to instantly track your results and program effectiveness. It’ll provide you with insights into your sales. You’ll be updated on the most successful affiliates. You’ll see whether you meet your measurable goals as well. Now that you know how to incorporate an affiliate program, let’s see some excellent examples from famous brands. It’ll serve you as an inspiration for your future program. Best Affiliate Programs In this section, we’ll uncover some amazing examples of affiliate programs from famous brands. They will help you find the best solution for your business. Fiverr Fiverr is a huge online marketplace for freelancers. The service helps businesses find the right candidates and freelancers get their desired jobs. The platform has an affiliate program on its website, so anyone can join it. Individuals and businesses start for free while having access to all tools Fiverr provides. Afterward, these people should share words with their followers about Fiverr and promote it. Once traffic converts, affiliates receive commissions. Since the company has different products to offer, the payouts depend on that product. Depending on the category a customer buys from, affiliates receive various commissions, ranging from $15 to $150. Elementor Elementor is a software company that helps build websites. Its drag-and-drop editor enables users to design sites with ease. The company offers a powerful affiliate program to individuals with the same niche and audience. The program is available for everyone with a strong online presence ready to generate conversions for Elementor. The platform invites content creators, web educators, marketing agencies, and digital entrepreneurs to join the program and become brand advocates. Elementor offers great payouts. Affiliates get a 50% commission from every purchase. The company pays automatically through PayPal or other preferred payment methods. iSpring iSpring is a platform that empowers users to build interactive learning materials like courses, quizzes, dialog simulations, screencasts, and video lectures. The company invites affiliates to participate in its program and bring new customers. The service encourages brand advocates and industry leaders to spread information about iSpring Suite to readers and receive a 10% commission for each purchase referral completed. The affiliate program is simple, and individuals and businesses can apply for free. The company looks for people who have experience in eLearning, understand iSpring software, and possess a network of iSpring prospects. Affiliates fill in a short application form on the platform’s website and wait for a response. The chosen candidates should use the unique link provided by the company to recommend iSpring on affiliates’ websites or social media. Once there’s a sale, an affiliate gets paid. Congrats, now you know what affiliate marketing is and why it’s essential. Hope that our examples inspire you to launch your own program soon.

After-sales service is the support a company provides to customers after they’ve already bought its product. It encompasses customer onboarding, customer service, product training, warranty service, returns, and exchanges. After-sales service enables brands to increase customer satisfaction, customer loyalty, and encourage word-of-mouth promotion. In this article, we’ll uncover the importance, types, and examples of after-sales service. We’ll also review tips for the effective implementation of the strategy. Why is after-sales service important? Although improving the pre-sales experience is more profitable, businesses should ensure excellent support for customers who have already purchased a product since the prosperity of a company depends on regular clients. After-sales support allows brands to benefit. According to statistics, 96% of consumers will leave if a company provides bad customer service. However, if it succeeds in giving customers the after-sales service they deserve, this business can increase customer retention and encourage people to buy again. As a result, a company can obtain repeat customers. After-sales service is essential for enhancing a customer base. Happy customers can recommend a company to their friends and refer them. Satisfied clients are the most valuable asset since they have higher customer lifetime value. Making clients happy with after-sales support ensures new leads from referral programs and more sales. Since an excellent post-sales experience impacts a brand image, businesses can use it to their advantage. If entrepreneurs build a positive brand image with after-sales support, they can boost conversions, establish customer loyalty, and reduce customer churn. Now that you know the reasons to consider after-sales service for your business, let’s review the types. 5 Types of After-Sales Service You can implement various types of after-sales services to meet your marketing goals. We’ll review the most commonly used strategies to increase customer satisfaction. User training. This type is essential for customers since it helps them figure out how to use your product. Users can understand how everything works with your customer onboarding, training, and educational materials. Onboarding will ensure smooth promotion down the funnel. Online support. For companies involved in eCommerce, it’s a must to provide online support to assist customers when they face problems with a product. Clients can reach businesses through various communication channels. They can call support, use a chatbot, or social media networks to get help. Warranty services. Almost every company provides warranty services for their customers. These might include replacing an item or specific parts of a product during the specified period. For example, Apple provides its customers with a one-year warranty and 90 days of technical support. Upgrades. Companies often upgrade software so that devices can be compatible and serve their owners longer. For example, Apple users can upgrade their software for 4-5 years. Free replacement. Brands offer to change the product if it doesn’t work properly. Consumers can get a free replacement for a limited time. Now that you know the types, let’s proceed to the tips for effective after-sales service. 4 Tips for Effective After-Sales Service Customer loyalty is an essential part of every business's success. We’ve prepared 4 tips to encourage loyalty in the post-sales period. Provide customers with discounts. Enticing offers always help raise the interest of the target audience. Discounts are effective in the after-sales period. Creating coupons, special offers, and discounts for your customers demonstrates that you care about your audience. Consider creating campaigns for customers who show interest in your products. Ensure excellent customer support. You should always keep in touch with customers and be ready to assist them. After-sales service requires you to ensure clients with seamless customer service. Use different communication channels so that consumers can contact you any time they face problems. Make use of LIKE.TG’s chatbots for Facebook, WhatsApp, Instagram, and Telegram to provide clients with instant help and answer basic questions about customer onboarding, installation, features, and updates. Ask for feedback. After clients purchase, ask them about their experience. Customer opinion is crucial for making your business better. With customer surveys, you can evaluate your product’s value, and customer satisfaction and identify problems that need to be eliminated. You need to select the perfect timing after customers perform a purchase with your company. Ask clients about their experience at least a week after they buy your product. Send relevant content. Consider email marketing to send content that might interest your customers. Your email campaigns can include updates about new releases, discounts, bonuses, and special promotions. They also show your care and let you stay in touch with your clients. Besides, it encourages customer loyalty. With LIKE.TG, you can develop email campaigns to your liking and send them to selected customers at perfect timing. You don’t need any special skills and knowledge of code to create stunning campaigns. Our drag-and-drop editor and free email templates will help you develop them fast based on your requirements. Moreover, you can inform clients about new releases via chatbots, SMS, and web push notifications. Now it’s time to jump into the examples. Examples of Good After-Sales Service Many small and big brands provide excellent post-sale service. They succeed in ensuring a great customer experience and providing visible value for consumers. Let’s review several examples of companies that managed to incorporate outstanding after-sales service. EPIC Bar The company takes care of its customers after the sale. There was a case of one of its consumers receiving a bad bar in a box. After contacting customer service, this client had a replacement of a product, additional bars, and a coupon for further purchases. Moreover, the company’s customer service sent a hand-written note showing its care. As a result, customers can feel appreciation and stay loyal. Suzuki Suzuki, famous for its powerful motorcycles, provides support through its post-sale programs. Excellent user experience and customer support provide the company with amazing customer satisfaction. Suzuki’s team cares about its customers and makes everything possible for them to purchase a great product that will serve them for years. The brand supports clients with Suzuki’s Guaranteed Asset Protection, Extended Protection, and Tire & Wheel program. Congrats, now you know the tips and examples to ensure a seamless after-sales experience for your customers. Make use of them to develop excellent post-sale support.

Agile marketing is a marketing approach in which self-organized and cross-functional teams perform tasks and receive instant feedback on their work. It helps companies focus on customer value, brand outcomes, and learning through experiments. This article will uncover the importance and benefits of agile marketing. We’ll also unveil how to implement it. Why is agile marketing important? Incorporating agile marketing into your business is essential since it helps ensure communication and coordination between departments. Shareholders are instantly updated on the project’s progress and can see every step a team takes to succeed. Agile methodology is transparent, so you can see project development and bring improvement. Tests and experiments allow you to find the best solution for your project and provide the best result to consumers. As a result, customer satisfaction improves. Experiments enable your teams to learn something useful for your project and avoid the same mistakes in the future. Productivity and efficiency of your departments increase. Collaboration between teams empowers you with the insights important for creating a valuable product. Now that you know the importance, it’s time to proceed to the benefits. After exploring the pros, you’ll be able to decide whether it will help you reach your business goals. Benefits of Agile Marketing When brands’ marketing approaches evolve daily, it’s challenging to stay ahead using traditional marketing. Agile marketing enables companies to remain competitive by ensuring effective work of departments, quick reaction to customers’ needs, and changes in marketing trends. In this section, we’ll review the essential advantages of agile marketing for you to consider. They will help you decide whether you need this marketing approach for your business. Agile marketing helps you improve the efficiency of your business. Below you can uncover the advantages. Increase productivity. Companies’ teams that incorporate cross-functionality and self-organization report improved productivity of their departments. With agile marketing, you can delegate tasks and divide them among teams to work independently. Every completed task receives feedback. It enables you to make improvements right away. Collaboration between departments empowers you to create a perfect product and find the right approach to consumers. Adapt to changes and challenges. Agile marketing is flexible and easily responsive to change. You can modify this approach to your needs and cope with unexpected external influences. Once you receive customer insights or other crucial information, you can always adapt your further steps and improve your strategy. As a result, you increase efficiency. Collaborate with different departments. With agile marketing, members of different teams can become part of various projects. The approach doesn’t limit employees, so they can engage and collaborate with the company’s departments. To ensure proper communication, brands use special tools for interaction and organize cross-functional meetings. Ensure transparency. Agile methodology allows shareholders to observe each stage of the project. Teams’ decisions and actions are transparent, meaning you can see project development and make alterations if necessary. As a result, teams produce high-quality work with minimum drawbacks. Teams make well-informed decisions that bring you toward a valuable product. Assess results. Since the agile approach is open to experimentation and change, you can always evaluate the results of your teamwork. Once you finish the assessment, you can think of the improvements. You can discuss the details with your shareholders to determine the key weaknesses and take action. Consider using tests and experiments to understand your product and strategy clearly. You might need to invest more resources into your email campaigns or advertisements to create buzz around your product or work on the product’s features. Now that you know the advantages of the agile approach, let’s discover how to implement it for your business. How to implement agile methodology? If you’ve decided that agile methodology will suit your company and team perfectly, stick to our step-by-step guide to incorporate it correctly. We’ll review each of the essential steps in detail. Ensure collaboration with all members of your team. Since agile marketing implies communication and transparency, you should update all key members of your company on the situation and plans. The project team, stakeholders, and customers need to be connected. To prevent your team from having any problems, you need to discuss the details of your project and uncover all concerns. This way, you’ll be able to talk about the methodology, answer critical questions, and address inquiries. Focus on one project. If you’re just starting with agile, it’s worth trying it with a small project. Take one project first to test your team and its ability to perform cross-functional tasks. Once you finish this project, evaluate the results and feedback. If you complete all the tasks, you can use all the techniques and approaches for your next project. By implementing tests and experiments, you’ll be able to find the best scenario for your team’s effectiveness and productivity. Encourage your team to bring excellent results. Communication and collaboration are the two fundamentals of agile methodology. You need to ensure communication between decision-makers to solve problems and bring an excellent project to customers. You need to focus on motivated individuals to reach your goals and create a great product. If people in your team are goal-oriented and self-organized, you will have the work well done. These individuals will help you reach your goals and finish the project within a given timeframe. Select a framework. If you want to have your work done in an organized manner, you need to choose a framework and follow it. After selecting a framework, you’ll know what to do. You’ll identify whether you need daily meetings, coordination in your communication channel, or reports on the completed tasks. Stick to the framework and evaluate. When you implement an agile methodology for the first time, you don’t need to experiment and combine several frameworks simultaneously. After choosing the framework, stick to its basics. There’s no need to change rules when your team still learns the basics of agile. Ensure interactive work, feedback, smooth collaboration, and constant communication. Congrats, now you know what agile marketing is and why it’s essential. Hope that the advantages of this technique inspired you to consider it for your company.

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!

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.

AI chatbot is a tool that applies artificial intelligence and natural language processing to provide customers with human-like conversations and relevant answers. It’s a useful software application that enables your company to ensure support at any stage of the customer journey and reduce the workload of your team. LIKE.TG allows you to build chatbots for Facebook Messenger, Instagram, WhatsApp, and Telegram and use ChatGPT integration. In this video, the LIKE.TG marketer Anya explains the difference between AI chatbots and rule-based chatbots. So, check it out to boost your customer service! In this article, we’ll unveil the difference between AI and rule-based chatbots and the benefits of AI chatbots. We’ll teach you how to use and build an AI chatbot with LIKE.TG. ChatGPT ChatGPT is a conversational AI platform that uses technology to understand clients’ questions and respond accurately in human language. The software enables you to provide instant, accurate, and natural answers to customers’ questions, recommend products based on customers’ needs, and update on relevant discounts and promotions. There are several types of ChatGPT you can use to solve specific issues. Some help analyze the context and generate more complex text or code. Others allow you to work with translation, complex classification, text sentiment analysis, and summarization tasks. You can create Q&A chatbots and provide instant answers to consumers’ questions. Besides, it helps you generate new text. The model understands text structures to create simple plots and titles. With ChatGPT, you can take your customer support and sales department to the next level. The software empowers your company to provide clients and leads with relevant answers without distracting team members from urgent tasks. The language and messages look human-like so users won’t feel the difference. ChatGPT provides answers based on messages’ history. ChatGPT lets you update clients on upcoming deals and discounts based on their shopping history. Messages are sent according to customers’ preferences; therefore, they are personalized. ChatGPT empowers you to increase customer satisfaction since consumers get their desired products. Now that you know some basic facts about ChatGPT, it’s time to clarify the difference between AI and rule-based chatbots. What is the difference between AI chatbots and chatbots? Sometimes it’s hard to differentiate the rule-based chatbots and AI chatbots because they can perform the same tasks. They can engage with users and answer basic questions. However, after testing these two types of chatbots, marketers report the rule-based chatbots’ inability to understand all customers’ inputs. Rule-based chatbots operate based on pre-written responses triggered when a user types the input. Sometimes the chatbot can interpret the answers in the wrong way and provide not accurate answers. AI chatbots use language processing functionalities and can understand the context of conversations with customers. The answers this type of chatbot provides are accurate and natural. They look human-like so clients can’t figure out they are dealing with a chatbot. The answers are generated from the knowledge base the chatbot is connected to. It can be trained based on previous conversations with clients. For instance, if the question is the same, the bot can use previous conversations as a source of information. To sum it up, AI chatbots can understand the context of the conversation and provide accurate answers. They are natural and human-like. After receiving responses from customer support, your clients will be satisfied with your company’s service. AI chatbots are trained based on the existing conversations with agents, while rule-based chatbots function based on pre-written scenarios. Now that you know the difference, let’s explore the main advantages of AI chatbots. Benefits of AI Chatbots AI chatbots can help you in various areas of your business. They enable you to reduce the workload on your customer support team and sales department. A trained AI chatbot can replace your customer support agents at anytime and provide instant answers. Since clients don’t have to wait for your customer agents to reply in hours or days, customer satisfaction with your brand improves. AI chatbots can be used for lead generation and qualification, increased sales, better customer engagement, and essential customer insights. We’ll cover all the benefits of AI chatbots in short. AI chatbots help provide support 24/7; use natural language to answer questions; look human alike when assisting customers; generate prospects; qualify leads; improve sales; gather information from customer data; improve customer support; boost satisfaction with your company; increase interactions with customers; reduce bounce rates; save costs on hiring more customer support agents; respond to clients’ inquiries faster; provide personalized recommendations based on customers’ preferences. Now that you know the pros, let’s jump into the next section to find out how to use an AI chatbot. How to use an AI chatbot? AI chatbots can be used in multiple areas: customer support, sales, online learning, and many others. It can provide answers and create written content (articles, social media posts, essays, code, and emails). AI chatbot can come in handy if you incorporate it right. In this section, we’ll unveil how to use a conversational AI chatbot for your company. If your team wants to work towards the success of your brand, you need to take care of several aspects. This type of chatbot is an AI-powered chatbot that doesn't require human supervision once everything is set up. However, before the process begins, you should complete several tasks. Let’s find out what you should do to implement your chatbot successfully. Create a knowledge base. If you want your chatbot to send logical and natural answers to customers, think about developing or improving your site’s knowledge base. A knowledge base is a web page on your company’s website that stores all the information about your product (guides, how-tos, instructions, etc.). When discussing AI chatbots, a knowledge base is data stored from all conversations. The chatbot becomes better and more accurate if it’s trained with conversations. The best way to teach your AI chatbot to provide natural and right answers is to have many conversations with clients. Your chatbot should also be connected to the knowledge base on your website. It’ll serve as an additional source of information for your chatbot. Once it receives a question from a user, it’ll search the knowledge base for the most detailed answers. AI chatbots can provide responses based on previous conversations with customer support agents. If your chatbot is already well-trained, you don’t need to take care of it and track every message. An AI chatbot will do everything for you. Connect your chatbot to a live chat. Since there’s still a possibility of a chatbot not understanding customer input, you need to prepare for such a scenario. For this purpose, we connect your AI chatbot to the live chat on your website. This way, if a chatbot cannot answer the question or provide the right response, the user can always talk to a live representative. For instance, you can automatically connect with a customer support agent after the chatbot gives two unclear answers. LIKE.TG empowers you to create a live chat and add it to your site within minutes. Prevent dissatisfaction with your brand and get ready with the live chat. Support customers when they need it the most and ensure that they get appropriate responses when necessary. Adjust AI chatbot answers. To make sure that your chatbot is giving the right and consistent answers, you need to audit it. Don’t let your chatbot operate on its own for months because you can’t know the answers it provides to customers and whether they are happy with them. Your top priority is customer satisfaction, so you need to take care of it. First, consider surveying to ask how happy your customers are with the chatbot. Secondly, look through the answers your AI chatbot usually provides to users. Thirdly, analyze them and find out whether the answers are appropriate and accurate. If everything is right, you don’t have to edit the answers. However, if the answers are not satisfying, there’s a need for improvement. Train your chatbot to provide new answers to questions to make clients happy with your chatbot. Now that you know the tasks you need to complete for an effective chatbot, let's create an AI chatbot with LIKE.TG. How to build an AI chatbot with LIKE.TG? When you consider creating a chatbot, it’s essential to find the right platform for it. Make sure that it doesn't require you to have programming or coding skills to create a chatbot. In LIKE.TG, you can develop a rule-based and AI chatbot within minutes without any special skills or knowledge. The process is simple and fast. With LIKE.TG visual builder, you can set up auto-replies and develop sales pipelines on Instagram, WhatsApp, Facebook, and Telegram. Create scenarios for various messengers or social media platforms and start conversations with customers whenever they need it. You can add a multi-channel subscription widget to convert visitors into clients. To set up a rule-based chatbot, create triggers that will start the flow. The platform will launch auto-replies and message flows once a user triggers a specific action. The next step is writing auto-replies. Based on the trigger, you need to develop different auto-replies that will be automatically sent out to clients. Then, you should create the logic of your bot. With LIKE.TG visual chatbot builder, you’ll only need to drag and drop the necessary messages and triggers and connect them. Connect messages one by one to establish a sequence of messages in your auto-reply flow. Once everything is done, you need to double-check your chatbot. Use test mode to do it. If the bot works right, feel free to launch your chatbot. Below you can see how to work in the LIKE.TG editor. LIKE.TG has an integration with ChatGPT. It means that your bot communicates with clients in a human-like manner. Its answers look natural. The answers will also be more accurate since the bot analyzes previous conversations to understand customers’ inputs. LIKE.TG allows you to create chatbots using four main models: Curie, Davinci, Ada, and Babbage. The model you choose depends on your main business purpose. Some of them are designed to optimize texts, others can decrypt codes. So you need to select the type wisely after you determine your main goal. If you want to optimize texts, consider using the Ada model. You’ll be able to identify keywords, proofread your text, or optimize it to comply with SEO principles. Below is an example of an Ada model chatbot created in LIKE.TG. If you need a FAQ database, consider using the Davinci model. With LIKE.TG, you can train your bot to provide relevant and accurate answers to customers’ questions regarding a product or service. Below, you can see an example of such a model. The service enables you to create a helper bot. It will allow you to solve life issues fast with a chatbot. Users can describe their situations in the bot, and it will send them relevant solutions or advice. Below is an example of the Davinci model for such a chatbot. ChatGPT is a new level of support for customers. Besides customer support, you can perform multiple actions using this conversational chatbot. Relevant, natural, and human-like are the words to describe the help you can provide to customers without the intervention of your agents. Register with LIKE.TG to create quality AI chatbots for Instagram, Facebook Messenger, Telegram, and WhatsApp.

AI marketing is a type of marketing that applies artificial intelligence to collect, analyze and obtain customer insights for further marketing efforts. Marketers manage to improve their content, personalize buyer journeys, learn customer behavior, and provide excellent service. Examples of AI marketing include chatbots, targeted advertising, automated email campaigns, content generation, content personalization, etc. Consider using LIKE.TG to create chatbots and send automated email campaigns with Automation 360. In this video, Anya explores the latest advancements in AI marketing, shedding light on 4 key trends and technologies shaping the industry. In this article, we’ll unveil the benefits and examples of AI in marketing. We’ll also uncover how companies use it and how to do it in LIKE.TG. Benefits of Using AI in Marketing AI in marketing, such as chatbots, automated email campaigns, and targeted advertising, helps improve your company’s ROI. Campaigns created with AI are more effective and easier to monitor. Tracking campaigns enables you to identify issues and eliminate them instantly. Campaigns are personalized and crafted based on customers’ preferences, needs, and problems. Data analytics allows you to produce better content and improve it in seconds. This way, you can avoid additional expenses on hiring specialists and reduce the workload on your team members. They can work on more urgent tasks and improve productivity. With AI, brands can provide personalized experiences to their customers. Email campaigns and highly targeted ads allow companies to send offers based on clients’ preferences, needs, and shopping history. Customer behavior, search history, and shopping lists empower you to send personalized recommendations with products buyers are interested in. Automated email campaigns help you establish trusting relationships with audiences and encourage them to purchase. Moreover, you can prevent customers from churning out. With AI marketing, you can identify such clients and engage with them by using email campaigns. AI tools enable you to grow your business faster by making well-thought-out decisions. You can do it by receiving and analyzing essential marketing data. Automation empowers you to engage with clients at every stage of their customer journeys. Now that you know the pros of using AI in marketing, it’s time to find out how brands use it to their advantage. How do companies use AI in marketing? Lately, AI is one of the topics actively discussed in marketing. More and more brands implement artificial intelligence for better team productivity and effectiveness of marketing campaigns. Let’s discover in what areas you can apply this technique and achieve your main business targets. Content personalization. With personalization, the effectiveness of your marketing increases. You can do it automatically with special AI services. It enables you to send content to customers based on their behavior, shopping history, wishlist, and browsing history. This way, you’ll be able to reach prospects and existing clients with a highly targeted offer. It makes it harder to resist a purchase. You can also send messages using your customer’s data, making the conversation more personal. When you figure out what customers are looking for, you can provide highly targeted offers and help them solve their problems faster. Content generation. Content generation isn’t a problem at all with AI. With its help, brands add social media posts, identify the right keywords and captions, and write email subject lines. Moreover, ChatGPT allows you to create quality blog copies without your team's involvement. Provide the subject line, and AI will create an article for you. However, you still need to double-check the article's content before publishing it. Ensuring that the post complies with your requirements and contains the right information is necessary. Automated email campaigns. Automated email campaigns help your team avoid spending time researching your target audience and deciding what to write in email campaigns. You can create engaging email campaigns and send them to customers based on their preferences, wishes, and needs. As a result, customers are happy with your offers, and your company gains more sales. Data analytics. With AI, you can analyze and use large amounts of data. It helps collect data from multiple campaigns and analyze them. Doing so lets you find out what works and doesn’t with your customers. As a result, you can create effective marketing campaigns that bring you closer to achieving your key goals. You can also predict the outcomes of future campaigns by analyzing customer engagement, email open rates, and time spent on specific web pages. Media buying. If you want to effectively place your ads and promote products, consider using AI. It helps predict the best places to put ads. This way, you’ll be able to reach your potential clients and improve ROI. By using a special tool, you can receive recommendations regarding your ad spend. 24/7 customer support. With AI chatbots, your company will be able to assist 24/7. Potential customers will receive instant answers to their questions. Customer satisfaction with your brand will increase. Besides, chatbots help reduce the workload on your team and ensure instant responses. This way, you make sure your prospects get an answer quickly. Customer experience. Personalized content and instant support improve customer experience. Excellent customer experience is vital when we talk about marketing. It influences consumer decisions. If leads are satisfied with your support, content, and overall experience, they are more likely to purchase. When they buy, they increase the profitability of your business. Creating customer experience positively affects customer loyalty and repeat purchases. Customers who are happy with your brand are satisfied with your product. Sales forecasting. AI technology enables you to calculate the approximate income after implementing specific campaigns. This way, your company can determine whether running this or that marketing campaign is profitable. It will help you figure out whether the sales will cover the costs for the campaign and bring pure revenue. SEO. AI technology is perfect for search engine optimization. Special tools empower you to optimize content to comply with search engine algorithms and search for appropriate keywords. This, in turn, helps improve ranking and understand competitors’ situations. Moreover, AI allows you to identify customers’ interests and create content to match their requirements. Now that you know how to use AI technology to your advantage, it’s time to find out how to use AI in LIKE.TG. How to use AI in LIKE.TG? In this section, we’ll unveil how you can use AI in LIKE.TG, a multichannel marketing platform. Let’s find out the benefits of using the service for your company. LIKE.TG empowers you to create chatbots for various channels such as Instagram, Telegram, WhatsApp, and Facebook Messenger. After creating the bot, you can easily connect the GPT model from OpenAI. The integration is available within the service. ChatGPT allows you to provide your users with even more proficient automated replies and help them solve additional tasks. In LIKE.TG, you can integrate one of the available models: Curie, Davinci, Ada, ChatGPT, ChatGPT (gpt-3.5-turbo-16k), GPT-4, and Custom Fine-tuned model. Davinci is perfect for analyzing complex intent and cause-and-effect problems, summarization, and explaining and generating code, while Curie will suit those who deal with translation, complex classification, text sentiment analysis, and summarization. Incorporate the ChatGPT model if you want to generate replies for messaging apps and virtual assistants and implement QA systems. The language of the bot will be natural and human-like. Now that you know how to use LIKE.TG for AI, it’s time to jump into some examples. The following section will help you figure out how the technology works in practice. Examples of AI Marketing To unveil how AI technology works in real life, let’s review some famous brands and the way they incorporate this approach. After this section, you’ll be inspired to apply AI in your business. Spotify The famous digital music service uses AI to understand the preferences of its listeners and provide personalized experiences. The technology helps Spotify identify the music preferences of their users and provide recommendations based on their most listened songs, podcasts watched, purchase history, location, and other criteria. As a result, the user gets a customized playlist that covers all the songs he is interested in. Although personalization helps this streaming service by providing the best experience to listeners, their marketing efforts don’t end there. The company uses automated email campaigns to deliver personalized recommendations. This way, Spotify makes sure that the client will notice their efforts. By sending specific messages, the platform increases the likeliness of conversions. Netflix The well-known media streaming platform does everything possible to provide customers a seamless experience. Netflix uses a similar approach to Spotify and recommends movies based on clients’ preferences, movies watched, and interests. This works perfectly for those who want to watch a new movie but have no idea what to choose. The service will do all the work by analyzing preferences and providing films based on various criteria. For instance, people who prefer drama will receive a list of the latest Netflix drama releases. Below you can see what it looks like. Amazon The place for millions of products, Amazon is a company that uses AI for analyzing available information and predicting future sales. Once a user visits Amazon, he will see recommended products. These items are selected based on predictive analytics, which helps figure out how likely specific customers are to purchase this or that specific item. This way, Amazon’s team knows which products should be in the recommendations field. Below you can see an example of a recommendation based on browsing history. Congrats, now you know what AI marketing is and why it’s essential. Look through the examples above to get inspired and implement the technique into your business strategy.

AI marketing automation is the process of using artificial intelligence to improve customer support, sales, and customer experience. Data collection, personalization, predictive analysis, natural language processing, and machine learning allow marketers to generate content, provide instant responses, and improve customer satisfaction. In this article, we’ll uncover why marketers use AI for automation and how to use it. We’ll also show you how to automate AI marketing with LIKE.TG and provide some effective tips to achieve even better results. Why do marketers use AI for automation? There are numerous reasons for marketers to use AI for automation. Here are the main ones. Efficiency. With AI automation, business owners automate repetitive tasks, allowing marketers to focus on more urgent issues. These tasks include data analysis, content creation, email scheduling, and social media posting, ensuring consistency across various channels. Consistency. AI allows companies to stay consistent in their marketing efforts by operating based on predefined rules and algorithms. With automation, marketers deliver uniform messaging and branding across multiple communication channels, ensuring brand consistency and seamless customer experience. Speed. AI-powered automation allows marketers to quickly react to changes in market trends and customer actions. Incorporating real-time data analysis and automated decision-making allows marketers to adjust their marketing strategies and campaigns, contributing to competitiveness. Optimization. AI automation helps optimize marketing campaigns by analyzing their performance and feedback. With machine learning algorithms, business owners can identify patterns and predict outcomes. It enables them to determine the appropriate targeting strategy, messaging, and marketing approaches to achieve the best results. Cost-effectiveness. When using automation for repetitive tasks, companies reduce operational costs while improving efficiency. After investing in AI marketing automation, marketers can expect significant cost savings and a higher ROI over time. Personalization. AI-powered automation enables marketers to provide customers with highly personalized experiences across different marketing channels. After analyzing customer data, AI helps segment audiences, generate content, and tailor communications based on customer preferences and behavior. Now that you know how AI automation can benefit your business, it’s time to explore how to utilize it. How to use AI marketing automation? AI-powered automation is a game-changer for marketers, offering increased efficiency, seamless customer experiences, and successful campaign execution. In this section, we'll delve into the practical applications of AI automation and demonstrate how it can revolutionize your marketing efforts. Personalization. With AI, you can personalize content and how you communicate with different audience segments. By providing relevant product recommendations, email content, and ad campaigns that match different audience groups based on customers' preferences and behavior, you can improve conversions and customer experience. Segmentation. By applying AI algorithms, you can segment leads and clients based on demographics, behavior, interests, and other criteria. Machine learning allows you to identify patterns and preferences, resulting in better targeting. Improving customer service with chatbots. AI-powered chatbots offer businesses a powerful tool for engaging with prospects and customers, providing instant human-like answers, and ensuring round-the-clock support. AI helps you enhance customer engagement, streamline processes related to promotion, sales, and assistance, and boost business growth. While incorporating advanced technology and automation, your company can ensure better customer experiences, improve efficiency, and attain business goals faster. Lead qualification. AI automation enables your business to improve lead management and achieve better results. When utilizing advanced technology and data-driven insights, your company can optimize its lead qualification efforts and drive growth and profitability. Lead nurturing. AI automation helps you build stronger relationships with prospects, improve the nurturing process, and drive better results across the entire sales funnel. By applying automation consistently, you’ll be able to gain trust and drive your business revenue. Customer data collection. With AI automation, you can effectively gather information about customer behavior and preferences to approach them with relevant messages in the future. It will help you establish trusting relationships with customers, encouraging them to return and perform repeat purchases. Post-purchase communication. AI-powered chatbots allow you to communicate with clients after they buy from you. Automated human-like questions will enable you to collect valuable insights and customer feedback on your products, customer service, and overall experience with your brand. This data will improve your marketing strategy and things related to your product or service. As a result, you’ll be able to enhance customer experience and improve customer satisfaction. Now that you know how to incorporate AI marketing automation, it’s time to find out how you can do it with a multichannel platform like LIKE.TG. How to automate AI marketing with LIKE.TG? To automate AI marketing, you’ll need a professional platform that allows you to do it. LIKE.TG is one of them. This multichannel marketing solution allows you to create chatbots, web push notifications, websites, email campaigns, and pop-ups and deliver them to the right people at the right time. It also helps you build courses, live chat, and use its CRM. However, in this section, we’ll focus on the development of AI-powered chatbots for WhatsApp, Telegram, Instagram, and Facebook Messenger. LIKE.TG enables you to build a conversational chatbot and integrate it with ChatGPT. By creating such chatbots for messengers and social media, you’ll be able to provide consistent, relevant, detailed, and human-like responses. The service allows you to determine the topics your bot can cover and its tone and language. As a result, you provide customers with a chatbot that uses natural language and ensures open-ended answers with all the details prospects and customers need. By using integrations with OpenAI tools, you can process voice messages and generate custom images to help customers visualize their requests. This feature is a must for small businesses providing made-to-order items. Even more, LIKE.TG’s integration with AI empowers you with the necessary tools to build chatbot flows and template messages in the service. So you won’t need to spend days creating quality chatbot flows for your prospects and customers. Here’s a short guide on developing a chatbot flow using AI in LIKE.TG. Step 1. Build a flow Proceed to the “Chatbots” section, select a bot from the list, and create a new flow. Click “Create a flow using AI” in the editor’s field. If you don’t have a chatbot in the service yet, we recommend you check out the instructions on how to do it. Step 2. Add a prompt Add short instructions on what your flow should contain and look like. Strive to describe the flow as clearly and in detail as possible, allowing AI to understand and meet your needs. Click “Create a flow.” Step 3. Edit the flow Wait a few minutes for your newly generated flow. Once the flow is ready, you can make some improvements by editing its elements. You can add more information, check it for variables and tags, and add unaccounted-for links between elements. You can add variables in text to personalize your message. To add a variable, click {} in the right corner of the text block and select the variable from the drop-down menu. You can attach tags to your subscribers when they click on the necessary button or go through a certain flow branch scenario. This way, you can filter your subscribers based on a particular tag and send them a message. Go to the flow editing mode, drag the “Action” element to the workspace, and link it with your button. LIKE.TG’s site builder also has an AI writing assistant. It will help you rewrite, translate, or proofread your text. With this builder, you’ll avoid brainstorming sessions and double-checking for typos or mistakes in content before you launch your website. Below, you can see how to generate new text using AI in the service. The process is fast and straightforward and provides good content within minutes. Now that you know how to use LIKE.TG to automate AI marketing, let’s jump into the effective AI marketing automation tips. 11 Tips for Effective AI Marketing Automation You can use AI in marketing in various ways, yet not all approaches yield the results you strive for. In this section, we’ve compiled a list of effective tips that will help your business scale and provide a seamless customer experience. Segment your audience. Consider incorporating AI algorithms to divide your audience into segments based on criteria such as demographics, interests, purchase history, and other relevant factors. By doing so, you’ll be able to communicate targeted messages and relevant offers to different customer groups, improving engagement and sales. Personalize content and recommendations. Personalize content and product recommendations according to individual customer preferences and behavior. For better results, you should use data-driven insights to send timely messages that resonate with different customers’ needs. Optimize your email marketing strategy. AI is essential for effective email campaigns. You can analyze open rates, click-through rates, and other metrics. Consider testing various subject lines, content formats, and send times to identify the best options that increase engagement and conversions. Use a marketing automation platform. Consider choosing a marketing automation service to run your communication with customers on autopilot, such as email campaigns, chatbots, and content generation. It will help you perform repetitive tasks automatically without needing to assign them to your team members, improving their efficiency. Utilize behavioral targeting. Incorporate AI-powered behavioral targeting to send relevant and timely content and product recommendations based on user's interactions with your company and their behavior. It will help customers receive what they need and make faster purchasing decisions. Optimize your content. Consider using AI to dynamically optimize content based on real-time data such as user demographics, preferences, and browsing history. This way, you’ll be able to deliver the most relevant content. Incorporate automated email campaigns. Incorporate AI-driven email campaigns to send personalized emails to the right audience segments at the right time. Since AI can analyze user behavior, you’ll be able to identify the best timing and content for each email you send to subscribers. Integrate chatbots and virtual assistants. Consider implementing AI-powered chatbots or virtual assistants into your marketing strategy to ensure round-the-clock support, instantly provide answers to FAQs, and guide users through the sales funnel. It will help you improve customer satisfaction, help clients find the right products, and increase sales. Optimize advertising campaigns. Utilize AI to improve advertising campaigns across multiple marketing channels by analyzing performance data and adjusting targeting, bidding strategies, and ad creatives. Apply predictive analysis. Since AI can forecast future trends, identify prospective customers, and find out customer needs, you should use it to your advantage. Employ predictive analytics to optimize your marketing campaigns and improve ROI. Qualify leads on autopilot. Use AI algorithms to automatically identify the right audience and qualify prospects based on their engagement level, demographics, and purchase intent. This will allow you to focus on qualified leads who are more likely to purchase and improve your conversion rates. Congrats, now you know what AI marketing automation is and how your business can benefit from implementing it. Register in LIKE.TG to improve customer experience and satisfaction by incorporating chatbots for Facebook Messenger, Instagram, Telegram, and WhatsApp into your customer support, product recommendations, and reservation system.

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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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

With close to 200 different solutions for data integration out there, choosing the right one is like stepping into a bustling market with a myriad of vendors—each promising seamless connectivity. Except, you have to do your own due diligence to identify the solution that caters to your organization’s needs most effectively. So, in this article, we’re going to compare Airbyte vs Fivetran vs LIKE.TG, three of the best data integration tools, to help you make an informed decision. Airbyte vs Fivetran vs LIKE.TG: Overview Airbyte Finally, Airbyte is primarily an open-source data replication solution that leverages ELT to replicate data between applications, APIs, data warehouses, and data lakes. Being an open-source solution means users can customize and extend their pipelines. Like other data integration platforms, Airbyte features a visual UI with built-in connectors. Fivetran Fivetran is a cloud-based data movement platform that relies solely on ELT to move data between two systems. It’s an automated data synchronization solution that syncs data from different databases, APIs, applications, and other structured data sources into data warehouses and lakes for analytical purposes. Fivetran can be an ideal solution for businesses that only need to move data between sources and destinations without massively transforming it. LIKE.TG LIKE.TG is an end-to-end data integration and management platform that features a 100% no-code UI that delivers a consistent user experience across its suite of applications. The result? A simple, easy-to-use platform that allows all users, regardless of their technical expertise, to build fully automated data pipelines based on ETL or ELT. These data pipelines enable users to establish native connectivity to a wide range of endpoints, including files such as PDFs, XML, Excel, etc., databases and data warehouses, whether on-premises or in the cloud, data lakes, cloud storages, web applications, as well as data visualization and BI tools, consolidating their company-wide data. Manage All Your Data in a Single, Unified Data Management Platform Empower your data journey with LIKE.TG Data Stack—a comprehensive suite of solutions! Whether it's unstructured data management, data integration, API management, managing EDI workflows, or building a bespoke data warehouse from the ground up, LIKE.TG has you covered. Learn More Comparing Airbyte vs Fivetran vs LIKE.TG LIKE.TG Fivetran Airbyte Data Integration Uses both ETL and ELT to integrate data across the organization. Also supports change data capture (CDC) for replication. Does not offer ETL. Instead, uses ELT to ‘move data’ between systems. Also supports CDC for replication. Does not offer ETL, instead uses ELT to sync data across applications, databases, etc. Also supports CDC for replication. Data Warehousing Offers the ability to build a fully functional data warehouse from scratch without writing a single line of code in addition to supporting native connectivity to popular data warehouses. Fivetran’s data warehousing is limited to offering connectivity to popular data warehouses only. Airbyte’s data warehousing is limited to offering connectivity to popular data warehouses only. API Management Apart from the ability to connect to and integrate APIs, LIKE.TG offers a single platform that caters to the entire API lifecycle—from design to publishing. API management is not offered by Fivetran. Instead, it requires integration with external services in order to enable users to design, share, and test API requests. Only offers Airbyte API, that provides an interface to users to “programmatically” control the Airbyte platform. Does not offer API management. Unstructured Data Extraction LIKE.TG offers a complete data extraction solution capable of extracting not only structured data but also semi-structured and unstructured data, including PDFs, invoices, reports, etc., that too in bulk. LIKE.TG leverages AI to simplify and accelerate unstructured data extraction. Fivetran only replicates structured data. It does so by understanding the source data structure and mapping it to a destination schema of tables and columns. Although it has only recently started extracting text from documents, Airbyte does not offer full-fledged unstructured data management. Additionally, Airbyte users can extract text from documents stored only on Amazon S3 and Google Drive. EDI Management LIKE.TG has a dedicated platform for no-code EDI management. No support for EDI files or management whatsoever. No support for EDI files or management whatsoever. Connectors and Integrations Offers native, bidirectional connectors for a range of sources and destinations, including files, databases, data warehouses, data lakes, cloud storages, and web applications. Offers connectors for files, applications, databases, data warehouses. Fivetran’s website states that support for data lakes is in development. Like LIKE.TG and Fivetran, Airbyte also offers a long list of connectors. However, most of its source connectors are either in beta or pre-beta. Custom Connectors Along with native connectors for popular sources and destinations, LIKE.TG offers the ability to build custom connectors seamlessly. Fivetran offers custom connector development through cloud functions. It also offers its “By Request” program, albeit only for Lite connectors. Users can use Airbyte Connector Development Kit for custom connector development. However, they need technical expertise to build them. Data Quality LIKE.TG offers comprehensive data quality features embedded into its platform. Together, they ensure data accuracy, reliability, and completeness. Being an ELT-based platform, Fivetran does not provide any dedicated data quality features. Airbyte does not offer any built-in data quality features. Instead, it relies on external services. Transformations LIKE.TG offers a plethora of pre-built transformations. There’s no need to integrate with dbt, or any other service, to transform data. Since it’s an ELT tool, the Fivetran relies on dbt heavily to transform data. Being a data replication solution, Airbyte also relies heavily on external services for data transformation. Customer Support LIKE.TG has won several awards for outstanding customer support, highlighting its commitment to supporting all its customers 24/7. Although Fivetran claims to provide 24/7 support, some popular forums, such as reddit, state that the quality of customer support is not up to the industry standards. Airbyte provides enterprise-level support only to its enterprise customers. Build Data Pipelines Effortlessly With LIKE.TG’s Automated Data Integration Solution Building and maintaining ETL/ELT pipelines does not have to be complex and time-consuming. Leverage LIKE.TG's 100% no-code solution for seamless data integration. Get Your Free Trial! Airbyte vs Fivetran vs LIKE.TG: Data Integration Airbyte Airbyte is primarily a data replication tool that enables its users to extract data from their source systems and load it into their target systems. In other words, Airbyte facilitates data extraction and loading between systems, with transformation being optional and only possible, via external services or integrations, after the data is loaded into the destination system. Therefore, Airbyte can be a good solution for businesses that only need to sync data between their systems without substantially transforming it. It is certainly not the solution of choice for organizations looking for an end-to-end data integration solution, especially those that frequently need to transform their data before moving it to their data warehouses or lakes. Fivetran Similar to Airbyte, Fivetran lacks broader data management capabilities as it relies on ELT to “move” data between systems. Put simply, the platform’s focus is on moving data from a data source to a target destination as quickly as possible. This means that the data can only ever be transformed once it’s in the destination system. While this can be beneficial in some scenarios, for example, for SaaS companies that want to analyze data from their applications, the solution is far from ideal for large enterprises that need to transform and integrate all their data to achieve a single source of truth. LIKE.TG Whereas Fivetran and Airbyte rely on ELT to replicate and sync data, LIKE.TG uses both the ETL and ELT data integration techniques to integrate data in the true sense. With LIKE.TG, users have the flexibility of choosing between either, depending on their requirements. LIKE.TG’s pre-built transformations simplify and accelerate the ETL pipeline development process significantly, enabling users to take charge of their own data integration projects without relying too much on IT. TL;DR: If your business use case requires that you transform your data before loading it into the destination system, consider LIKE.TG. On the other hand, if the sequence of data transformation is not a major factor, it ultimately comes down to how much of a hassle your data teams are willing to get into and your budget. While both Fivetran and Airbyte support post-loading data transformation, you’ll have to use dbt, which can increase your costs significantly, especially if you have multiple developers working on your data integration pipelines. With LIKE.TG, however, there’s no need for any sort of external integrations for data transformation. Airbyte vs Fivetran vs LIKE.TG: Data Warehousing Airbyte As far as data warehousing is concerned, Airbyte only supports it in terms of providing connectivity to sources and destinations. This means that while Airbyte users can extract and load data into data warehouses supported by the platform, there’s no way to create a custom data warehouse using the platform. Fivetran Fivetran, being a data movement platform, majorly supports data warehouses as destinations. This means it doesn’t support all major data warehouses as sources, which can be a significant downside for many potential customers. LIKE.TG Not only does LIKE.TG offer native, bi-directional connectors for major data warehouses, but it also provides a platform to build a custom data warehouse from scratch without writing a single line of code. The user-friendly and intuitive UI keeps the process simple for business and technical users alike. TL;DR: If your business requirements are limited to loading data into a data warehouse, then it’s mostly a question of how effectively and efficiently a solution caters to them. However, if you anticipate building a data warehouse in the future, choose LIKE.TG. Build a Custom Data Warehouse Within Days—Not Months Building a data warehouse no longer requires coding. With LIKE.TG Data Warehouse Builder you can design a data warehouse and deploy it to the cloud without writing a single line of code. Learn More Airbyte vs Fivetran vs LIKE.TG: Data Quality Airbyte Unlike other data integration tools, like LIKE.TG, Airbyte does not provide built-in functionality for data quality management. Instead, it relies on external services, such as dbt and re_data, to identify data quality issues on data ingestion pipelines. In some instances, users need to use multiple different dbt packages to transform their data the way they want, which can quickly become tedious. Fivetran Fivetran also relies on dbt for data transformation and, therefore, any improvements to data quality. The fact that it’s a simple data movement platform means that its users can only improve the quality of their data sets once they are in the destination systems. While this may appeal to some users, this separation is not always an ideal solution. For instance, some data transformations require complex logic or intricate operations that are more efficiently handled closer to the source during the extraction or loading phase. Additionally, post-loading transformations consume resources in the destination system, which might lead to increased costs, especially when dealing with large data sets. LIKE.TG In contrast to both Airbyte and Fivetran, LIKE.TG offers comprehensive data quality features built right into its platform. This means that there’s no requirement for any external integrations to improve data quality. Users can easily clean, validate, and standardize data, apply custom data quality rules to their data sets even at the record level, and get insights into data quality issues via data profiling and by creating detailed logs for erroneous records. TL;DR: If your organization requires improving data quality before loading data into its enterprise data warehouse, opt for a data management solution with built-in data quality management, such as LIKE.TG. Airbyte vs Fivetran vs LIKE.TG: Customer Support and Documentation Airbyte Airbyte offers varying levels of customer support for its offerings—community, professional, and enterprise. Airbyte’s users that opt for open-source version are mostly on their own when it comes to fixing bugs and finding workarounds. It offers its enterprise-level support only to its enterprise customers. Airbyte only offers limited support hours, i.e., 12 hours per day, 5 days a week. While Airbyte does maintain documentation, it’s certainly not amongst the more extensive ones. Fivetran Compared to Airbyte, Fivetran offers 24/7 support for all its customers via a dedicated “team of technical specialists.” Fivetran support is available via email, call, and live chat. Additionally, Fivetran claims 1 hour response time for its customers using either the Standard, Enterprise, or Business Critical plans. However, Fivetran users on popular forums like Reddit frequently report subpar customer support from Fivetran. Fivetran also maintains documentation on its website that can help users with their basic questions. LIKE.TG LIKE.TG is recognized for its outstanding customer support and has consistently received awards for its relentless pursuit of customer satisfaction. It offers the same level of 24/7 customer support to all its customers. Users can get support over the phone, via email, as well as through live chat. LIKE.TG also provides additional on-demand training highly tailored to its customers’ most immediate needs. Additionally, business users can benefit from LIKE.TG’s Data Academy and hone their data skills as they continue to use its products. LIKE.TG’s documentation covers its entire suite of products, their functionalities, and use cases in detail. TL;DR: With the growing reliance on third-party data integration solution providers, organizations expect proactive customer support. This means timely responses to queries, proactive issue resolution, and a customer-centric approach that aligns with their evolving needs. While all three offer varying levels of customer support, LIKE.TG stands out with its unwavering commitment to customer satisfaction, proven by its award-winning track record. Airbyte vs Fivetran vs LIKE.TG: Pricing Airbyte Airbyte’s open-source version is free for basic use cases with limited volume and features, but users must pay for the infrastructure needed to operationalize the connectors. For Airbyte Cloud, the company uses volume-based pricing, which depends on the number of rows and the amount of data replicated. Finally, Airbyte Self-Managed will have users paying for support and enterprise features while hosting Airbyte on their own infrastructure. Calculating the actual total cost of using Airbyte can be very difficult, given its use of “credits.” One credit costs $2.5; however, it’s not immediately clear how many rows or how much data synced from what kind of source will lead to the consumption of 1 credit. Fivetran Compared to Airbyte, and in addition to its free, albeit basic, version, Fivetran uses a different pricing mechanism—MAR or monthly active rows. MARs are, in fact, the unique identifiers, or primary keys, that Fivetran uses to track transfers from a user’s source system to their destination each month. These keys are counted separately for each account, destination, connector, and table. This means that users are charged for monthly distinct primary keys that are inserted, updated, or deleted via its connectors. Additionally, Fivetran has multiple pricing plans, which vary by the number of connectors, the security and governance features, and sync frequency. Once again, the problem here is the formidable challenge of accurately estimating the actual cost of using the platform. LIKE.TG In contrast to Airbyte and Fivetran, LIKE.TG offers 100% transparency when it comes to pricing. It follows an annual license-based pricing model where the customers are fully aware of how much they will be charged. The cost of this license mostly depends on the number of connectors, the computing requirements, and number of users. The license can be renewed every year. TL;DR: If you are associated with a small business with a limited number of rows and amount of data to integrate, volume-based pricing can be very cost-effective as long as you continue to monitor your usage. However, if you have multiple sources and destinations with frequent data movement, volume-based pricing can easily get out of hand. In this case, considering a solution with more scalable and predictable pricing, such as flat-rate or subscription-based options, will certainly be more economical. Manage All Your Data in a Single, Unified Data Management Platform Empower your data journey with LIKE.TG Data Stack—a comprehensive suite of solutions! Whether it's unstructured data management, data integration, API management, managing EDI workflows, or building a bespoke data warehouse from the ground up, LIKE.TG has you covered. Learn More The LIKE.TG Advantage Compared to Airbyte and Fivetran, LIKE.TG offers a unique value proposition to businesses of all sizes. It’s an end-to-end data platform that caters to multiple aspects of data integration and management, covering diverse use cases. On the other hand, Airbyte and Fivetran only cater to a few use cases, all of which are based on ELT. From data integration to API management to building a custom data warehouse, LIKE.TG provides a unified and 100% no-code platform for all your data management needs. Its ability to transform data and improve its quality without requiring any further integrations significantly simplifies the process of building data pipelines. All of this is complemented by LIKE.TG’s award-winning customer support. If you’re looking to implement an end-to-end data integration solution for your organization, try LIKE.TG. Download a 14-day free trial or sign up for a demo.

Sales teams and social media managers are facing a lot of challenges when it comes to lead generation, nurturing and conversions. These challenges include the ever-increasing number of social media platforms, the sheer volume of data that needs to be processed, and the need to stand out in an overcrowded digital landscape. However, with the advent of artificial intelligence and automation, sales teams can now streamline their social media campaigns and make more informed decisions in less time. AI and automation can help sales teams to improve the quality of their leads, and increase their conversion rates. In this blog post, we will discuss some of the benefits of using AI and automation in social sales, and how they can help you to achieve your sales goals. AI-powered Social Listening AI-powered social listening tools help sales teams to keep track of brand mentions, customer feedback and other important information about their target audience. With the help of machine learning algorithms, these tools can analyze and interpret social media data to identify trends and patterns that would otherwise be difficult to detect. This allows sales teams to make more informed decisions about their social media campaigns, and to create more targeted and effective content. Chatbots Chatbots are automated programs that can communicate with customers on social media platforms, providing them with information and assistance in real-time. These bots can be programmed to answer common questions, provide product recommendations, and even help customers make purchases directly on social media platforms. Chatbots help sales teams to automate customer interactions, saving time and resources, while also providing customers with a better experience. Automated Lead Generation AI and automation can help sales teams to identify and qualify leads more efficiently. With the help of machine learning algorithms, social media platforms can analyze customer behavior and identify potential leads based on their interests and past interactions with the brand. This allows sales teams to focus their efforts on the most promising leads, increasing the chances of conversion and ultimately improving the ROI of social media campaigns. Automated Content Creation AI-powered tools like OpenAI’s GPT-3.5 can generate high-quality content with minimal human intervention. This can be a game-changer for sales teams, as it allows them to create more content in less time. With the help of GPT-3.5, sales teams can generate social media posts, blog articles, and even email marketing campaigns with ease. This can help to keep their social media channels active, while also freeing up time for other important tasks. Automated Reporting Automation can also help sales teams to generate reports and analyze data more efficiently. With the help of AI-powered tools, sales teams can create reports that provide insights into customer behavior, campaign performance, and other important metrics. This allows sales teams to make more informed decisions about their social media campaigns, and to adjust their strategies in real-time. LIKE.TG’s Facebook Sales Engagement Platform Social media has become a crucial aspect of modern-day sales and marketing strategies. With Facebook boasting over 2.8 billion monthly active users, businesses are keen to leverage the platform’s vast user base to generate leads, drive sales and stay ahead of the competition. However, with so much noise on the platform, it can be difficult to stand out and capture the attention of potential customers. This is where LIKE.TG’s Facebook Sales Engagement Platform comes in, with its latest integration of OpenAI’s GPT-3.5 language model. The integration of ChatGPT into LIKE.TG’s Facebook Sales Engagement Platform has significantly enhanced the software’s capabilities. The ChatGPT module enables salespeople to create marketing content and communicate with prospects more efficiently. With the assistance of ChatGPT, businesses can streamline their communication and improve their sales capabilities, achieving better results with less effort. In addition to ChatGPT, LIKE.TG’s Facebook Sales Engagement Platform also features an AI writing assistant built into the Posting feature. This AI-powered tool allows businesses to generate engaging content with just a simple prompt, such as “Write a promotional copy for LIKE.TG Facebook Sales Engagement Platform.” The AI writing assistant will then generate relevant and compelling text that businesses can use to create their posts. The advanced automation features of LIKE.TG’s Facebook Sales Engagement Platform make it easier than ever to create and distribute engaging content, join groups, send DMs, and perform other marketing practices. The platform can also be used to generate leads, with its marketing automation feature spreading marketing content automatically to generate leads on autopilot. Moreover, LIKE.TG’s Facebook Sales Engagement Platform provides detailed analytics that enable businesses to track their performance over time and make data-driven decisions to optimize their sales strategy. With this all-in-one sales automation tool, businesses can stay ahead of the competition and achieve their sales goals. The integration of OpenAI’s GPT-3.5 language model into LIKE.TG’s Facebook Sales Engagement Platform marks a significant milestone in the evolution of sales and marketing on Facebook. With ChatGPT and AI writing assistant, businesses can create high-quality marketing content and communicate more effectively with prospects. The advanced automation features and detailed analytics of the platform make it easier than ever to generate leads and stay ahead of the competition. If you’re looking to improve your sales and marketing efforts on Facebook, LIKE.TG’s Facebook Sales Engagement Platform is definitely worth checking out. How social selling can benefit from automation and AI Automate repetitive tasks Social selling requires an enormous amount of time and energy, so any opportunity to save time is worth exploring. Automation can help you streamline the process of following up with leads, scheduling meetings and sending emails by reducing the number of manual steps involved in these activities. Automate manual tasks When it comes to social selling, there are many aspects that require human intervention: connecting people on LinkedIn; sending out messages via email or text message (SMS); responding to comments made on social media channels etcetera. However, there are also some repetitive manual tasks that can be automated using AI technology such as coordinating calendars between different stakeholders when planning an event or meeting; automatically creating lead profiles based on information provided during conversations at events. Automate outreach with AI-powered bots Social selling is all about building relationships, but it’s also about being efficient. When you’re busy and trying to reach out to new prospects, the last thing you want is a bunch of manual labor slowing down your process. You can use bots to automate repetitive tasks like scheduling meetings or sending follow-up emails so that they take less time–and get better results.Automating outreach doesn’t just mean sending more messages; it also means creating better ones that are tailored specifically for each prospect based on their needs and interests (and not just “Hi there!”). This kind of personalization isn’t easy when done manually, but artificial intelligence enables marketers to analyze vast amounts of data quickly in order create highly personalized pitches at scale–which means fewer wasted efforts on people who aren’t interested in what you have to offer them! Use AI to identify and engage with leads AI can help you identify and engage with leads. The more leads you have, the more likely you are to make sales. With AI, you can use data to identify the most relevant people to engage with on social media, so that you spend less time searching for them manually and more time actually engaging with them. AI will also help make better decisions about which content should be shared on social media in order for it to resonate with your target audience – this saves both time and money as well as increasing engagement rates. Finally, automation allows companies who use it effectively (like Buffer) not only save lots of money but also free up employees from repetitive tasks so they can focus on what matters most: creating high quality content! Bridge the Customer Lifecycle Gap Here’s a quick recap of the customer lifecycle: Acquire new customers Retain existing customers, who are likely to churn if you don’t provide value or keep them happy Convert your acquired leads into paying customers (or upgrade them from free trials) Turn those paying clients into raving fans by providing exceptional service, advice and support Leverage automation to boost your reach, not just scale. Automation is not just about scale. It’s also about boosting your reach, which means you can focus on what you are best at–the customer. If you’re using automation to scale, it will help avoid mistakes and keep the process running smoothly while saving time in the long run. But if your main goal is to boost your reach or sales team engagement and productivity, then AI technology may be better suited for this purpose. Automation can help social selling professionals do what they do best. Automation can help social selling professionals do what they do best. Social selling is a field that relies on human interaction and communication, but it also has many repetitive tasks that can be automated. Social selling professionals are often responsible for identifying leads, following up with those leads and engaging them in conversations about their needs and problems. Conclusion AI and automation can help sales teams to streamline their social media campaigns, increase their conversion rates, and achieve their sales goals. With the help of AI-powered tools, sales teams can automate customer interactions, identify and qualify leads more efficiently, and create high-quality content with minimal human intervention. As social media continues to evolve, sales teams that embrace AI and automation will be better positioned to succeed in an increasingly competitive landscape.

Data aggregation is the process of combining and summarizing data from disparate sources into a cohesive dataset. It prepares data for analysis, making it easier to obtain insights into patterns and insights that aren’t observable in isolated data points. Once aggregated, data is generally stored in a data warehouse. Then, you can leverage it to gain a holistic perspective on your operations and market trends, design effective risk management practices, and make more informed decisions overall. This blog offers an in-depth look at data aggregation to help you understand what it is, how it works, and how it benefits your business when done right. Understanding Data Aggregation What is Data Aggregation? Data aggregation is performed using specialized tools called data aggregators. Depending on the nature of the data and your objectives, the aggregation process can include grouping, averaging, summing, and counting. Individual records and atomic data rows are consolidated into a summarized dataset following these operations — resulting in aggregated data. Examples of Aggregated Data Number of students in a given school district: The total number of enrolled students in each school district can offer insights into the state of education in different city areas. Number of customers by city: Instead of assessing each customer individually, a business can group its customers in a particular town to determine their shared characteristics, interests, and preferences. Financial reporting: Financial data is regularly aggregated for reporting purposes. For example, balance sheets, income, and cash flow statements are consolidated to create comprehensive financial reports. These reports help stakeholders understand an enterprise’s financial health. Comparing Aggregated and Disaggregated Data These are some of the factors that differentiate aggregated and disaggregated data from each other: Aggregated Data Disaggregated Data Replaces individual data elements with summaries that represent various groups as a whole. Comprises individual data elements or groups that aren’t summarized or collated. Accelerates querying and analysis, allowing you to quickly evaluate vast data. Features the same granularity as the original data, allowing you to assess each data point individually if needed. Offers a concise representation of the whole dataset, enabling you to identify patterns and trends. Provides a detailed view that is useful for accessing and analyzing individual data records. What are Data Aggregators? Data aggregators are crucial to data aggregation and management. These tools collect data from various sources and feature built-in capabilities for aggregate measurements. They utilize different techniques to collect, clean, and process data. They can also help you track data lineage to trace your aggregated data. The Only Data Aggregation Tool You Need Zero coding but plenty of aggregate functions and an interface that's ideal for business and technical users alike. Your free trial awaits. Start Using LIKE.TG Today! Importance and Benefits of Data Aggregation for Businesses 1. Decision-Making Data aggregation provides comprehensive insights that can assist you in making better strategic decisions for your organization. Consolidating, summarized data from wide-ranging sources ensures you aren’t considering just one perspective in your analysis. The result is a more detailed picture of trends, opportunities, and potential room for growth. 2. Performance Monitoring Data aggregation facilitates you in monitoring key performance indicators (KPIs) more effectively. When you keep up with sales, revenue, operational efficiency, and customer satisfaction, you can assess your business’ performance capably and course-correctly to achieve your goals. 3. Risk Management Aggregating your data helps identify potential risks and take the necessary steps to mitigate them. Including various sources in your data aggregation process lets you determine potential anomalies and threats. You can then take decisive, proactive measures to protect your business. 4. Increased Efficiency Data centralization is one of the crucial components of data aggregation. Storing all relevant information in a single accessible location reduces the time and resources required to access and analyze data, which enhances efficiency. 5. Better Insights Aggregated data unlocks deeper insights into your business. It spotlights vital factors such as your market dynamics, operations, and current and potential customer base. Analyzing aggregated data allows you to unravel correlations, trends, and patterns to drive growth. 6. Competitive Edge Data aggregation gives you a competitive advantage over other businesses in your niche. You can make data-driven decisions in less time and with better accuracy. Over time, this edge allows you to adapt and respond to changing market patterns more effectively than others. Data Aggregation Types and Techniques There are various types of data aggregation. Your requirements and how you plan to use the data will determine which approach suits your use case. Temporal As the name suggests, temporal aggregation summarizes data over specified time intervals. These intervals can be as short as hours or days and as long as months or years. This aggregation type is preferable to conduct trend or pattern analysis over time. Temporal aggregation is extensively utilized in time-series modeling, financial analysis, and economic forecasting. You can use it to identify seasonality or cyclical patterns in your data. Spatial Spatial aggregation is helpful to summarize your data based on geographical locations or other spatial units — such as postal codes, towns, cities, states, or countries. You can use spatial aggregation to analyze spatial patterns, identify regional differences in data, or perform geographic analyses. It’s commonly used in urban planning and geographic information systems (GIS). Hierarchical Hierarchical aggregation arranges data into hierarchical structures like groups, subgroups, categories, and subcategories. This aggregation is useful for cases requiring drill-down analyses or hierarchical reporting. These are some uses of hierarchical aggregation in a few industries: Finance: Evaluating financial data by transaction, account type, and branch. Manufacturing: Overseeing the production cycle by SKU, production line, and factory. Government: Using regional and administrative level demographic data to guide decision-making. Healthcare: Reviewing patient data by medical condition/diagnosis, department, and hospital. Supply Chain: Optimizing distribution and inventory levels by studying customer, route, and warehouse/storage facility details. Techniques of Data Aggregation Data aggregation techniques are critical in data summarization and analysis. These techniques range from simple processes to more complex operations, enabling you to analyze vast data volumes efficiently. Here are ten common data aggregation techniques: Grouping: Arrange data in groups based on specified criteria, such as grouping purchase orders by department or product type. Summation adds numerical values for calculating totals, for example, determining the total purchase expense in a cycle. Counting tallies the occurrences or records, such as the number of transactions every day in a purchases dataset. Averaging calculates the average value or mean of a set of numbers. This technique is useful for determining the average purchase amount or the average number of items in a purchase order. Minimization identifies the lowest value of an attribute across a group of records, such as the smallest purchase amount each day. Maximization determines an attribute’s highest or largest value across a group of records, such as the most significant purchase amount each day. Median calculation finds the middle value in a sorted list of numerals and provides a measure of central tendency. Mode calculation finds the value that occurs most frequently in a group of records. In a purchases dataset, we can summarize each city’s most common product category. Weighted averaging averages values and assigns different weights to each of them. This technique is useful for calculating weighted average prices or scores. Hierarchical aggregation aggregates the data at multiple granularity levels in a hierarchical structure. For example, aggregating sales data by city, country, or region. How to Aggregate Data: Key Steps Involved 1. Define Your Objectives You need to define your objectives for data aggregation before starting the aggregation process. Identify your uses for the aggregated data and determine how it can aid you in making decisions and achieving your goals. 2. Collect Your Data For effective data aggregation, you must collect data from relevant sources. Besides being relevant, your data must be complete, up-to-date, and accurate. Automated tools can help you streamline data collection and eliminate the errors associated with manual processes. 3. Enhance Data Quality Next, enhance your data’s quality to improve its reliability. At this stage, you can implement data validation checks, enforce data governance policies and procedures, perform data profiling, and standardize data formats and values to keep your datasets consistent. 4. Aggregate Data Once you’ve collected your data and undergone the necessary procedures to improve its quality, you can start aggregating it. As discussed above, your aggregation process may involve grouping or summarizing your data — depending on your requirements. 5. Validate The Results Lastly, you’ll need to validate the results of the data aggregation process. Compare the aggregated data with your source data, identify any discrepancies that need attention, and fix them as required. There's a Faster Way of Aggregating Data Numerous businesses are already benefiting from LIKE.TG's enterprise-grade data platform. Yours can be next. Find out how LIKE.TG can cater to your unique use case. Speak to Our Team Challenges in Data Aggregation Missing or Incomplete Data: Incomplete data can disrupt aggregation and generate inaccurate or skewed results. Implement effective strategies such as imputation techniques or data sampling to counter its impact on the aggregated results. Handling Large Volumes of Data: Aggregating vast amounts of data is a resource-intensive process that can affect performance. Consider turning to scalable data aggregation solutions to easily process and analyze massive datasets. Addressing Privacy and Security Concerns: You may encounter privacy and security concerns when aggregating confidential data. Protect sensitive data throughout the aggregation process by implementing data privacy and security measures such as access controls, encryption, and anonymization methods. Managing Data Sources and Integration: Since data aggregation involves working with numerous sources, data integration can often be challenging owing to each source’s different format, semantics, and structure. A comprehensive data integration process can help you ensure seamless data compatibility regardless of your sources. Effective Data Aggregation: Considerations and Best Practices Focusing on these key considerations and best practices helps ensure effective and reliable data aggregation that results in quality data: Ensuring Data Quality and Consistency Data quality and consistency are of the utmost importance when aggregating data. Remember to monitor and validate your data to ensure it remains accurate, complete, and relevant. Choosing The Right Data Aggregation Methods You should also choose the correct aggregation methods, which will vary depending on the nature of your data and your objectives for your analysis. Data complexity, granularity, and volume are crucial when selecting a data aggregation technique. Documenting All Processes and Underlying Assumptions When aggregating data, document all processes and assumptions you use to obtain the aggregated results. Maintaining comprehensive documentation ensures transparency and repeatability, besides being valuable for troubleshooting and auditing. Performing a Sensitivity Analysis Conducting a sensitivity analysis determines how variations or uncertainties in the data affect the aggregated results. A typical sensitivity analysis involves the following: Identifying the key parameters or variables that can impact the aggregated data. Modifying those parameters within reason to simulate varying conditions. Analyzing how changes in these variables affect the aggregated results. Evaluating the aggregated results’ reliability under various scenarios. Documenting the sensitivity analysis process to gain insights into the aggregated data’s reliability. Data Governance and Compliance Inadequate data governance and compliance procedures can risk your data security, quality, and integrity. Prioritizing adherence to data governance standards keeps your business compliant with data regulations, improves auditability and transparency, and maintains data integrity. Scalability and Performance Your organization’s data aggregation should have scalability and performance at its core. These factors account for further growth and expansion. Additionally, they maintain aggregation efficiency even with growing datasets, improve productivity and mitigate bottlenecks, ensure optimal resource utilization, and future-proof your data aggregation process. Data Quality Assurance Data quality is central to every data management process. In data aggregation, focusing on maintaining data quality has multiple benefits. It ensures access to accurate insights, mitigates potentially expensive errors, improves decision-making, and elevates your organization’s reputation by proving your commitment to excellence. Engaging with Stakeholders Like many other organizational activities, data aggregation is most effective when you incorporate suggestions and input from stakeholders. Engaging with them while developing your data aggregation process helps you better understand varying departmental requirements, cultivate trust and buy-in, facilitate adoption and ownership, and foster collaboration to ensure alignment. Tools for Data Aggregation There are different kinds of data aggregation tools and technologies available to help you with data integration. Here’s a closer look at each of them: Spreadsheet Software Spreadsheet software provides elementary data aggregation functionality. You can organize, manipulate, and analyze data in tabular format within them. While they’re great for basic aggregation and ad-hoc analysis, a lack of advanced features prevents them from supporting large-scale data processing and automation requirements. For instance, a small business owner can use Microsoft Excel or Google Sheets to aggregate sales data from different store locations to calculate total revenue for the quarter. Database Management Systems Database management systems (DBMS) allow you to store, query, and aggregate large volumes of structured data. SQL databases support relational data aggregation, and NoSQL databases efficiently handle unstructured and semi-structured data. For example, if a financial institution needs to aggregate and analyze transactions from different branches to find possible fraudulent activity, it can use a DBMS. Business Intelligence Platforms Business Intelligence (BI) platforms offer advanced data aggregation and visualization features so you can analyze and present business data. Some of these features include reporting tools, dashboards, and data modeling. These platforms support complex data aggregation and analysis tasks across finance, operations, and marketing. For example, a retail company’s marketing department can use a BI platform for aggregating customer data from various channels — email, website, and social media. This aggregated data can help create customer segmentations and profiles, leading to more targeted marketing campaigns. Programming Languages and Libraries Programming languages can be powerful — albeit more difficult— tools for aggregating data. These languages feature extensive libraries for manipulating and aggregating data and performing statistical analyses. Consider a data scientist using Python and working with genomic data for a research project. They’ll use the panda’s library to analyze this data to identify genetic markers linked to a disease or medical condition. Data Integration Platforms Data integration platforms like LIKE.TG provide data aggregation solutions for collecting, cleansing, and integrating data from disparate sources. With features such as data connectors, transformation tools, and built-in workflow automation functionality, these platforms streamline data aggregation while maintaining data quality. For instance, a retail business can use LIKE.TG to summarize purchases across different cities Calibri (Body) in a purchase orders dataset to find information such as average, max, min, purchase amounts, and number of purchases across different cities. This aggregated data can help the business craft a more targeted marketing strategy. Uses of Data Aggregation Data aggregation is essential across different industries, seven of which are: 1. Financial Services By consolidating financial data from various sources, data aggregation enables thorough financial analysis and leads to informed investment decisions. Specific applications of data aggregation in finance include: Risk Management: Evaluating credit risk, operational risk, and market risk. Portfolio Management: Assessing asset allocation, portfolio performance, and investment strategy. Regulatory Compliance: Ensuring compliance with Markets in Financial Instruments Directive 2014 (MiFID 2), the Dodd-Frank Act, Basel III, and other regulations. 2. Retail and E-Commerce Analyzing aggregated data from different channels helps businesses in the retail and e-commerce domain with the following applications: Inventory Management: Maintaining optimum stock levels, minimizing stockouts, and lowering carrying costs. Sales Analysis: Evaluating product performance, sales trends, and customer behavior. Customer Segmentation: Developing targeted marketing campaigns, offering more personalized recommendations, and boosting customer retention rates. 3. Healthcare Aggregating and evaluating medical data helps healthcare facilities with the following: Improving Patient Care: Creating customized treatment plans to provide more targeted care and lead to better patient outcomes. Population Healthcare Management: Identifying patterns in population health, disparities in healthcare accessibility, and disease outbreaks. Studying aggregated data can also aid in developing community healthcare initiatives. Streamlining Clinical Research: Aggregated clinical trial data enables researchers to analyze patient demographics, treatment efficacy, and safety profiles for drug development and regulatory approval. 4. Manufacturing Different types of aggregated data in the manufacturing sector serve different uses: Production Planning: Assessing aggregated production data to optimize resource utilization, decrease downtime, and improve manufacturing processes. Supply Chain Management: Aggregated supply chain data enables businesses to monitor supplier performance, check inventory levels, and identify and correct supply chain bottlenecks. Quality Control: Aggregated quality control data aids in identifying flaws, determining their root causes, and applying corrective measures. 5. Marketing and Advertising Studying the insights gained from various kinds of aggregated data helps marketing teams with the following: Campaign Performance: Aggregated marketing campaign data is useful for measuring ROI, assessing customer engagement, and optimizing advertising spend. Market Research: Aggregated consumer data (such as survey responses, social media posts, and online activity) reveals market trends and consumer preferences. Customer Analytics: Aggregated customer data allows marketing personnel to create more accurate customer profiles and personas for targeted messaging. 6. Transportation and Logistics A variety of data is available for aggregation in the transportation and logistics sector. A few examples and uses include: Fleet Management: Aggregated vehicle and driver data helps optimize routes, lower fuel consumption, and increase fleet efficiency. Supply Chain Visibility: Aggregated logistics data aids in tracking shipments, monitoring delivery times, and inventory management. Demand Forecasting: Aggregated demand data can help predict future transportation needs, alleviate supply chain disruptions, and optimize resource allocation. 7. Government/Public Sector Data is available abundantly in the government/public sector — and in the context of data aggregation, here are a few ways they can leverage it: Policy Development: Aggregated economic, social, and demographic data can guide policy development, impact resource allocation, and help identify and resolve common public welfare challenges. Emergency Response: During emergencies, aggregated real-time data enables prompt response coordination and facilitates timely assistance. Performance Measurement: Aggregated government program data is crucial for evaluating program effectiveness, tracking outcomes, and maintaining accountability. Conclusion Aggregation offers you a uniquely comprehensive view of your data. It reveals insights that individual data points can’t deliver on their own. To derive maximum value from your data, you should design a well-planned aggregation process that aligns with your organizational requirements — and choose the right data aggregation tool. With its award-winning, highly acclaimed no-code interface and robust data management capabilities, LIKE.TG is the intuitive, user-friendly solution to your data aggregation problems. You can ingest data from various sources, use the Aggregate transformation object, and apply aggregate functions, such as Sum, Count, First, Last, Min, Max, Average, Var, or Standard Deviation, and load data to your preferred destination. LIKE.TG helps you make the most of your data by aggregating it for high-level insights. Start your FREE trial today, or contact our team for more information.

Data completeness plays a pivotal role in the accuracy and reliability of insights derived from data, that ultimately guide strategic decision-making. This term encompasses having all the data, ensuring access to the right data in its entirety, to avoid biased or misinformed choices. Even a single missing or inaccurate data point can skew results, leading to misguided conclusions, potentially leading to losses or missed opportunities. This blog takes a deep dive into the concept of data completeness, exploring its importance, common challenges, and effective strategies to ensure that datasets are comprehensive and reliable. What is Data Completeness? Data completeness refers to the extent to which all necessary information is present in a dataset. It indicates whether there are any missing values or gaps in the data. When all relevant data points are included, a dataset is considered complete. In contrast, incomplete data contains missing or empty fields, which can hinder analysis and decision-making. Examples of Incomplete Data Survey Data with Missing Responses Customer Database with Inconsistent Entries Financial Records with Incomplete Transactions The Importance of Complete Data When it comes to drawing conclusions and making informed decisions, data completeness matters more than businesses often realize. Data Completeness leads to: Improved Accuracy: Complete data ensures that analyses, models, and decisions are based on the most accurate representation of the situation. Incomplete data may lead to skewed results or erroneous conclusions. Increased Reliability: With complete data, findings and predictions gain higher reliability, minimizing the likelihood of errors stemming from data gaps and enhancing the trustworthiness of results. Optimized Decision-making: Complete data empowers decision-makers with the necessary information to make informed and timely decisions. It reduces uncertainty and enables stakeholders to assess risks and opportunities more accurately. Long-term Planning: Complete datasets support long-term planning efforts by providing reliable historical data, enabling organizations to identify trends and make informed projections for the future. Higher Customer Satisfaction: Complete data supports better understanding of customer needs and preferences, enabling organizations to tailor products, services, and experiences effectively. The Role of Data Completeness in Data Quality Completeness is one of the six primary dimensions of data quality assessment. Data quality is a broader term that encompasses various aspects of data, including completeness, accuracy, consistency, timeliness, and relevance, among others. It represents the overall condition of data and its fitness for use in a specific context or application. Data completeness, on the other hand, refers to the extent to which all required data elements or attributes are present and available in a dataset. Data completeness is a measure that directly affects the accuracy and reliability of data. When important attributes or fields are missing, it can lead to erroneous analyses and incorrect conclusions. Incomplete data may also skew statistical measures, such as averages or correlations, potentially leading to flawed insights. Rather than engaging in the data quality vs. data completeness debate, it is crucial to recognize that prioritizing data completeness is fundamental for ensuring high data quality. Data Completeness vs Data Accuracy vs Data Consistency Understanding the differences between data completeness, data accuracy, and data consistency is crucial for ensuring the quality and reliability of data in any organization. Here’s a comparison table highlighting the differences between data completeness, data accuracy, and data consistency: Aspect Data Completeness Data Accuracy Data Consistency Definition Presence of all required data elements or attributes in a dataset. Correctness, precision, and reliability of data values. Uniformity and conformity of data across different databases, systems, or applications. Focus Ensures all expected data points are present without any missing values. Ensures data values reflect real-world entities accurately and reliably. Ensures data remains synchronized and coherent across various sources or systems. Concerns Missing data points, gaps in datasets. Errors, discrepancies, inconsistencies in data values. Conflicts, contradictions, discrepancies between datasets or systems. Importance Essential for comprehensive analysis and decision-making. Critical for making informed decisions and accurate reporting. Vital for reliable analysis, preventing errors, and ensuring trust in data. Example Ensuring all sales transactions are recorded in a sales database. Verifying that customer contact information is correctly entered in a CRM system. Ensuring product prices are consistent across different sales channels. Mitigation Implementing data validation checks, data collection protocols. Data cleansing, verification against reliable sources. Implementing data integration strategies, synchronization mechanisms. How To Determine and Measure Data Completeness There are several approaches to assess data completeness, including attribute-level and record-level approaches, as well as techniques like data sampling and data profiling. Here’s an overview of each approach: Attribute-level Approach In the attribute-level approach, each individual data attribute or field within a dataset is examined to determine its completeness. To measure completeness at this level, users can calculate the percentage of non-null or non-missing values for each attribute. For categorical attributes, users may also look for the presence of all expected categories or values. Example: A dataset contains customer information, including attributes like name, age, email, and phone number. To measure completeness at the attribute level, one would examine each attribute to see how many records have missing values. For instance, if 90% of the records have a value for the “age” attribute, but only 70% have an email address, the email attribute would be considered less complete. Record-level Approach In the record-level approach, entire records or rows of data are evaluated for completeness. This involves assessing whether each record contains all the necessary attributes or fields, and if those fields are populated with meaningful data. Completeness can be measured by calculating the percentage of fully populated records in the dataset. Example: Continuing with the customer information dataset example, with the record-level approach, each record is assessed as a whole. If a record is missing any essential attribute (e.g., name or email), it would be considered incomplete. For instance, if 70% of records have non-null name and email, the dataset will be 70% complete. Data Sampling Data sampling involves selecting a subset of data from the larger dataset for analysis. Sampling can be random or stratified, depending on the characteristics of the dataset and the objectives of the analysis. By analyzing a sample of the data, you can infer the completeness of the entire dataset, assuming the sample is representative. Example: Let’s say there’s a massive dataset with millions of records. Instead of analyzing the entire dataset, one might randomly sample 1,000 records and assess completeness within this sample. If the sample is representative of the overall dataset, findings can be extrapolated to estimate completeness across the entire dataset. Data Profiling Data profiling is a systematic analysis of the structure, content, and quality of a dataset. It involves examining various statistical properties of the data, such as distributions, frequencies, and summary statistics. Profiling can help identify frequency of missing values, outliers, duplicates, and other data quality issues that may affect completeness. Tools like histograms, summary statistics, frequency tables, and outlier detection algorithms can be used for data profiling. Example: Using data profiling tools or techniques, one can generate summary statistics and visualizations to identify frequency of missing values across different attributes. For instance, a histogram could be generated showing the distribution of missing values for each attribute or calculating the percentage of missing values for each attribute. 5 Common Challenges in Ensuring Data Completeness Data Entry Errors: Human errors during data entry, such as typos, missing values, or incorrect formatting. Incomplete datasets may contain missing values due to various reasons, including equipment malfunctions, respondent non-response, or data collection errors. Data Integration Issues: Combining data from multiple sources can cause incompatibilities in data structures or identifiers, which can lead to incomplete or inconsistent datasets. Data Quality Control: Inadequate quality control processes can lead to incomplete data, as errors may go undetected during data collection or processing. Lack of Data Governance: Absence of clear data governance policies and procedures can result in inconsistent data definitions, ownership issues, and poor data management practices, ultimately leading to incomplete datasets. Obsolete Data Systems and Architectures: Inadequate infrastructure or outdated technologies may hinder data collection, processing, and storage. Incomplete data sets can also be due to data privacy regulations and compliance requirements which may limit access to certain data. Strategies to Ensure Data Completeness Establish Clear Data Entry Protocols: Organizations should develop clear guidelines and protocols for data entry to ensure consistency and accuracy. This includes defining data fields, formats, and validation rules to minimize errors during data entry. Implement Data Validation Checks: Automated data validation checks should be implemented to identify incomplete or inaccurate data entries in real-time. This can include range checks, format checks, and cross-field validations to ensure data accuracy and completeness. Regular Data Audits: Conducting regular audits of the data can help identify incomplete or missing data points. These audits should involve comparing the dataset against predefined standards or benchmarks to ensure completeness and accuracy. Use Data Profiling Tools: Data profiling tools can access the contents of a dataset, providing statistics such as minimum and maximum values, unique value count, missing value count etc. By leveraging these tools, organizations can proactively address data completeness issues and take corrective actions. Implement Data Quality Monitoring: Establishing a robust data quality monitoring process allows organizations to continuously monitor the completeness of their data. Alerts and notifications can be set up to flag any deviations from expected data completeness levels. Incorporate Data Governance Policies: Implementing data governance policies ensures that data completeness requirements are clearly defined and enforced across the organization. This includes assigning responsibilities for data stewardship and establishing processes for data quality management. Data Enrichment Strategies: In cases where data completeness is compromised, organizations can employ data enrichment techniques to fill in missing data points. This may involve integrating external data sources or using algorithms to extrapolate missing values based on existing data. Using Automated Tools for Complete Data Automated tools play a crucial role in ensuring the completeness and reliability of data across various domains. These tools facilitate the collection, processing, and analysis of large datasets efficiently, enabling organizations to derive valuable insights and make informed decisions. By automating tasks such as data cleaning, integration, and analysis, these tools streamline workflows and minimize errors, resulting in more accurate and actionable information. Additionally, automated data visualization enables stakeholders to understand complex patterns and trends quickly, facilitating communication and decision-making processes. Moreover, automated tools help organizations maintain data security and compliance with regulations, mitigating risks associated with data handling. LIKE.TG: Ensuring Data Completeness with Advanced No-Code Data Management LIKE.TG offers an end-to-end no-code data management platform equipped with advanced and automated capabilities for data integration, extraction, and preparation. With a wide range of features, LIKE.TG empowers users to create and maintain automated data pipelines that deliver accurate and timely data. With LIKE.TG, users can seamlessly extract and cleanse data from unstructured sources, leveraging AI-powered document processing capabilities. Users can effortlessly integrate data from diverse file sources and database providers, supported by a data pipeline builder that accommodates various formats, systems, and transfer protocols. This reduces the challenge of incompatibilities in data structures or identifiers, which often lead to incomplete or inconsistent datasets. Through the LIKE.TG Dataprep feature, users can cleanse, transform, and validate extracted data with point-and-click navigation, supported by a rich set of transformations including join, union, lookup, and aggregation. With attributes like active profiling, data quality rules, and preview-centric grids, LIKE.TG ensures data cleanliness, uniqueness, and completeness, providing users with attribute-level profile and vivid graphical representations to easily identify patterns of completeness or lack thereof. LIKE.TG also offers ease of integration, allowing users to effortlessly utilize cleaned and transformed data in analytics platforms, thus enabling informed decision-making based on comprehensive and reliable data. Achieve data completeness effortlessly with LIKE.TG today – Book a personalized demo now!

Ambient marketing is a way of promoting products or services using unusual locations or items and their elements to your advantage as a communication channel. It involves creative advertising messaging, interacting with the target audience, and should affect consumers on an emotional level. In this video, you can see an ambient marketing campaign from Coca-Cola. The company put a special Coke machine at a bus stop in a Swedish city. Why should brands consider ambient marketing? According to MarketingWeek, 65% of UK consumers say that diversity in advertising formats is important to them. This indicates that modern marketers think not only about traditional ads but non-standard ways to promote their products as well. When conventional advertising is no longer surprising enough, ambient marketing comes to the rescue. The term ‘ambient media’ appeared in Great Britain in the 1990s and was defined as visual advertising in uncommon places. When developing an ambient ad, paying considerable attention to creativity and using an unconventional approach to provide deeper contact with prospects and customers is important. It is important that people passing by stop, look at it, and tell their friends about what they saw. Ambient ads are considered a more effective product promotion than conventional outdoor advertising. Studies have shown that billboard advertisers have a vehicle passenger’s attention for about six seconds, while creative advertising can increase this indicator to a minute or even more. Also, it helps improve the ad’s effectiveness and can be an integral part of social media marketing. On the one hand, brands can create an unconventional ad and share it on social networks using a hashtag. On the other hand, people can take pictures, make videos of this creative advertising and share it on social media using the brand's hashtag, thereby building brand awareness. Now that you know why companies should use ambient ads in their campaigns, let’s walk you through the benefits of this type of brand promotion. Advantages of Ambient Marketing Grabs attention Can go viral Low cost Non-intrusive Reaches a wider audience Memorable Creative ambient ads that convey a strong message are unique in design and appear in unexpected locations to make people talk about your brand. Here are several benefits of using ambient marketing for your campaign. Grabs attention In our modern world, where there is an incredible amount of advertising, it is really difficult to stand out to attract prospects or customers. That is why companies should come up with something brand-new and unique. It is a well-known fact that to grab people’s attention, you should create a positive emotional feeling, surprise consumers, and build an emotional roller coaster. Here comes ambient advertising that can draw attention by creating interesting objects, unusual billboards, and useful items, and placing them in unexpected locations. Can go viral If your ad affects people emotionally and has distinctive features and practical value, it can easily go viral. In a world shaped by technology, people can easily take photos and videos and share them on social media like Twitter, YouTube, Instagram, and Facebook. This is how you can expand your audience. This doesn’t mean that it can immediately boost your sales, but it can boost your brand recognition at the very least. Low cost An ambient ad is about using unconventional promotional methods to engage with the audience. This means that it won’t cost you much to implement such advertising into your marketing strategy. Find a creator for your future ambient ad and decide on a perfect location. Non-intrusive One of the main benefits of ambient ads is that they convey a brand's message clearly and don't enter customers’ personal space. With some unique constructions, murals, and unusual objects on the streets, you can’t make people become your customers. However, if you manage to intrigue people passing by, they may tell others about your brand. Their friends and colleagues are more likely to purchase your product of their own free will. Reaches a wider audience Usually, marketers choose popular places or busy streets to put their ambient ads. The more people see it, the better for a brand. With the help of this type of marketing, you can reach not only your customers but also prospects or people just passing by. The object that conveys your marketing message can build brand awareness for different audiences. Memorable Ambient ads allow people to see your brand from a completely new side. Such campaigns evoke strong emotions using experimental promotion techniques, so consumers remember a brand. Sometimes this form of advertising can evoke emotions such as shock or fear to achieve brand awareness. Let's look at several ambient marketing examples from well-known brands. This will give you the opportunity to see this type of marketing in action.

Ambush marketing is a strategy that involves a brand team trying to associate their products or services with a big event that already has official companies-sponsors. In most cases, it happens during sports events and aims to raise brand awareness of a particular company with no sponsorship rights. Why do businesses use ambush? The majority of ambush campaigns have one aim, which is to associate their company with the fame of a certain event without signing a sponsorship contract. Ambush marketing presents a rather unusual approach to creating a marketing campaign and, at the same time, includes full creative freedom and flexibility. Brands use various visual forms and wordplay to respond to marketing campaigns created by official sponsors of an event. Since such campaigns are extremely entertaining, they are more likely to attract the masses and are easy to remember. With this strategy, companies often employ methods that go beyond the scope of a brand's established guidelines. You can use ambush marketing in different ways, starting from PPC ads in search results to giant billboards on the most crowded city's streets. Now that you know some of the benefits your brand can reap after implementing ambush marketing, it's time to find out whether it's lawful to use. Is ambush marketing legal? By using this marketing technique the wrong way, companies can get involved in unlawful actions such as infringing the event owner's trademarks, copyright, and other rights of a particular event. However, if implemented correctly, it can bring several benefits to your business, which some famous brands have proven true. When developing an ambush marketing campaign without any official right to use an event for your promotion, don't: use actual names, logos, slogans, or branding of events; mention words "Sponsor", "Partner" or "Supporter" concerning this event; conduct giveaways and give people tickets to the event. Since it's trendy in sports, many actions are directed against this marketing strategy in this industry. These measures can be found in major sports events that aim at protecting exclusive sponsorships and their organizers' intellectual property rights. As a result, these regulations can limit the freedom of speech and obstruct brands from promoting themselves with the help of a certain event. It's crucial to follow several steps to legally and successfully implement this creative marketing technique without violating various rights. Let's dive into the pros and cons of ambush marketing if you are considering it for your next marketing campaign. Advantages and Disadvantages of Ambush Marketing Like any marketing strategy, ambush advertising has advantages and disadvantages. Let's take a closer look at them. The advantages are as follows: cost-effectiveness; improved brand equity; freedom and flexibility; an opportunity for startups to jump-start their business; a higher competition that brings lower prices for consumers; a creative approach; a direct response to a campaign; various forms and sizes, starting from a mobile sidebar ad to huge billboards in a city center; companies can go off-script from their initial content, style, etc. All marketing methods have drawbacks, and this approach isn't an exception. It has the following disadvantages you should be aware of before implementing it: makes it difficult to calculate ROI; requires quick response and detailed planning; relies on time and space; violates the rights of actual sponsors and partners of an event; relies solely on competitors' ads to address them. Knowing the advantages and disadvantages allows you to decide whether this marketing strategy suits your business. The next step is to determine which type is the best for your brand. Types of Ambush Marketing The strategy can be divided into two main types: direct marketing; indirect marketing. In direct ambush marketing, a brand's team aggressively uses various activities to associate themselves with an event without being a sponsor or partner. There are four types of direct marketing: predatory ambushing is a technique that includes one brand attacking another (an official sponsor of an event), which prevents people from identifying an actual sponsor; coattail ambushing is a strategy where a brand aims at gaining exposure by becoming a sponsor of a person who participates in the event; property or trademark infringement is when a brand uses logos, symbols, or phrases of another brand to promote its services that often leads to confusion among customers and diminishes the marketing efforts of the property's owner; self-ambushing means that a brand has sponsorship rights but gets involved in activities not mentioned in the contract. Let's review self-ambushing in more detail. The situation with the 2008 UEFA European Championship and Carlsberg is a great example. The famous beer brand, an official sponsor, did something not stated in their sponsorship contract. During the tournament, the brand gave out free T-shirts and headbands. Indirect ambush marketing doesn't necessarily involve association with an event but rather tries to align a company through a suggestion or reference to it. This strategy doesn't include a brand that attacks another brand but allows you to gain exposure.