The financial services industry is constantly evolving, and businesses need to keep up with the latest technology to stay competitive. One important tool that can help financial services businesses succeed is a customer relationship management (CRM) system. Throughout this blog, we will explore the importance of CRM for financial services businesses, and how a CRM can help improve customer service, streamline sales, and enhance collaboration. What Is a CRM for Financial Services? The financial services and insurance industry is one that constantly changes, so having the right tools to manage customer relationships and interactions is paramount to success. This is where CRM for financial services comes into play. CRM stands for Customer Relationship Management and refers to the technology and strategies employed by businesses to manage and analyse customer interactions and data throughout the customer lifecycle. Specifically designed for financial institutions, a CRM system for financial services is a software platform that centralises customer data, sales, marketing, customer service, and support activities. This centralised platform allows financial institutions to gain a holistic understanding of their customers and deliver exceptional client service. By leveraging the capabilities of a CRM system, financial institutions can streamline their operations, enhance collaboration among teams, and ultimately, drive business growth. Integrating CRM with other tools like Outlook and LIKE.TG can save time and reduce human error, ensuring that data is accurately copied and managed across platforms. CRM systems for financial services address the unique needs of the industry, ensuring compliance with regulatory requirements, data security, and customer privacy. With a CRM system in place, financial institutions can efficiently manage customer information, track interactions, and make informed decisions based on data-driven insights. This leads to improved customer experiences, increased operational efficiency, and a competitive edge in the market. How Can a Finance CRM Help Your Business? A CRM system can help financial services businesses improve customer service by providing a centralised platform for managing all customer interactions. This allows businesses to track customer history, preferences, and interactions, and to provide personalised and efficient service. This reduces customer frustration when contacting their service provider. For instance, a financial advisor can use a CRM to quickly access a client’s account information, view their past transactions, and make notes about their financial goals. This enables the advisor to provide tailored advice and recommendations that meet the client’s specific needs. Additionally, a CRM can help automate tasks such as sending appointment reminders and following up on leads, freeing up advisors to focus on providing excellent customer service. A CRM can also help financial services businesses streamline sales by providing tools for managing leads, tracking opportunities, and forecasting revenue. This can help businesses identify and prioritise high-value leads, allocate resources effectively, and close deals faster. For example, a CRM can automatically score leads based on their likelihood to convert, allowing sales teams to focus their efforts on the most promising opportunities. Additionally, a CRM can provide insights into the sales process, such as which marketing channels are generating the most leads or which sales tactics are most effective. This information can help businesses optimise their sales strategies and improve their bottom line. Finally, a CRM can help financial services businesses enhance collaboration by providing a central platform for sharing information and communicating with colleagues. This can help break down silos between departments and improve teamwork. For instance, a customer service representative can use a CRM to quickly get in touch with a subject matter expert to resolve a customer issue. Additionally, a CRM can be used to track and manage projects, ensuring that everyone is on the same page and that projects are completed on time and within budget. In conclusion, a CRM system can provide numerous benefits for financial services businesses, including improved customer service, streamlined sales, and enhanced collaboration. By leveraging the capabilities of a CRM, financial institutions and clients can gain a competitive advantage and achieve long-term success. How Can a Finance CRM Help Your Customers? A finance CRM can provide customers with a secure and convenient way to manage their finances. It can help them track their spending, set financial goals, make informed financial decisions, and stay up-to-date on their financial accounts and transactions. By providing customers with a centralised platform for managing their finances, a CRM can help them improve their financial literacy and make better financial decisions. For example, a finance CRM can help customers track their income and expenses, create budgets, and set savings goals. It can also provide customers with insights into their spending habits and help them identify areas where they can cut back. Additionally, a finance CRM can help customers stay up-to-date on their financial accounts and transactions by providing them with real-time notifications and alerts. Overall, a finance CRM can be a valuable tool for customers looking to manage their finances more effectively. It can provide them with the information and tools they need to make informed financial decisions and improve their financial literacy. The Features You Need From a CRM for Client Management in Financial Services A CRM for financial services should have robust contact management capabilities. This includes the ability to store and manage customer contact information, such as name, address, phone number, and email address. It should also allow users to create and manage contact groups, such as customers, prospects, and vendors. Lead management is another important feature for a CRM for financial services. This includes the ability to track leads from the initial point of contact through the sales process. It should also allow users to qualify leads, assign them to sales representatives, and track their progress. Opportunity management is also essential for a CRM for financial services. This includes the ability to track sales opportunities from the initial contact through the close of the sale. It should also allow users to create and manage sales forecasts, see interest rates, track win rates, and identify trends. Integration with other business systems is also important for a CRM for financial services. This includes the ability to integrate with accounting systems, ERP systems, and other business applications. This allows users to access all of their customer data in one place, which can help companies improve efficiency and productivity. Reporting and analytics are also important features for a CRM for financial services. This includes the ability to generate reports on customer activity, sales performance, and other key metrics. It should also allow users to create custom reports and dashboards, so they can track the metrics that are most important to them. How to choose the best financial CRM for your business? When selecting the most suitable CRM for your financial services business, there are several factors to consider. First, it is essential to identify your specific CRM requirements. Determine the key features and functionalities that are essential for your business, such as customer data management, sales tracking, and reporting capabilities. This will help narrow down your options and select a CRM that aligns with your specific needs. Next, it is important to set a budget for your CRM investment. CRM systems can vary significantly in cost, so it is crucial to determine how much you are willing to spend before beginning your search. Consider the long-term return on investment (ROI) when making your decision, as a well-chosen CRM can lead your company to increased efficiency, improved customer service, and ultimately, increased revenue. The size of your business is also an important factor to consider when choosing a CRM. Smaller businesses may have different needs and requirements compared to larger enterprises. Look for a CRM that is scalable and can grow with your business as it expands. Consider the number of users who will need access to the CRM’s ability, and ensure that the system can accommodate your current and future needs. The industry in which you operate can also influence your CRM selection. Different industries have specific requirements and regulations, so it is important to choose a CRM that is tailored to your industry’s needs. For example, a banking or financial services CRM should have features that support compliance with industry-specific regulations and provide the necessary security measures to protect sensitive customer data. Finally, it is advisable to read reviews of different CRM systems before making a decision. Customer reviews and feedback can provide valuable insights into customer satisfaction and the pros and cons of various CRM solutions. Look for reviews that highlight the strengths and weaknesses of each CRM and consider how they align with your business requirements. By carefully evaluating these factors and conducting thorough research, you can select the best financial CRM for your business and reap the benefits of improved customer service, streamlined sales, and enhanced collaboration. Key features of CRM for financial services Contact management is a key feature of CRM for financial services. It enables financial institutions to centralise and manage customer information, including personal details, contact information, and financial data, in a single, easily accessible platform. This allows financial advisors and customer service representatives to quickly access customer information, understand their financial needs, and provide personalised advice and services. Lead management is another important feature of CRM for financial services. It helps financial institutions capture, qualify, and track leads, and manage the sales pipeline. With a CRM system, financial advisors can easily capture lead information, such as contact details, interests, and financial goals, and track their progress through the sales funnel. This enables them to prioritise high-quality leads, allocate resources effectively, and optimise their sales efforts. Task management is also a valuable feature of CRM for financial services. It allows financial institutions to assign, track, and manage tasks and activities related to client management, customer interactions, sales, and service. Financial advisors can use the task management feature to create to-do lists, set priorities, and track their progress in completing tasks. This helps them stay organised, manage their time effectively, and ensure that no important tasks fall through the cracks. Reporting and analytics is another essential feature of CRM for financial services. It enables financial institutions to generate reports and analyse data related to customer interactions, sales, and service performance. Financial advisors can use these reports to gain insights into customer behaviour, identify trends, and measure the effectiveness of their sales and service strategies. This information can help them make data-driven decisions, improve their performance, and achieve better business outcomes. Finally, integration with other financial software is a key feature of CRM for financial services. It allows CRM systems to connect and exchange data with other financial software applications, such as accounting systems, portfolio management systems, and trading platforms. This integration enables financial institutions and organisations to streamline their operations, reduce manual data entry, and improve the accuracy and efficiency of their financial processes. Best financial CRM software When it comes to choosing the best financial CRM software for your business, there are many factors to consider. Some of the top financial CRM software solutions on the market for Financial Services. Each of these solutions offers a unique set of features and benefits, so it’s important to evaluate your specific needs and requirements before making a decision on one platform. LIKE.TG Financial Services Cloud is a comprehensive CRM solution that offers a wide range of features specifically designed for the financial services industry. These features include contact management, lead management, opportunity management, forecasting, and reporting. LIKE.TG Financial Services Cloud also integrates with other LIKE.TG products, such as Sales Cloud and Service Cloud, to provide a complete customer experience management solution. Oracle Financial Services Cloud is another CRM solution for the financial services industry. Oracle Financial Services Cloud offers a comprehensive set of features, including contact management, lead management, opportunity management, forecasting, and reporting. Oracle Financial Services Cloud also integrates with other Oracle products, such as Oracle E-Business Suite and Oracle Siebel CRM, to provide a complete customer experience management solution. Microsoft Dynamics 365 for Financial Services is a cloud-based CRM solution that offers a range of features designed for the financial services industry. These features include contact management, lead management, opportunity management, forecasting, and reporting. Microsoft Dynamics 365 for Financial Services also integrates with other Microsoft products, such as Office 365 and Dynamics 365 Sales, to provide a complete customer experience management solution. SAP Hybris Cloud for Financial Services is a cloud-based CRM solution that offers a comprehensive set of features specifically designed for the financial services industry. These features include contact management, lead management, opportunity management, forecasting, and reporting. Pegasystems CRM for Financial Services is a cloud-based CRM solution that offers a range of features specifically designed for the financial services industry. These features include contact management, lead management, opportunity management, forecasting, and reporting. Pegasystems CRM for Financial Services also integrates with other Pegasystems products, such as Pegasystems BPM and Pegasystems Customer Service, to provide a complete customer experience management solution.

Are the fundamental building blocks of your business kept in spreadsheets? Fast insights into sales pipeline and dashboards, revenue forecasting, customer information, and lead management are crucial to a sustainable business. Learn why it may be time to move beyond spreadsheets to position your small to medium-sized business (SMB) for growth. There comes a point in the growth of a SMB when the business owner will look at the vast amount of spreadsheets that contain critical data and think: there must be a better way. This reality check comes quicker to leaders during times of change as they’re forced to juggle competing priorities. According to LIKE.TG’s latest Small and Medium Business Trends Report, leaders say the shift in customer expectations for online transactions have driven 63% of SMBs to now manage an ecommerce presence. Investing in digital capabilities has become the keyfocus for many SMBs — 71% of SMB leaders say their business survived the pandemic because of digitisation. Their top motivators to focus on digital transformation are increasing productivity, improving business agility, and enhancing data security. The use of technology not only helps them establish a foundation for growth but also build better relationships with their customers and stakeholders. For businesses that are still using spreadsheets such as Excel to host important data, it is now time to move beyond manual processes and choose automation. Here’s why. CRMs give SMBs what matters — time and efficiency Creating one-to-one relationships with customers is what sets a SMB apart from its competitors. Without a customer relationship management (CRM) system that efficiently manages business data, SMB leaders will often go to bed feeling anxious about customers and prospects they haven’t responded to. For any interactions that day, they’ll need to manually add details to the right spreadsheet on what was covered and when to follow up. The change from spreadsheets to a CRM software is when business leaders can step out from working within the business and find the time to work on the business. With intelligent automation processes, they will have the ability to build a diverse and comprehensive customer reference database that supports marketing, sales, and service. Unlike spreadsheets, a CRM makes it easy to automate certain tasks so your team can focus on high-value business. All those hours of manual data entry and the stress of remembering previous customer conversations will be taken away, giving business leaders back the resource that matters — time. The costs of using spreadsheets over CRM systems To put this into perspective, how much does one hour cost to a business leader who is working with a customer? Think about the hours per week spent updating, collating, and organising spreadsheets. Is the reliance on spreadsheets and monotonous data entry detrimental to revenue and growth? What if all these manual tasks could be integrated in one system? How would that change the cost of one hour? CRM 101: The Small & Medium Business’s Guide to build your business strategy and maximise ROI LIKE.TG’s small business CRM gives leaders the tools to capture new leads and effectively manage their prospects, right through the sales cycle to becoming an active customer. The personalised email templates provide consistent messaging and intuitive activity tracking ensures that no conversation is lost or forgotten. Combined with CRM analytics, it allows SMBs to seamlessly access, collaborate, and share insights from anywhere. As such, businesses can win deals and keep their customers happy in one unified 360 platform. With these features easily accessible on desktop, laptop or mobile devices, LIKE.TG enables small to medium-sized business leaders to focus time and energy on marketing their brand, strengthening relationships with key customers, and looking at future growth opportunities for the business. Don’t hold back your business with manual data processing and disconnected spreadsheets. It’s time to reassess whether your processes are driving growth or holding you back. Level-up with a CRM software that gives you back time to work on what matters most. This post was based on the original at the A.U. LIKE.TG blog.

Over the last two decades, communications services providers (CSPs) have faced a two-fold cash flow squeeze. First, accelerated adoption of competing OTT (Over-The-Top) service offerings – such as voice calls and messaging through WhatsApp – have put downward pressure on consumer revenue. Second, increasing spends on generational technology advancements every few years – like 4G to 5G network upgrades and fibre rollouts to address increased data consumption demands – have put upward pressure on costs. So it’s logical for CSPs to seek alternative strategies to maintain healthy margin levels and retain market foothold. Mergers and acquisitions (M&As) are a powerful strategy CSPs use to achieve this. How M&As are reshaping the APAC communications market APAC markets are a diverse mix of prepaid and postpaid, characterised by a blended mobile ARPU (Average Revenue Per User) – as low as USD $3 in Indonesia and India, and up to USD $30 in Australia. At the same time, the prepaid heavy markets have an extremely cost savvy subscriber base that churns easily from one provider to next. Consumers are lured by lower costs or value-added incentives such as free data roaming packages or unlimited local calls. And most countries have high teledensities – as high as 145% in Singapore, 124.8% in Australia, and 113.9% in New Zealand – that leave limited headroom for net new customer acquisition. These unique market dynamics have powered a surge in M&A activity that has reshaped the marketplace and created new market leaders throughout the region. True Corporation and Total Access Communication (dTac) in Thailand, for example, created a new company with an enterprise value exceeding USD $20 billion. That’s only one of several examples. Celcom and Digi in Malaysia formed the largest mobile services provider in the country. Indosat and Hutchinson in Indonesia created the country’s second largest service provider with more than 100 million subscribers. And the Telkomsel and IndiHome merger in Indonesia resulted in expected annualised savings of USD $330 million. In Australia, the merger between Vodafone Hutchison and TPG Telecom created an enterprise value of USD $4.9 billion for Vodafone. And in neighbouring New Zealand, 2degrees and Vocus joined forces to form the country’s third largest service provider with an annual turnover of more than USD $1 billion. The same can be seen in India. When Vodafone India and Idea Cellular merged a few years ago, the combined entity emerged as the market leader with nearly 400 million subscribers. M&As have enabled each of these players to establish a 50% or above market share in their markets of operation. Increasing customer stickiness and wallet share with M&As In addition to an increase in market share, M&A activities typically enable CSPs to increase customer stickiness where subscribers use a mix of volatile prepaid services (such as mobile data) from one provider, and highly retentive postpaid services (like fibre broadband) from another. Tapping in on each party’s offerings generates cross-sell opportunities CSPs use to increase customer wallet share and retention. On the spend side of the equation, M&A activities tend to free up capital by reducing or eliminating spend on overlapping infrastructure. CSPs can then choose to use such capital to develop and market innovative products in the information and communities technology (ICT) space (such enterprise apps, IoT, and data centres), or develop partnerships with other industry service providers (like digital banks and micro insurance companies). The critical need for an integrated customer view Achieving a successful M&A in the communications industry presents several challenges. For CSPs with legacy systems, realising the business benefits of an M&A requires the rationalisation and integration of business strategies, customer facing and internal functions, product offerings, business processes, and IT stacks. However, delivering high-quality customer service over the course of the rationalisation period – and beyond – is key to retaining customers across the merging companies. In scenarios where a customer is consuming products from both merging companies, having an integrated view of the customer becomes crucial to achieving this goal. Gavin Barfield, VP Solutions and CTO ASEAN at LIKE.TG, makes the point that M&As provide an opportunity to retire legacy technology and embrace modern technology stacks. Solving the integration puzzle with a 360-degree customer view Developing this integrated 360-degree customer view requires systems integration and normalisation of data across product offerings, sales transactions, inflight orders, customer’s assets, trouble ticket histories, and more. LIKE.TG Customer 360 provides an integrated view of each customer, across multiple functions, products and systems. This view is what communications companies’ marketing, sales, contact centre and field service teams require for day-to-day operations, and to maintain business-as-usual – or better. For example, marketing teams enabled with deep customer insights from Customer 360, can review customer segments, customer spend and preferences to develop attractive cross-sell and up-sell offers for the new acquired customer base. Sales and customer service teams can also review customer sentiment to inform meaningful conversations with customers from the merging organisations, and address customer concerns with the right insights at hand. Singtel in Singapore is one CSP that’s using such data insights to understand and prioritise its customers’ needs in a complex, hyper-connected and fast-changing world. Rationalising business processes with a connected CRM Integrated CRM platforms also enable the rationalisation of business processes during M&A activity. When CRM platforms are served over a connected user interface, it enables seamless handovers across internal functions. For example, when a sales representative requests pricing approvals for mobility and connectivity products, their manager uses the same connected interface to review and approve the pricing. Solution specialists use the same interface to review the overall solution construct for consistency and coherency. Sales Ops uses the same view to review quote accuracy, and can derive weekly forecast reports using a single data instance. Such simplification allows identification of redundant or unnecessary business processes that are candidates for transformation during the rationalisation process. Leveraging AI technologies to enable sales and service teams Gavin Barfield is seeing more communications companies increasingly motivated to embrace generative artificial intelligence (AI) to lead innovation and stay ahead. That’s largely because AI technologies provide relevant and contextual information – during the sales stages and customer service engagement – that enables sales and service teams to meet their customers where they are. Customer 360 uses LIKE.TG Einstein AI technologies to leverage the full power of this kind of data analytics. Let’s take an example of a high-value subscriber who uses different prepaid SIM cards for voice and mobile gaming from two merging CSPs. AI could potentially suggest an up-sell to a 5G plan with more voice minutes and gigabytes for this subscriber, and the latest bluetooth earphones to enhance the subscriber’s gaming experience. Additionally, based on customer demographics and preferences across similar customers, AI could suggest an Instagram and WhatsApp add-on for a few extra dollars. Such examples not only generate increased wallet share, but also project the CSP as an intelligent organisation that understands and wows its customer throughout the customer lifecycle. From business transformation, process harmonisation and operational streamlining to increasing customer delight and wallet share, CRM platforms help CSPs across all stages of the M&A journey, and ensure long-term business success.

Customer acquisition is the lifeblood of any business. It’s the process of bringing in new customers and growing your business. Without a steady stream of new customers, your business will eventually stagnate and die. In this comprehensive guide, we’ll cover everything you need to know about an effective customer acquisition strategy, from the basics to advanced strategies. We’ll discuss what customer acquisition is, why it’s important, and how to create an effective and sustainable customer acquisition strategy. We’ll also explore the different channels you can use to acquire customers and how to measure your success. By the end of this guide, you’ll have the knowledge and tools you need to develop a successful customer acquisition strategy for your business. What is customer acquisition? Customer acquisition is the lifeblood of any business. Simply put, it is the process of identifying and acquiring new customers. As the first stage in the customer lifecycle, a customer acquisition plan involves creating awareness of your product or service, generating leads, and converting those leads into customers. Every business needs a steady stream of new customers to survive and grow. Without a consistent influx of fresh faces, your business will eventually stagnate and eventually cease to exist. Customer acquisition is an ongoing process that requires businesses to constantly be on the lookout for new ways to reach and engage with potential customers. Customer Acquisition and the Customer Lifecycle Customer acquisition is the first stage in the customer lifecycle, which is the journey a customer takes from the moment they become aware of your business until they become a loyal customer. The customer lifecycle can be divided into four main stages: 1. Awareness: This is the stage where potential customers first become aware of your online business, and what you have to offer. 2. Consideration: This is the stage where potential customers are considering your product or service as a solution to their needs. 3. Conversion: This is the stage where potential customers make the decision to purchase your product or service. 4. Retention: This is the stage where you focus on keeping your customers happy and satisfied so that they continue to do business with you. Customer acquisition is the key to moving potential customers through the customer lifecycle and ultimately turning them into loyal customers. Why is customer acquisition so essential? Customer acquisition holds significant value for enterprises across all stages and sizes. This process enables your company to: Generate revenue to cover expenses, compensate staff, and fund further expansion, and Demonstrate growth and momentum to external stakeholders like investors, partners, and key influencers. The ability to consistently draw in and secure new clients is an essential for maintaining the vitality and expansion of businesses, ensuring investor confidence in the process. What is the purpose of customer acquisition? Customer acquisition is the process of identifying and acquiring new customers. It is an important part of any business’s growth strategy and can have several key benefits for a business. Some of the main benefits of paid customer acquisition strategies include: – Increasing the number of satisfied customers a business has: This can lead to increased revenue and profit, as well as a larger customer base to which the business can market its products or services. – Increasing revenue and profit: Acquiring new customers can directly increase a business’s revenue and profit. This is because new customers can purchase products or services from the business, increasing the business’s overall sales. – Building brand awareness and customer loyalty: Acquiring new customers can help build brand awareness and loyalty. This is because when new customers have a positive experience with a business, they are more likely to return for future purchases and become loyal customers. – Entering new markets or expanding into new customer segments: Acquiring new customers can help a business enter new markets or expand into new customer segments. This can help the business grow its customer base and reach new customers who may not have been aware of the business before. – Increasing market share: Acquiring new customers can help a business increase its market share. This is because when a business acquires new customers, it takes away market share from its competitors. What is acquisition marketing? Acquisition marketing focuses on attracting new customers or clients to your business. It encompasses various strategies and channels aimed at generating leads and converting them into paying customers. The primary goal of any customer acquisition channel or marketing is to increase your paying customer base and boost revenue. One of the key elements of acquisition marketing is lead generation. This involves identifying potential customers who have shown interest in your products or services. This can be done through various channels such as online advertising, social media marketing, content marketing, search engine optimisation (SEO), and email marketing. By creating engaging and relevant content, you can attract potential customers and encourage them to provide their contact information, thus becoming leads. Once you have generated leads, the next step is to nurture them and convert them into customers. This can be done through personalised email campaigns, follow-up phone calls, or providing prospective customers with additional resources and information to help them make informed decisions. By building relationships and trust with potential customers, you can increase the likelihood of converting them into paying customers. Acquisition marketing involves ongoing marketing efforts, to continuously attract and acquire new customers. It requires a combination of effective strategies, understanding your target audience, using customer acquisition techniques and analysing customer behaviour. By implementing a well-executed acquisition marketing plan, you can expand your customer base, grow your business, and achieve long-term success. The customer acquisition funnel is a model that businesses use to understand and track the customer journey from awareness to purchase. The funnel is divided into five stages: awareness, interest, consideration, decision, and retention. At the top of the marketing funnel is the awareness stage, where potential customers first become aware of your brand or product. This can happen through various channels, such as advertising, social media, or word-of-mouth. The goal of the awareness stage is to generate interest and motivate potential customers to move down the funnel. The next stage is the interest stage, where potential customers start to show interest in your product or service. They may visit your website, read your blog, or follow you on your social media channels. The goal of the interest stage is to engage potential customers and provide them with more information about your offering. Once potential customers are interested in your product, they move into the consideration stage. At this stage, they are comparing different options and considering whether or not to make a purchase. The goal of the consideration stage is to differentiate your product from the competition and convince potential customers that your offering is the best solution for their needs. The fourth stage of the funnel is the decision stage, where potential customers make a decision about whether or not to purchase your product. This is the critical stage of the funnel, as it is where you convert leads into customers. The goal of the decision stage is to make it easy for potential customers to purchase your product and provide them with the information they need to make an informed decision. The final stage of the funnel is the retention stage, where you focus on retaining existing customers and building long-term relationships. The goal of the retention stage is to ensure that customers are satisfied with your product and continue to do business with you. By understanding the customer acquisition funnel, you can develop targeted marketing and sales strategies to move potential customers through each stage of the funnel and increase your chances of converting them into customers. Acquisition channels There are various other customer acquisition methods and channels that businesses can use to reach and acquire new customers. These channels include organic search, paid search, social media, email marketing, and content marketing. Organic search refers to the process of optimising a website so that it appears higher in search engine results pages (SERPs) for relevant keywords. This can be achieved by creating high-quality content, building backlinks, and improving the technical aspects of a website. By optimising for organic search, businesses can increase their visibility in search engines and attract more visitors to their website, leading to increased customer acquisition. Paid search involves using paid advertising to place ads at the top of SERPs for specific keywords. This can be an effective way to reach potential customers who are actively searching for products or services like yours. However, it is important to carefully manage paid search campaigns to ensure that you are getting a positive return on investment (ROI). Social media marketing strategy involves using social media platforms such as Facebook, Twitter, and Instagram to connect with potential customers and build relationships. By creating engaging content, running social media ads, and interacting with followers, businesses can use social media to generate leads and drive traffic to their website. Email marketing involves sending promotional emails to a list of subscribers. This can be an effective way to stay in touch with potential and existing customers, promote new products or services, and drive traffic to your website. However, it is important to follow best practices for email marketing, such as obtaining permission before sending emails and providing valuable content, to avoid alienating subscribers. Content marketing involves creating and distributing valuable, relevant, and consistent content to attract and retain a clearly defined audience. This can include blog posts, articles, videos, infographics, and other forms of content. By creating high-quality content that addresses the needs and interests of your target audience, you can build trust and credibility, generate leads, and ultimately acquire new customers. How to develop a customer acquisition strategies To develop a customer acquisition strategy, the first step is to identify your target audience. This involves understanding their needs, pain points, and demographics. This information can be gathered through market research, surveys, and analytics. Once you have a clear understanding of your target audience, you can develop a strategy to reach and acquire them. Setting clear goals and objectives is essential for any customer acquisition strategy. What do you want to achieve with your customer retention strategy? Do you want to increase brand awareness, generate leads, or drive sales? Once you know your goals, you can develop a plan to achieve them. Developing a customer journey map is a helpful tool for visualising the customer experience from the initial touchpoint to the final purchase. This will help you identify any gaps or friction points in the customer journey and make improvements to optimise the process. Creating compelling content is essential for attracting and engaging potential customers. This content can take various forms, such as blog posts, videos, infographics, and social media posts. Ensure that your content is relevant to your target audience and provides value to them. Customer acquisition metrics Metrics are essential for measuring the success of your customer acquisition efforts. This section will discuss the key customer data metrics you should track, including customer acquisition cost (CAC), customer lifetime value (CLTV), customer churn rate, marketing qualified leads (MQLs), and sales qualified leads (SQLs). Customer acquisition cost (CAC) is the total cost of acquiring a new customer. This includes all costs associated with marketing, sales, and customer onboarding. CAC can be calculated by dividing the total cost of new customer acquisition by the number of new customers acquired. Customer lifetime value (CLTV) is the total amount of revenue that a customer is expected to generate over their lifetime. This can be calculated by multiplying the average customer value by the average customer lifespan. Customer churn rate is the percentage of customers who stop doing business with a company over a given period of time. This can be calculated by dividing the number of customers who churned by the total number of customers at the beginning of the period. Marketing qualified leads (MQLs) are potential customers who have shown interest in a company’s product or service but are not yet ready to make a purchase. MQLs can be generated through various marketing channels, such as website visits, email campaigns, and social media. Sales qualified leads (SQLs) are potential customers who have been identified as being ready to make a purchase. SQLs have typically been through the MQL stage and have expressed a strong interest in a company’s product or service. Tracking these customer acquisition metrics can help you measure the effectiveness of your customer acquisition efforts and make adjustments as needed. By optimising your customer acquisition process, you can reduce your CAC, increase your CLTV, and improve your overall customer acquisition ROI. 3 customer acquisition strategy examples Here are three examples of customer acquisition strategies that businesses can use to grow their customer base: 1. Paid advertising Paid advertising is one of the most direct ways to reach new customers. By using platforms like Google AdWords, Facebook Ads, and LinkedIn Ads, businesses can target potential customers with specific ads based on their interests, demographics, and online behaviour. Paid advertising can be an effective way to generate leads, drive traffic to a website, and increase brand awareness. 2. Referral programs Referral programs are a great way to incentivise existing customers to bring in new customers. By offering rewards or discounts to customers who refer new business, businesses can tap into the power of word-of-mouth marketing. Referral programs can be especially effective for businesses with a loyal customer base. 3. Partnerships and collaborations Partnering with other businesses can be a great way to reach new customers and expand your market reach. By collaborating with complementary businesses, businesses can cross-promote each other’s products or services and access new customer segments. Partnerships can also be a great way to gain credibility and build trust with potential customers. These are just a few examples of customer acquisition strategies that businesses can use to grow their customer base. By understanding the target audience, setting clear goals, and creating compelling content, businesses can successfully attract and acquire new customers. Common customer acquisition challenges and solutions There are several common challenges businesses face in their organic customer acquisition strategies. These include: – Competition: In today’s competitive business environment, there are numerous businesses competing for the attention of the same potential customers. This means businesses need to find ways to stand out from the competition and differentiate their products or services. – Lack of brand awareness: For new businesses or those with limited brand recognition, creating awareness of their products or services can be a significant challenge. – High customer acquisition costs: Acquiring new customers can be expensive, especially if businesses rely heavily on paid advertising or other marketing channels that require significant investment. – Long sales cycles: For some businesses, the sales cycle can be long and complex, which can make it difficult to convert leads into customers quickly. – Customer churn: Once businesses have acquired customers, they need to focus on retaining them and preventing churn. This can be challenging, especially in industries with high levels of competition. To overcome these challenges, businesses can implement various solutions, such as: – Developing a strong value proposition: Clearly articulating the unique value proposition of a business’s products or services can help differentiate it from competitors and attract potential customers. – Investing in brand building: Building brand awareness through effective marketing and communication strategies can help businesses reach a wider audience and establish a strong reputation. – Optimising customer acquisition channels: Analysing and optimising the effectiveness of different customer acquisition channels can help businesses allocate their resources more efficiently and reduce customer acquisition and marketing costs further. – Streamlining the sales process: By simplifying the sales process and removing unnecessary steps, businesses can shorten the sales cycle and improve conversion rates. – Implementing customer retention strategies: Developing and implementing customer retention strategies, such as loyalty programs and excellent customer service, can help businesses measure customer acquisition, reduce churn and increase customer lifetime value. How LIKE.TG can help with customer acquisition LIKE.TG is a powerful customer relationship management (CRM) platform that can help businesses of all sizes acquire new customers. It provides a complete view of your customers across all touchpoints and channels, so you can understand their needs and preferences and tailor your marketing and sales efforts accordingly. With LIKE.TG, you can automate your marketing campaigns, nurture leads with personalised messaging, and manage your sales process from start to finish. You can also build a seamless omnichannel shopping experience for your customers, so they can easily purchase from you no matter how they choose to interact with your business. In addition, LIKE.TG provides robust analytics and reporting tools, so you can track your customer acquisition progress and make informed decisions about your marketing and sales strategies. By using LIKE.TG, you can streamline and improve your customer acquisition and process and grow your business faster. Here are some more customers’ specific examples of how LIKE.TG has helped businesses acquire new customers: – A leading technology company used LIKE.TG to create a personalised customer journey for each of its website visitors. By understanding the interests and needs of each visitor, the company was able to target them with relevant content and offers, which resulted in a 30% increase in conversions. – A major retailer used LIKE.TG to automate its email marketing campaigns. By sending targeted emails to its customers, the retailer was able to increase its open rates by 20% and its click-through rates by 15%. – A small business used LIKE.TG to manage its sales process. By tracking leads and opportunities, the business was able to increase its sales by 25%. These are just a few examples of the many ways that LIKE.TG can help businesses acquire new customers. If you’re looking for a CRM platform that can help you grow your business, LIKE.TG is a great option.

It’s the question on many marketers’ minds: how can I unify my customer data? Enter Customer Data Platform (CDP). What is CDP you ask? Simply put, it is analytics software designed to help marketers manage their customer data. But because CDP is such a versatile piece of marketing technology, it’s not always easy to quickly summarise its many capabilities. Which is why we challenged our product marketers to do exactly that. Here, LIKE.TG’s Pam Samathivathanachai and Robert Colborne sit down to explain why CDP is the game changer we’ve all been waiting for in 60-seconds. Let’s see how they fared. The customer experience, but supercharged Success! In a nutshell, LIKE.TG CDP allows you to bring together first-party customer data across sales, service, and marketing to build the most accurate of unique customer profiles and understanding of the individual customer. For the customer, this means their interactions with your brand are not only personalised to them but appear at the right time and on the right channel. Never again will your customer see an ad for or receive communications on a product in which they have no interest, or that they already purchased months ago. Finally, here’s the kicker: the more data you feed into your Customer Data Platform, the more you get out of it. To take ultimate control of your customer data, get started with the Customer Data Platform e-book. This post originally appeared on the A.U-version of the LIKE.TG blog.

Are you measuring your customer effort score yet? Service professionals are now prioritising this key performance indicator (KPI) with the same importance as tried-and-true metrics like customer satisfaction, revenue, and customer retention, according to the latest findings from the LIKE.TG State of Service report. Since we began surveying service professionals for the State of Service reports in 2016, this is the first year that customer effort has cracked the top five most-measured metrics. Today, the majority (60%) of service organisations track customer effort compared to 44% in 2018. That’s a 37% increase year-over-year. Service professionals have clearly realised how much work a customer puts in to get the information they need matters. So, what exactly is customer effort, how do you find your score, and what are the steps you can take to improve it? What is a customer effort score? A customer effort score is a quantifiable measurement of the amount of work a customer puts in to get information they need or to reach a resolution on an issue. Harvard Business Review introduced the idea of customer effort back in 2010 as something that is directly tied to customer loyalty. Consider some of the most frustrating customer service experiences that you’ve had — they probably involved more steps and callbacks than expected. If a customer is transferred to multiple departments and has to repeat themselves several times, or they search your help centre only to have to reach out by chat or phone anyway, that’s increased effort for the customer. The goal is to do the opposite: Ensure as little effort as possible. How do you measure your customer effort score? Most organisations measure their customer effort score with follow-up surveys after a service interaction that might include questions like, “How easy did we make it to resolve your issue?” Response options are on a multi-point scale (strongly agree, agree, neither, disagree, strongly disagree). To get your score, find the percentage of those who selected the “agree” options. Of course, there are other factors that contribute to customer effort beyond a survey. Average handle time (AHT), the amount of repeat calls, and the number of transfers can add to the hurdles that customers have to jump over to get their issue resolved. One way to gain insight into these areas is to review contact centre analytics to spot gaps and opportunities for improvement. Using a heat map to see where users navigate on your website is another good way to understand effort. Do patterns indicate that they easily find what they need on your help centre or customer portal? Review search terms to signal any gaps as well. 5 ways to improve your customer effort score You can create a frictionless customer service experience — and improve your customer effort score — by giving customers what they need at the very first interaction. Take these steps: 1. Make it easy with self-service options Reduce or eliminate the need for customers to contact a customer service representative with helpful, informative self-service channels, including your help centre, customer portal, and chatbots. Review search trends and have agents track requests to identify new patterns. Create knowledge articles based on recurring customer questions. Update your help centre and chatbot messaging. And be sure to revisit search engine optimisation (SEO) terms to ensure customers find your content first. 2. Have the right channels Customers use an average of nine channels to engage with brands, and 76% prefer different channels depending on context. Survey customers to understand their channel preferences. Keep an ear to the ground: Are customers asking for service on another channel that you haven’t considered yet? Stay up-to-date on emerging trends and technology as well, including new social media platforms and messenger apps. The more relevant channels that you have, the less customer effort is required. 3. Speed up resolutions with workflows Preconfigured workflows guide agents through processes to reach resolutions faster. For example, they can help agents report an error on a customer’s billing statement. Intelligent workflows also work on self-service channels to walk customers through simple processes on their own, such as how to initiate a return. 4. Connect your data Seventy-six percent of customers expect consistent interactions across departments, but 53% say it generally feels like sales, service, and marketing don’t share information. Create a single source of truth for data that connects teams and technology for a holistic view that goes well beyond service interactions. This way, if a high-value B2B customer reaches out with a problem, the agent has access to details on the relationship and may choose to loop in a sales rep. 5. Assign the right agent When customers connect with someone with the best skill set to solve their specific problem, whether that be institutional knowledge or a deep understanding of a process, the likelihood of a resolution increases. Try a workforce engagement solution, which can be integrated across your service channels to automatically analyse case data and assign the right agent. It can also predict demand based on volume across channels, geographies, and expertise. 6. Stay proactive Artificial intelligence (AI) helps in a number of ways to cut down on customer effort. AI analyses data and offers recommended next steps for agents, such as follow-up questions, opportunities to up-sell, and ways to continue the engagement (for example, attaching a special offer to a customer’s profile). AI-powered chatbots deliver personalised responses to common questions. And, you can use AI on your website to recommend other relevant knowledge articles and content based on what a customer has already viewed on your help centre.

On the surface, customer journeys may seem simple – you offer a product and someone buys it. But look more closely and it’s easy to see that the customer journey is becoming increasingly complex. Customers use messaging apps, email, phone calls, websites, and various other channels to communicate with businesses. All these touchpoints create increasingly complex customer journeys, making it more difficult to always ensure a great customer experience. But customer experience is more important than ever. According to 2020 global research from LIKE.TG, 80% of customers now consider their experience with a company to be as important as its products. 69% of Gen X customers prioritise convenience over brand loyalty 91% of customers agree that a positive customer experience makes them more likely to purchase again Customer expectations are undoubtedly undergoing a major transformation. How can brands meet these expectations and ensure every customer journey is smooth? One excellent way to understand and optimise the customer experience is a process called customer journey mapping. Your customer’s journey – in pictures A customer journey map is a visual picture of the customer or user journey. It helps you tell the story of your customers’ experiences with your brand across social media, email, livechat, and any other channels they might use. Mapping the customer journey ensures that you are not missing out on the chance to interact with your customer at any stage.This process also helps business leaders gain insights into common customer pain points. With these insights, businesses can deliver more optimised and personalised customer experiences. Creating a customer journey map Customer journey mapping (also called user journey mapping) is the process of creating a customer journey map. Doing this helps businesses see things from the customer’s perspective and where they can improve. First, all the possible customer touchpoints are mapped out. Touchpoints include websites, social channels, or interactions with the marketing and sales teams. User journeys are then created across these various touchpoints for each buyer persona. For example, a millennial buyer persona may typically become aware of a product on social media, research it on the mobile version of your site, and finally make a purchase on a laptop. The customer experience at each touchpoint should be included in your customer journey map. This can include what action the customer needs to take and how your brand responds. Why customer journey mapping matters Customer journey mapping is important, because it is a strategic approach to better understand customer expectations. It is also crucial for optimising the customer experience. Customer journey mapping is just as important for small and medium-sized businesses (SMBs) as it is for larger companies. Customer expectations are changing for all businesses, regardless of size. Customers demand an omni-channel approach to customer service, marketing, and sales. One of the most important aspects of the customer experience is personalisation. Recent research found that 52% expect their offers to always be personalised. Customer journey mapping allows SMBs to create personalised experiences across all touchpoints – for every individual, across all channels. Mapping the customer journey has a host of benefits such as: Allowing you to optimise the customer onboarding process Checking customer expectations against the experience they actually receive Understanding the differences in buyer personas as they move from prospect to conversion through the buying funnel Creating a logical order to your buyer journey However, the biggest benefit of customer journey mapping is simply understanding your customers more. The better you understand their expectations, the more you can tailor the customer experience to their needs. How does customer journey mapping enable omnichannel marketing and customer service? Today’s consumers want a highly personalised experience and this includes your marketing and customer service efforts. This interconnected approach is called omni-channel marketing and omni-channel customer service. In terms of marketing, customer journey mapping can target one prospect across multiple touchpoints. For example, a customer who browses a product on a website can be retargeted with a social media ad later on. To offer the best possible customer experience, omni-channel marketing is often backed up by omni-channel customer service. This is where the customer can receive customer support across any channel, such as on social media, messenger apps, or live chat. Again, customer journey mapping can allow your customer service team to better understand the customer experience and improve their ability to resolve issues. How can I optimise my customer journey map? Mapping out many different customer journeys across many different buyer personas can be quite time-consuming. Once you have mapped them out, you still need a way to offer a personalised omni-channel customer experience. If you’re serious about customer journey mapping, you need to invest in software that can help. Customer journey mapping tools are typically part of marketing automation software like Pardot. These allow you to easily create customised journeys and automate marketing actions. This takes your marketing automation efforts to the next level. Check out a demo of Marketing Cloud Account Engagement’s powerful marketing automation software built on the world’s #1 CRM. This post originally appeared in the U.K.-version of the LIKE.TG blog. FAQs What is a Customer Journey Map? A Customer Journey Map is a visual representation of the end-to-end experience a customer has with a brand. It outlines touchpoints and interactions, helping businesses enhance customer satisfaction and loyalty. Explore LIKE.TG solutions for optimising your customer journey today. How do you create a customer journey map? Here are the steps: 1) Identify customer touchpoints and interactions across their lifecycle. 2) Gather data, analyse feedback, and collaborate cross-functionally. 3) Leverage LIKE.TG’s tools to streamline the process and design effective journey maps for improved customer experiences.

From labour and skills shortages to increased competition from digital-only institutions, traditional banks today are facing unprecedented challenges. At the same time, customers continue to demand excellent, timely, and highly personalised service. Yet, according to The Future of Financial Services report, only 11% of customers stated their banks anticipated their financial needs. So what can banks do to win and retain customer trust while being more efficient and keeping costs low? How do they wow their customers? Evaluate how and which technology can deliver business value Banks need the right data and infrastructure in place to understand and anticipate consumer needs. In other words, they need to undergo significant digital transformation. Yet, one report found that less than one third of APAC banks’ digital transformation strategies are ‘advanced’. More than half the banks surveyed were at risk of missing their transformation targets altogether. This is despite the fact that the surveyed leaders were aware of the need to embrace digital transformation, or risk losing market share (67%), or even ceasing to exist within a decade (58%). To embrace the opportunities ahead, banks need to first have firm digital transformation objectives. Next, they must identify gaps in their current technological portfolio so they can amend or add tools to help them get to their goal. But such changes are often costly, so any technology investment must deliver value in the long term to make it worthwhile. Move from custom solutions to out-of-the-box technology It may be tempting to build highly customised solutions that cater to every stringent protocol a bank needs to adhere to. However, this can be costly, and may limit future return on investment (ROI). Any changes to a custom build would likely require significant coding, which leads to loss of time and money. It also puts undue pressure on IT teams to troubleshoot problems and provide always-on support. Instead, banking institutions should consider out-of-the box cloud solutions. By nature, they are designed with usability and accessibility in mind. Such services take the “clicks not code” approach, which means they do not require any coding knowledge and can be leveraged by employees across the organisation. However, as The Reset report points out, it is also important to understand that while many tech features can be deployed out the box, organisations shouldn’t simply take a blanket approach. Instead, they should remain selective and focus on using out-of-the-box features in areas with low strategic value, where the outcome is highly commoditised. For instance, when used for customer service, the teams involved can adopt ready-made technology quickly, without having to rely heavily on IT teams. And without a steep learning curve, their productivity improves rapidly, leading to higher efficiencies, cost savings, and happier customers. A Forrester study found that the implementation of LIKE.TG Service Cloud improved call centre average handling time by 25%, and improved agent time to competency by 66%. Break down silos to deliver personalisation Another way to elevate customer engagement is by delivering personalised experiences. With 71% of APAC consumers responding positively to tailored products, bespoke service can be a great business differentiator for banks. However, businesses often use technology that is siloed for particular functions or teams. This means information doesn’t flow through between teams and nobody gets a clear picture of what the customer is after. To break down these siloes, banks can consolidate their technology solutions for a 360-degree view of customer data across various channels. A leading bank in the Philippines uses Sales Cloud, Service Cloud and Marketing Cloud to better understand and wow its customers: When a customer calls, the contact centre agent can see all the customer information in Service Cloud. Not only does this allow them to provide a more personalised service, employees also see a ‘next best offer’ on their interface to cross-sell products to meet customer needs better. The same offer can also be seen by reps in Sales Cloud, along with any known customer concerns. This empowers them to own the customer relationship and offer the right solutions for individual customer problems. And marketing teams can create highly personalised messages in Marketing Cloud. The result: 34% increase in engaged leads for the bank. Take advantage of service automation Like many other organisations, banks can simultaneously scale service and improve the customer experience by providing access to self-service portals. By intercepting simple customer enquiries and diverting them away from high-touch channels like the contact centre, automated or self-service portals free up agents to handle more complex customer requests. There are various channels you can start building today — chatbots, self-service knowledge banks, or automated telephone systems. In just one month in early 2020, the same leading bank in the Philippines handled more than 80,000 customer messages via chat, saving their contact centre agents hundreds of hours. Empower your teams to deliver innovation Automated processes allow companies to free up valuable employee time by cutting out mundane administrative tasks. This allows employees to dedicate more time to strategic thinking. In fact, with “drag and drop” technology solutions, staff members are encouraged to experiment with the tool and can often come up with new use cases for the technology. This can help the bank achieve true digital transformation — delivering greater business value through cost savings and opportunities for long-term growth. To thrive in the digital age, traditional banks must transform ways of working. It’s time to look for new and innovative strategies to scale operations without increasing costs, and seek out technology that can help them do all this while keeping customers satisfied. And to make sure that innovation can continue, it’s important to consider solutions that not only address your immediate challenges, but that also scale with you as you evolve.

It can take up to 35 pieces of data to create one personalised customer experience, according to MuleSoft. Yet, customers crave connected experiences. Customers volunteer information such as what they buy, where they buy it (online or at a store), if they contacted customer service, if they use a coupon, if they read your marketing emails, and on and on. Your customers never stop giving you valuable information about their preferences. Now, customers want you to remember these interactions. For your brand to connect with them, now and in the future, with these preferences in mind. They want you to see them as individuals. They want you to provide them with the personalised experiences they prefer. According to the 4th Edition of the State of the Connected Customer report, 80% of customers say the experience a company provides is as important as its products and services. Customers feel so strongly about this, one in three would abandon a brand after only one negative experience. Customer data increases exponentially The amount of data organisations must connect, analyse, and act on continues to explode. Yet, only one-third of the average 900 applications enterprise organisations have is connected. It is no wonder nine in 10 IT leaders believe these data silos create unaddressed business challenges. So, it’s time to ask: What if you could connect all those data points to power all aspects of your business? This question is creating massive hurdles for many companies across every industry. But this isn’t something IT leaders can ignore if they want to stay competitive and meet evolving customer needs. Instead, a data-driven business should be a priority now — if it wasn’t before 2020. Seventy-two percent of those surveyed in a recent report say experimentation with and investment in digital technologies have played a key role in helping them navigate the crisis. Sixty-seven percent of those same respondents invested more than industry peers in digital-related capital expenditures. You know there is a better way. Whether it is for your salespeople that spend two-thirds of their time not selling, or for your IT team who spends time on projects that aren’t business-critical. Maybe it is for the customer service people who spend time answering the same questions over and over again. Whomever it is for in your organisation, you know productivity and morale improvements are necessary. That data is the key. Employee experience supports customer success Not only should your technology bring you and your customers closer, but it should also support your employees in their work so they can be the glue to keep your customers close. You can use LIKE.TG Customer 360, the MuleSoft Anypoint Platform, and Slack to ensure a superior employee experience. It’s time you discover what other innovative companies have: Better employee experiences lead to better customer service experiences. That all leads to business growth. As businesses work to obtain their footing in a changing world, most (87%) IT and business leaders feel their alignment has improved over the past year. For example, INSEAD CIO Mr. Choo Tatt Saw knows increasing employee productivity means better customer service. “MuleSoft’s Anypoint Platform’s API capabilities enabled us to implement automated scheduling and course bookings — improving the efficiency and productivity of our staff. Now, they can focus on higher value tasks instead of manual work.” Additionally, connecting systems through one integrated platform enabled INSEAD to provide a 360-degree view of their data, which in turn powered innovation and cut down on development time from three months to two days. This type of flexible and secure technology should be at the heart of your digital transformation. As you evaluate solutions, it is crucial to get a trusted technology partner and platform capable of adjusting as your organisation and market needs it to. Flexible technology is the answer The MuleSoft Anypoint Platform provides this flexibility. It uses application programming interfaces (APIs) to connect the data from your LIKE.TG CRM to Tableau to visualise that data. It is possible to use Slack as a frontend interface that can enhance inter and intradepartmental communication. The current business landscape is still evolving as the pandemic economy continues. Businesses that want to continue growing need a flexible platform that can respond quickly to the market. A recent study shows a correlation between those that have successfully navigated the ongoing pandemic and have invested, more than their peers, in experimentation in digital technologies. Whether that is the evolving market of today where coronavirus variants are now surging or the market on the horizon with the return to schools and hybrid work schedules. What’s next? The only way to prepare for the unimaginable is to do the best you can with the data you have now. Have reliable, secure, and flexible technology built for agility. Finally, prepare to look at novel business challenges with a “beginner’s mind” when the situation demands it. Given everything that’s happened since March 2020, are you ready for the next step in your digital transformation? See how MuleSoft (with LIKE.TG, Slack, and Tableau) can provide your customers with the connected experiences they want — faster. Go ahead, take the next step.

In an uncertain economy, banking customers want clear guidance and familiarity from their financial institution — and they’re not afraid to move their money if they’re not satisfied. Improved customer service in banking should be your focus, as people have grown to expect easy digital services and personalised support from their bank. AI can help you meet those expectations – anytime, anywhere, and on customers’ preferred channels. In the last year, we found that 25% of customers switched banks, and 39% of those who switched did so due to poor customer service. Customers want to feel like their needs come first, and banks that can deliver will come out on top. How can banks offer a more personal touch? This is where AI can help. While you already may use predictive AI in customer churn prediction, ticket routing, credit scoring, and fraud detection, generative AI can help create new content that greatly improves customer service experiences. Banking use cases for generative AI You can prompt generative AI to help create emails, service replies in chat, and knowledge articles that make it easier to offer more relevant and personalised service. The technology understands natural language, so customers can use their own words and language to communicate with chatbots that sound human. Because generative AI can read and understand text, video, and sound, it can identify and summarise action items and insights from conversations, transcripts, and recordings to assist contact centre agents. Generative AI can help you improve and grow your customer service experiences, from no-touch interactions like self-service help with chatbots to high-touch situations like working through complex issues in a branch visit. When using this technology, your top priority should be better serving your customers’ needs in order to meet your fiduciary responsibility. Last month, for example, the Securities and Exchange Commission (SEC) in the United States proposed updating rules and regulations requiring wealth management firms to supervise AI technology, such as automated replies produced by generative AI. Future proposed regulations could lie ahead for all of financial services, so banking leaders should proactively think about how to manage the risks associated with using this technology. By combining your trusted customer data with AI, you can transform customer service in banking to improve the customer experience and boost loyalty. AI can help you deliver personalised customer service in banking Our research found 63% of service professionals say generative AI will help them serve their customers faster, saving them over four hours weekly (or nearly one month per year). Drawing from your trusted customer data, generative AI can automatically generate relevant content, such as action summaries and service replies. It helps agents find answers to known questions and issues, surfacing content from your knowledge base articles so they don’t start from scratch with each new caller. This makes your agents more efficient, allowing them to focus on more complicated cases. And it’s not just helpful for service inquiries. AI can look at customer data, preferences, transaction history, and customer service logs to generate new offers, recommend next steps, or provide proactive assistance for customers’ specific questions or issues, regardless of how they communicate. For example, if a customer is getting married and asks about opening a shared account, generative AI can create a suggested reply for an agent that includes details specific to the customer’s finances, while also triggering a followup email to the client with relevant offers for newlyweds. These highly personalised recommendations are possible because AI uses customer data to create content that’s better matched to real-time customer needs, improving customer engagement and loyalty. Make self-service tools easy to use and effective We found 81% of people try to solve a problem themselves with self-service tools like chatbots or how-to articles before seeking support. Self-service options save both customers and banks time and effort, making for quick, in-and-out interactions, but bots can be very impersonal if not set up right. Self-service tools must be easy to use and integrated well with your platform. When customers use these tools for simple banking transactions, you can use that data to better serve them in the future. While you should focus on making these tools intuitive and simple, you also need to make sure these services feel empathetic and personalised, which helps to build trust with your customers. Unfortunately, 59% of consumers say it often feels like they are dealing with separate departments, not one company. And 52% of customers describe most service interactions as fragmented. Customers want their banks to have a holistic view of their relationship, so they can avoid repeating their story or starting at square one when moving across service teams, channels, and departments. AI can significantly improve and scale customer service in banking with better self-service tools that handle more of your customers’ questions. AI-powered self-service enables banks to resolve high volumes of inquiries more efficiently, enhancing customer satisfaction and reducing operational costs. And using AI alongside the trusted data in your CRM system allows you to quickly analyse customer behaviour patterns to anticipate what they’ll need next. For example, if a customer frequently transfers funds between accounts, the system can provide shortcuts to reach the next step. Or at the start of a session, it could automatically offer a short list of the customer’s most frequent tasks to save time. This helps customers resolve issues and accomplish tasks quickly, providing full access to personalised services, showing them savings opportunities, and proactively recommending services that support their financial wellbeing. Use your data to offer proactive recommendations Banking customers want to feel like you know, remember, and value them. But only 37% of customers say their bank anticipates their needs. That’s concerning, because half of those we surveyed said they would switch banks if service felt impersonal. AI algorithms can help you anticipate customer needs and automate outreach — even before customers turn to you. For example, predictive AI can identify patterns, then alert bank staff to potential customer needs. Maybe it recognises a customer building up their savings account and frequently checking their credit score. This could indicate they are planning to make a purchase that requires lending services. A personal banker then could use generative AI to reach out to the customer with a personalised loan offer tailored to their finances. Similarly, predictive AI can alert your team to event triggers, such as a customer’s fixed deposit reaching maturity. Then generative AI can help you meet that customer at the right moment, generating a package of reinvestment options for their unique circumstances. That’s what people want from customer service in banking. Eventually, AI will make personalised financial planning more accessible for all banking customers, no matter their wealth. By analysing the vast amounts of data you already have, the algorithm can offer personalised recommendations tailored to every customer’s financial goals. This will revolutionise the way financial advice is delivered, making it more accessible and relevant to the broadest range of banking customers. Use AI to take customer service in banking to the next level Despite improving digital services and capabilities, many banks find it difficult to win the loyalty of their customers. Customer service could make the difference, helping the customer feel known and valued by offering empathetic, personalised care.Still, you will need to think about how you’ll supervise AI usage. Choosing the right technology platform is key, and you should look for solutions with built-in compliance and transparency features for protection and control. These might include audit trails for record-keeping, automatic hand-off to humans for decision-making, and transparency in data usage for recommendations. For banks to build trust with their customers, they must get to know them throughout the journey. To do this at scale, AI can help you connect with customers, addressing their needs and showing you care about their needs today and in the future.

Virtual spaces are new territory for cyber attacks, data leaks and company breaches. In fact, according to the IBM Cost of Data Breach Report 2021, phishing attempts have risen 600% and cloud-based attacks rose 630%. What would you do if your business data fell into the wrong hands? To stay secure, there are a number of layers of data security you need to consider. From physical hardware, to digital networks, and every manner of encryption in the cloud. Explore our cybersecurity infographic below to see how to secure your data, and get more tips on data security and compliance for every IT Leader in this e-book. Can you afford not to protect your most important business asset? For details on this infographic, please click here.

The COVID-19 pandemic has catalysed major organisational changes, especially in the ways we work and communicate. Just think: When was the last time you bumped into a colleague by the watercooler? What about the last time you gave your teammate a literal pat on the back for coming up with a brilliant business idea? Those days seem like a lifetime ago. The truth is that remote and hybrid work is here to stay. With less opportunities for face-to-face interactions, business leaders are concerned about how the new ways of working will affect business conversations and culture. How can leaders communicate effectively with teams and maintain business partnerships? Data drives better conversations Despite the challenges, many leaders have taken the bull by its horns and transformed business conversations for the better. Unexpectedly, many leaders in the Asia Pacific and Japan (APJ) region reported that changes associated with the pandemic had actually improved business conversations. These were the findings from a recent YouGov survey commissioned by Tableau with more than 650 business leaders across Singapore, Japan, Australia. We sought to examine how business leaders had adapted decision making and employee engagement strategies since the COVID-19 pandemic began. Each country had their own unique take. Singaporean leaders noticed a flattening of workplace hierarchies. In Japan, younger leaders adapted better to change than their older counterparts. Australian leaders adopted a more egalitarian approach to leadership. The positive changes experienced by these leaders all bore a common denominator: the use of data. In fact, APJ leaders were almost two times more likely than their counterparts elsewhere in the world to use data to improve workplace decision making and communication. By the same token, those who increased their data use were more than twice as likely to report these positive changes, compared to those that hadn’t. What the transformative power of data looks like Take Southeast Asia’s latest unicorn, CARRO, for instance. CARRO is an online marketplace where users can buy, sell, or lease pre-owned and new cars. Not only did CARRO keep their car marketplace business afloat, but they even managed to bag new funds in spite of the pandemic. Credit goes to CARRO’s leaders, who value the ability to curate, explore, and share data with teams across the region. CARRO uses data across all aspects of its business: to level the playing field for employees, remove bias during brainstorms, and improve accountability and transparency during performance reviews. Meanwhile, at retailer Levi Strauss & Co., data enabled the personalisation of customer journeys and helped the business anticipate market and inventory changes. Therein lies the transformative power of data. When it is at the centre of all conversations, everyone in the team can come together as equals, and make decisions based on fact and not gut feel. The importance of leaders in building data cultures Unlike CARRO or Levi Strauss & Co., not every leader is as tuned in to working with data. Many leaders are still ‘works-in-progress’ as they navigate their way in a data-driven world. Although many regional leaders recognise that data insights are important and critical in decision-making, they aren’t deliberate and purposeful in weaving data into the fabric of their organisational cultures. There is a strong correlation between leaders who personally use data on a weekly basis at minimum and overall business adoption. Yet, only 16% of these leaders use analytics daily. Worryingly, just 19% of these businesses empower everyone with data. The frequency and scale of data use is not there yet. This is where data-driven leadership comes in. If employees don’t see their executives making decisions with data, how can we expect the wider business to do the same? At the end of the day, it’s not data that makes the decisions, it’s people who make decisions based on data. Leaders must push the envelope to drive data adoption across their organisations. They must ask themselves: Am I setting a good example for my team? Have I established a healthy culture where people are comfortable working with data? What can I do to empower my team with the skills they need to succeed? At Phoon Huat, one of Singapore’s leading food suppliers, data is front and centre across conversations. The leadership team has been deliberate about instilling a digital-first mindset in all their employees. Everyone is equipped with the data skills they need to dig deep into a situation and question findings. This proved pivotal during a time of inflection for the business, when Phoon Huat decided to expand their brick-and-mortar presence online during the pandemic. Three key steps for creating data cultures When looking to create a data culture, business leaders need to consider three important areas: Make a commitment to lead by example Shift mindsets about how people think and behave around data Develop skills by hiring and training employees to use data instinctively What will remain true is that data and quality conversation will continue to be the bedrock upon which successful leadership is built. Learn more about how business conversations are changing and how business leaders can become more data-driven. Explore the results of the report here.

This guide encompasses the critical techniques of data science, which employs programming and statistical methods to glean insights from information, a process essential for better decision-making in business. Key Takeaways Data science involves using a variety of techniques such as classification, regression, and clustering to analyse and derive insights from raw data, playing a critical component in business decision-making and strategy. While related, data science and business analytics have distinct focuses: data science utilises interdisciplinary methods and machine learning for predictive modelling, whereas business analytics examines historical data to optimise business operations. Ethical considerations in data science, such as privacy, bias, and the societal impact of data use, are crucial, highlighting the importance of transparency and fairness in data collection and analysis. What is Data Science? The field of data science emerges at the intersection of programming, mathematics, and statistics. This trio forms the critical framework underlying contemporary analytical methods. Data science’s strength resides in its capacity to meticulously parse through an immense expanse of quantitative data to unearth patterns and connections that are pivotal for making informed business choices. Given the torrential outpouring of information stemming from every digital interaction—each click, swipe, or engagement—it is a discipline with an unquenchable thirst for data. The significance of data understanding has become even more pronounced in our modern era drenched with data. Every phase of the journey — from gathering raw figures to distilling insights and distributing them — relies on a sophisticated synergy between technologies and techniques aimed at deciphering big data’s complexity. The necessity for insight into extensive datasets propels this once specialised skill into a fundamental pillar as organisations across sectors generate unprecedented amounts of publicising profound consequences. Both industries and societies alike must adapt rapidly within transforming environments where crucially maintaining competitive advantage lies. Understanding Data Science At the heart of data science lies the dual-natured discipline focused on deriving valuable information from unprocessed data. It harnesses a diverse array of techniques within data science, ranging from simple to highly sophisticated methods, to transform large and often disorganised datasets into clear and useful insights. What sets data here apart from related fields is its comprehensive set of tools that span simplistic approaches like crafting data visualisations to implementing complex machine learning algorithms—all to parse through data to discover invaluable points. The Evolution of Data Science The lineage of data science can be traced back to the nascent days of computer science and statistics when these two fields began their dance in the 1960s and 70s. It was a time when the term ‘data science’ was first whispered as an alternative to statistics, hinting at a broader scope that would come to include techniques and technologies beyond traditional statistical methods. As databases and data warehousing became prevalent, the ability to store and work with structured data grew, laying the groundwork for the data science we know today. This evolution witnessed the formal recognition of the profession, with Hal Varian defining what it means to be a data scientist. The field has grown from simple statistical analysis to encompass predictive models and machine learning, marking a transformation that has redefined the possibilities of data-driven decision-making. As society moves forward, the history of data science continues to be written, with each chapter unveiling new technologies and methodologies that push the boundaries of what can be achieved with data. Key Data Science Techniques Data science encompasses various statistical, computational, and machine-learning methodologies aimed at understanding and forecasting data. The toolkit for data science projects consists of various specialised techniques, such as classification, regression, and clustering, to address unique challenges presented by different data sets. By implementing these methods in real-world scenarios, practitioners can derive significant knowledge. Complex quantitative algorithms underpin these methods in data science projects, enabling data scientists to unravel intricacies within massive datasets. Thus, obscure patterns are made evident, and intricate information is translated into comprehendible forms through these sophisticated behind-the-scenes mechanisms in machine learning-driven analysis. Classification Classification is the cornerstone of numerous applications in machine learning, such as identifying spam or providing medical diagnoses. It involves employing decision-making algorithms to organise data within specific predefined groups and is a vital part of science and machine learning. A variety of methods are deployed for classification purposes. Decision trees utilise branching logic to split the data. Support vector machines establish divisions between categories by creating boundaries with maximum margins. Neural networks apply deep learning techniques to process complex and extensive datasets. The brilliance of classification stems from its capacity to be educated using existing datasets and then extend this accumulated knowledge towards analysing new, unfamiliar data. Whether it’s engaging Naive Bayes classifiers that leverage probability theory or employing logistic regression for fitting information along a prognostic curve, classification involves making educated predictions about which group an incoming datum should fall under. Regression Regression methods serve as data scientists’ predictive oracle, forecasting numerical results by analysing variable interconnections. This kind of data analysis involves delving into historical trends to predict future events. The simplest form is linear regression, which aims to discover the optimal straight line that fits the dataset. In contrast, lasso regression improves prediction accuracy by focusing on a select group of influential elements. When working with datasets rich in variables, multivariate regression broadens these insights across several dimensions, helping data scientists decipher intricate patterns of interconnectedness among factors. For business analysts, regression acts as their navigational aid through vast oceans of information, steering them towards predictions that shape strategies and guide key decisions. Clustering Clustering involves identifying inherent groupings within data by analysing patterns and outliers, thus gathering similar data points. Methods such as k-means clustering utilise central points around which data is grouped, whereas hierarchical clustering forms a dendrogram that links the data based on resemblance. Sophisticated techniques like Gaussian mixture models and mean-shift clustering provide subtle approaches for delineating between concentrated and dispersed areas in a dataset. Excelling when there are no preset categories, this technique equips data scientists with the capability to decipher unstructured data—unearthing revelations that have the potential to spur significant breakthroughs. Data Science Tools The arsenal of tools available to data scientists is as diverse as the challenges they confront, encompassing everything from high-capacity big data processing systems to sophisticated data visualisation platforms and advanced machine learning technologies. These instruments serve as the bedrock for the entire data science process, allowing experts in the field to sift through, make sense of, and illustrate massive datasets in manners previously deemed unattainable. Technologies such as Apache Spark, Hadoop, and NoSQL databases equip these professionals with the ability to rapidly manage information on a large scale—matching strides with our continuously expanding digital footprint. On the one hand, tools like Tableau, D3.js, and Grafana convert undigested numbers into impactful narratives told through visuals that clarify abstract concepts or simplify intricate details. On another front, machine learning frameworks include TensorFlow and PyTorch. These lay down essential frameworks for devising complex algorithms capable of evolving by discerning patterns within data over time. An appropriate tool does not simply facilitate a task—it allows those immersed in different spheres of data to achieve groundbreaking advancements within their domain using an array of specially tailored mechanisms, undoubtedly transforms it into something more meaningful – enabling specialists to transcend previously often established boundaries enabled by utilising distinct data science methodologies. Data Science in Business Data science for business serves as a critical competitive differentiation within the commercial landscape. Companies can unlock the immense value of their data by using it to unearth transformative patterns that were previously unknown, fostering product innovation and enhancing operational efficiency, which ultimately steers them toward expansion and triumph. Utilising tools such as predictive analytics, machine learning algorithms, and deep customer insights fall under data-driving techniques that propel businesses into an era where decisions are informed by robust data analysis. This approach is reshaping traditional business methodologies with improved precision in efficiency and pioneering innovations. Discovering Transformative Patterns Data science operates like an expert detective, sifting through data to reveal insights and patterns that can revolutionise a company. These discoveries can reinvent how products are strategised, optimise operational efficiencies, and unlock new avenues in the marketplace. Employing data mining methodologies within vast datasets allows businesses to identify both emerging trends and irregularities—thus equipping them with the foresight to evade potential pitfalls while seizing advantageous prospects. The strength of data science stems from its capability to: Illuminate successful elements of a business as well as areas needing improvement. Direct companies toward refining their methods and embracing more effective strategies. Integrate techniques from various fields into deciphering the narrative told by data, rooted in the basic principle behind it, which is synonymous with the core principle behind data science itself. Propel an organisation towards growth beyond expectations. Engaging in this analytical journey holds profound implications for enhancing a business’s prosperity. Innovating Products and Solutions Data science is critical to the lifeblood of business innovation, acting as a catalyst that uncovers voids and chances for groundbreaking products and solutions. Through rigorous examination of consumer insights and industry tendencies, data scientists can steer product evolution to align more closely with user desires and tastes while simultaneously pinpointing enhancements in current workflows. The perpetual loop of inventive progression guarantees companies stay agile and attuned to their customer base’s demands. Data science does not merely accelerate product creation. It cultivates a setting where imagination is underpinned by solid factual analysis, resulting in novel yet impactful offerings. Real-Time Optimisation In the current rapid market environment, business agility is crucial, and data science serves as the driving force behind immediate optimisation. Data Science allows companies to foresee shifts and alter their tactics on the fly, keeping them flexible and ahead of the curve. The use of real-time data encompasses a range of applications, from enhancing marketing campaigns to streamlining inventory management, which ensures that a business maintains its competitive edge. As an essential feature of data science, predictive analytics empowers businesses by allowing them to: Predict what customers will want Tailor operations to align with customer needs Gain critical insights continuously flowing in from data Improve process efficiency Enhance operational efficacy instantaneously The synergy between big data and IoT has opened avenues for organisations to tap into these competencies, propelling their growth trajectory toward greater prosperity. Differences Between Data Science and Related Fields It is essential to understand the distinctive features of data science and how it stands apart from allied domains such as data analytics, business analytics, and machine learning. Although these fields are interrelated, and each plays a role in using data for informed decision-making, they differ in their emphases, methodologies, and specific contributions. Data Science vs. Data Analytics The difference between data analysts and data scientists can be characterised by the breadth and depth of their work. Data science dives into a more comprehensive array of tasks, including predictive modelling and developing sophisticated algorithms, while data analytics concentrates on digesting and portraying information to discern patterns. Typically, those in data analytics harness tools like SQL and Tableau for cleansing the data and presentation purposes, while those in the realm of science employ more complex technologies such as Python or R to execute machine learning processes and anticipatory analyses. Understanding this distinction is vital for companies when recruiting personnel suited to their operational requirements. Analysts examine current datasets while scientists look forward. Forecasting trends involves seeing what’s around the corner based upon scientific approaches within machine learning domains—all intended to help plan effective strategies informed by present insights from analysts alongside future projections posited by scientists adept at handling vast quantities of intricate datasheets. Data Science vs. Business Analytics Data science and business, along with business analytics, both pursue the objective of leveraging data’s potential. There’s a notable difference in their approaches: data science relies on exploiting unstructured data through interdisciplinary strategies to gain knowledge while analytics in science and business emphasises reviewing historical information to enhance decision-making processes within businesses. Business analysts use statistical techniques and tools such as ERP systems and business intelligence software to derive valuable insights from past events. This contrast between these domains underscores how vital it is that certain skills are matched appropriately with respective corporate goals. Data scientists bring an extensive set of technical capabilities alongside machine learning proficiency, which makes them ideal for developing new data-driven products or creating sophisticated predictive models. On the other side of this spectrum are business analysts who channel their analytical acumen into interpreting complex datasets. They deliver concrete advice aimed at improving operational practices, and social aid companies map out a course for future endeavours requiring strategic foresight. Data Science vs. Machine Learning Within the expansive domain of data science, machine learning is a distinct subset centred around algorithms designed to learn from and make predictions based on data. The emphasis on adaptive learning and forecasting distinguishes machine-learning initiatives from the broader scope of data science, which includes various tasks such as data analysis and data processing. There is an unmistakable interplay between data science and machine learning. With data science, laying down the foundational infrastructure and providing the necessary datasets upon which models for machine learning are devised and honed. Together, they form a formidable force driving industry evolution by facilitating automated complex decision-making systems and engendering deep insights at scales previously unseen. Ethical Considerations in Data Science Navigating the digital landscape comes with its own moral compasses, and in the field of data science, ethical considerations are at the forefront. Privacy, bias, and social impact are paramount, as data misuse can lead to violations of individual rights and the perpetuation of societal inequalities. Data ethics, therefore, becomes a guiding principle, ensuring that the collection and use of data are governed by standards that respect privacy, consent, and fairness. Transparency in data collection and use policies is essential in building trust between data providers and users. As machine learning models become more prevalent, addressing and mitigating biases in training data is critical to ensure fair and unbiased outcomes. Ethical data science is about more than just compliance with regulations; it’s about fostering an environment where the benefits of data are balanced with the responsibility to use it wisely and humanely. Career Paths in Data Science In the present era, where data reigns supreme, there is a surging demand for professionals in the field of data science across various industries, opening up a realm brimming with career possibilities. The new trailblazers of the digital era are those adept at dissecting complex data to draw out significant implications. Many entities ranging from behemoth tech companies to sprouting startups are on the lookout for proficient data scientists, machine learning experts, and data engineers—roles that come with enticing remuneration and influential positions in shaping both technological advancements and corporate landscapes. Possessing a distinct combination of competencies, including command over programming languages, statistical acumen, savvy in machine learning techniques as well as prowess in decoding intricate datasets into practicable tactics, defines what it means to be a successful data scientist today. Conversely, roles like data analysts primarily entail sifting through vast amounts of information to uncover trends that can steer tactical business resolutions. This necessitates mastery of analytical tools such as SASR and Python. In the diverse spectrum of data science careers, data engineers take charge of building and overseeing infrastructure facilitating smooth data transfers. At the same time, machine learning specialists work to enhance predictive algorithms—each career offers its own unique complexities and fulfilling opportunities. The Future of Data Science Peering into the future, we can see a vibrant and continuously evolving landscape for data science. The rise of cloud computing is levelling the playing field by granting access to high-powered computational tools, thus empowering data scientists to handle vast datasets with unparalleled ease. In this age where the significance of data storage cannot be overstated, blockchain technology stands as a bastion for security and transparency in managing our digital information—assuring both integrity and verifiability when dealing with transactions involving data. As innovations forge ahead, such as augmented analytics paving the way towards automated processing and data explorations, organisations are given more freedom to concentrate on gleaning interpretations from their insights rather than entangling themselves within complex facets of handling raw material—the intricate aspects tied up with processing it. Burgeoning marketplaces devoted exclusively to data are redefining their value proposition. Transforming it not just into an operational necessity but also into a negotiable currency ripe for a trade-off or potential financial gain. This revolutionary step broadens horizons for both entities conducting businesses and enfranchising individuals. For companies seeking relevance and professionals aspiring toward continued significance within this emerging terrain driven by datum dynamics, it’s crucial they keep pace and adapt progressively. They must harness every technological tide shift efficiently while skillfully leveraging what already lies at their fingertips: existing troves of valuable data ready at hand. Summary Throughout this journey, we have unveiled the layers of data science, a domain where mathematics, statistics, and programming converge to create a symphony of insights. We’ve explored the evolution of this field, from its early intersection with computer science to its current state as an indispensable tool for modern business. The key classification, regression, and clustering techniques have been dissected, revealing their power to predict, analyse, and interpret the wealth of data surrounding us. As we’ve seen, data science is not only about the tools and techniques but also about the ethical implications and the impact on society. It’s a field with a dynamic range of career opportunities, each offering the chance to significantly contribute to the world of data. With the future beckoning with advancements like machine learning and cloud computing, the potential for data science to continue reshaping industries is boundless. May this guide inspire you to delve deeper into data science, whether as a professional, a student, or an enthusiast eager to understand the forces shaping our data-driven world. Frequently Asked Questions What is the difference between data science and data analytics? Machine learning and predictive modelling are integral components of data science, while identifying trends and informed decision-making is at the core of data analytics through its emphasis on processing and visualising data. Both disciplines are essential in deriving significant insights from vast amounts of information. Can data science be used to innovate products and solutions? Certainly, data science can spur innovation in products and solutions by uncovering process inefficiencies and fostering innovations rooted in data. What are some of the ethical considerations in data science? Ethical considerations in data science are fundamental for maintaining responsible practices and entail safeguarding privacy, avoiding bias, and contemplating the broader societal implications to direct appropriate data gathering and utilisation. Data ethics play a pivotal role in ensuring these responsible behaviours. What skills are required to become a data scientist? In pursuit of a career as a data scientist, one must acquire proficiency in programming languages such as SAS, R, and Python, possess strong statistical knowledge, be able to visualise data effectively, and be well-versed in frameworks for machine learning and data processing. What does the future of data science look like? The future of data science looks promising, with advancements in technologies such as cloud computing, blockchain, augmented analytics, and data marketplaces revolutionising data processing and analysis across industries.

Perimeter-based security has often served as the first line of defence against cyberattacks. This ‘castle and moat’ approach treats the organisation’s network as a trusted space and places firewalls and other defences at the edges. The challenge with this approach is that the network perimeter is eroding. In our new hybrid workplace, employees access and share data from anywhere, using multiple devices. That’s why many businesses are moving to a zero trust approach. Zero trust includes the use of technologies like multi-factor authentication to secure individual data assets, as opposed to the perimeter they sit behind. Therefore, even if someone accesses one part of a network, they will not be able to move freely inside it. Many businesses are also realising the need to strengthen all their defences and make security the responsibility of every employee. The question is how can businesses elevate employees’ understanding of data security and equip them to play a more proactive role? Here are top tips and practical examples on how to embed trust and security into company culture. Educate and empower employees to keep data secure The rise of remote working has made businesses more vulnerable to phishing, malware attacks, rogue network access, and other cybersecurity threats. According to a 2021 report from IBM, phishing attempts alone rose 600%. Advancements in security technology can help businesses protect against these threats and secure data at every touchpoint. However, many successful data breaches are the result of human error. This issue was raised during a recent fireside chat with our customers on navigating the security frontier in 2022. “Everyone in IT is aware there are seven layers of cybersecurity. One of these is the human layer and it is always the weakest point,” said Hasniza Binti Mohamed, Director, Digital & Incubation at UEM Sunrise Berhad, one of Malaysia’s leading property developers. With this in mind, UEM Sunrise Berhad launched a new cybersecurity training program last year. The program included a series of online modules as well as a phishing simulation to test key learnings. A number of employees fell victim to the simulated attack, which reinforced the need for continual training. “Security depends not only on process and technology, but also on people. In the current environment, we need to strengthen all three,” said Hasniza. To ensure the success of awareness and training activities, it’s important to tailor content to business context and incentivise participation. In the case of UEM Sunrise Berhad, a leaderboard and prizes gamified learning and kept employees engaged. Engage employees to stay productive and secure LIKE.TG research into the role of technology in employee engagement confirmed that the quality of an organisation’s technology directly affects the quality of employees’ work. What’s more, 93% of office workers in Singapore say their experiences as consumers are increasing their expectations of workplace technology. Employees want better workplace apps and the pressure is on IT leaders to deliver technology that improves employee engagement. The challenge is that 76% of IT leaders say their teams are not aligned with the rest of the business. This misalignment can be heightened in the security space. For example, while every employee should have a vested interest in security, not everyone understands the language of cybersecurity. They might also not understand the impact threats can have on revenue, customer experience, and trust. To engage with the business, IT and security leaders should start by translating security concerns into the language of risk. They then need to balance the risks with the business’ priorities. Equip employees to build on a trusted platform The relationship between security and business teams can be contentious. For example, the business may blame data security and privacy controls for slowing down innovation. However, data has become a critical asset and securing that asset should be at the foundation of any business goal. The good news is that technology has evolved substantially. Today’s security solutions can actually help rather than hinder innovation. For starters, enabling teams to build on a trusted platform reduces risk, complexity, and the cost of compliance. Teams can also embed security and privacy controls into the application development process using solutions like LIKE.TG Data Mask. This can help to accelerate innovation while protecting regulated data. The bottom line is that businesses must prioritise security. Not only to protect their data, but also to earn the trust of their customers. A secure and trusted platform can help IT, security, and business teams to align on this goal and enable continuous innovation. Download the IT Leader’s Guide to Data Security and Governance for more tips on how to simultaneously empower your teams and protect your data.

Data-driven marketing can help businesses of all sizes drive engagement, maximise ROI and get the most out of their resources. Using data to inform your marketing decisions also helps you avoid wasting your marketing budget on creating impressions that won’t convert. Ultimately, data-driven marketing means targeting the right audience at the right time with the right message. But while data can be every business’s most valuable resource, today’s customer is becoming more and more unpredictable. According to the latest State of the Connected Customer report: The average customer uses nine different channels when they communicate with businesses 68% of customers have bought a product in a new way over the last two years 71% of customers switched brands at least once in the past year In other words, it’s becoming increasingly challenging for businesses of all sizes to truly know their customers. The ironic part? Seventy-three percent of customers expect companies to understand their unique needs and expectations. To meet these evolving expectations, you need precise control of your data. However, turning data into actionable information can be a difficult process. This is your guide on how to use your data to create a better roadmap towards your customer — and deliver the experiences they deserve. Let’s look at the concept of real-time marketing and show some ways leading brands use it to win customers. What is data-driven marketing? Data-driven marketing is a way to use customer information to craft personal messaging and deliver better customer experiences. By having the right insights, you can anticipate the needs of your customer and deliver the right messaging and offers at the right time. For example, imagine that you run a dog grooming business. Your data shows that the customers in one area don’t have a lot of disposable income and are likely to be sensitive to price. Your marketing message for this area could be tailored to focus on cost as your differentiator. Meanwhile, the residents in another part of town could have higher household incomes, and be more concerned with bespoke service for their furry friends. Your marketing message for this area could be tailored to focus on your award-winning service or eco-friendly products. If you were to ignore the data you have on the relative income for each area, you’d probably struggle with engagement. But by using data to address your audience’s priorities, you’ll see higher click-through rates and more conversions. That’s the power of targeted data — and why so many businesses are now writing data-driven success stories. The benefits of data-driven marketing It’s important to let go of hunches and gut feelings when it comes to marketing today. By letting your data lead you to your customer, you’ll discover a range of benefits. Some of these include: Targeting well-defined audience segments and offering tailored communications means a higher conversion rate. In fact, 72% of customers only engage with brands that offer personalised communications. Improved customer experience and trust. More than 60% of consumers are comfortable with companies using relevant personal information in a transparent and beneficial manner. Increased ROI. A lower marketing spend and more personal engagement means that you can expect a better return on your investment, as well as increased lifetime customer value. Improved campaign performance. Real-time campaign data allows you to keep up with customers’ evolving expectations. 5 tips for creating a data-driven marketing strategy for your growing business 1. Identify clear objectives for your data Data is most effective when it’s powering a strategy. For example, you may want to use your data to drive ad impressions, increase your website conversion rate or lower your cost-per-click. Once you have clear goals in place, you’ll have a better understanding of how you can use your data to reach those goals. But don’t just identify use cases — look at how you can create a more data-driven culture. According to Tableau research, 74% of employees say they’re more likely to stay with a company that provides them with the data skills they need. 2. Remove information silos and centralise your data Without a single view of your customer, it can be hard to have the full picture of their needs. Centralising your data management will eliminate rogue data that could present an incorrect picture of your audience. Dismantling departmental silos and uniting disparate data storehouses can also improve trust in your organisation’s data. This lets your marketing team create more effective strategies based on reliable information, rather than assumptions. 3. Make sure that your channels match your audience Your data will tell you where your audience likes to spend their time. Do they spend a lot of time on Instagram? What kind of ads do they respond to? Do they use self-service? Do they look at reviews? Your data can help you look beyond marketing KPIs and get a more holistic view of your customer’s lifestyle. You can then adjust your strategy and offer an omnichannel experience to engage them more effectively. 4. Place ads more efficiently and reduce your marketing spend Automation for programmatic marketing and ad buying can help maximise your marketing spend. By using granular data to automatically buy and sell digital ad space, you can create hyper-targeted ad campaigns that have high impact and provide maximum ROI. 5. Leverage artificial intelligence (AI) to personalise individual channel experiences. AI is not just the future — it’s already here. In fact, the State of Marketing report shows that more than 62% of marketing organisations are already using AI. Additionally, 70% of high-performing marketing teams have a clearly defined AI strategy. If you’re not already using AI, you may be asking your marketing team to do some unnecessary heavy lifting. You may also be using valuable marketing resources creating impressions that are unlikely to convert. Build a toolkit for delivering better insights and turbocharging your marketing strategy Once you have a good idea about how you want to use your data, you can make sure that you have the right tools for the job. Tableau makes data visual, with customisable dashboards that allow you to view and share deep analytical insights. In order to act on those insights, you can use Marketing Cloud to create personalised journeys and track campaigns. And if you want to skill up the workforce to learn new digital skills, then check out Trailhead, an on-demand learning platform where you can create personalised learning journeys. To see more about how you can manage your data to connect with the customer, download the CRM Handbook. And to take Marketing Cloud for a spin, check out the demo. Join us at LIKE.TG World Tour Essentials Asia and learn how LIKE.TG Customer 360 can help to unlock the value of your customer data. Register now This post originally appeared on the U.K. version of the LIKE.TG blog.

Wondering how to steer your business towards success? The marketing mix might just be your answer. It’s a proven blend of four essential elements—Product, Price, Place, and Promotion—that, when combined effectively, can elevate your marketing strategy and deliver results. This concept, a basis of marketing strategy, equips businesses to align their offerings with customer demands and stand out in a competitive landscape. Throughout this article, we will explore each ‘P’ in detail, showing you how to harness the marketing mix for business success. Key Takeaways The marketing mix, composed of the four Ps (Product, Price, Place, Promotion), provides a framework for businesses to create a successful marketing strategy that satisfies customers’ needs, effectively communicates value, and stands out in a competitive market. A comprehensive marketing strategy requires understanding and fulfilling customer needs, differentiating the product, and optimising pricing strategies to reflect the perceived value and maintain competitiveness. Expanding the traditional marketing mix to include People, Processes, and Physical Evidence enables companies to create a more holistic and customer-centric strategy, ensuring efficiency in service delivery and a memorable brand experience through physical aspects of interaction. Demystifying the Marketing Mix: A Comprehensive Guide The ‘marketing mix’ is essentially the bedrock upon which modern marketing strategies are constructed. It’s a term that was coined by E. Jerome McCarthy in 1960, a professor who reshaped traditional approaches to marketing with his innovative concept. Today, understanding the marketing mix is crucial for developing an effective marketing strategy, as it enables companies to provide customers exactly what they want—offering their products or services at the right place and price point, and effectively promoting them. McCarthy laid out this foundational formula for success through what became known as the four Ps of Marketing: Product Price Place Promotion Each component plays its own distinctive role within an all-encompassing whole—much like individual instruments contribute to an orchestral performance—to create harmonious results capable of commanding market success when performed skillfully. The Essence of the Marketing Mix The marketing mix provides a strategic framework that assists companies in navigating market complexities. It encompasses four key elements, often referred to as the “four Ps,” which act as navigational beacons for new entrepreneurs and established executives. Product: the offering designed to fulfil customer needs Price: the cost at which value is exchanged Place: optimal locations where products are accessible to customers Promotion: communicative efforts that connect services with consumers. This method of management transcends a basic enumeration. It represents an evolving synergy of strategic decisions. By integrating these essential components into their strategies, businesses can successfully tailor their marketing efforts to engage effectively with their target audience and distinguish themselves in a competitive marketplace. Key Elements of a Robust Marketing Strategy Crafting an effective marketing strategy demands mastery in merging the four essential pillars of marketing to amplify their cumulative effect. Each element must function synergistically with its counterparts, creating a fine-tuned balance that drives increased sales and advances the company toward achieving its goals. The critical components known as the four Ps include: Product: Ensuring that what is offered meets consumer needs. Price: Setting it at a level consumers are prepared to pay. Place: Carefully selecting distribution locations for optimum access. Promotion: Communicating persuasive messages that captivate and connect with audiences. When these factors harmonise, they form the foundation of impactful marketing initiatives, from basic strategies through to complex campaigns. A product’s features aligned with well-considered pricing structures, strategic distribution channel choices, and cohesive promotional activities together orchestrate success—building brand loyalty, increasing market visibility and securing a dominant spot within today’s competitive marketplace for businesses seeking distinction. Crafting Your Offering: Product Strategies Embarking on a successful marketing strategy hinges upon the product—a concrete exemplification of what a business brings to its clientele. Such products may encompass various forms from: Physical goods Services lacking physical form Experiences provided Digital offerings The true test involves not merely crafting an item, but grasping its attributes, promotional narratives, and, most importantly, how it addresses customer needs. To resonate with its target audience aptly, a product must navigate the evolving landscape of consumer behaviour and trends. This necessitates deep insights into what customers seek, extensive experimentation, and continuous enhancement of the value proposition offered by the product. Companies that agilely adjust to changing tastes in consumer preferences are typically those that attain market success with their products. Understanding Customer Needs Marketing strategy is fundamentally anchored in the profound grasp of what customers seek. Decoding their needs—akin to deciphering an esoteric language—not only paves the way for tailored products and marketing initiatives but also enables personalised customer experiences. By delving beyond apparent desires into core motivations, companies can craft offerings that connect profoundly with their target market. Deep knowledge of both product intricacies and consumer preferences must precede a product’s market introduction. An insightful exploration into potential customers’ mindsets guides every aspect of marketing—from crafting content to orchestrating sales promotions—ensuring each communication resonates accurately and that every service or item perfectly aligns with customer expectations within the specified target audience. Product Differentiation and Positioning Within the commerce industry, it is just as vital to stand out from the competition as it is to resonate with customers. Carving a distinct place in the minds of consumers through product differentiation and strategic positioning sets a brand apart from its rivals. By integrating distinctive features and designing appealing packaging, companies can draw in and maintain clientele, setting a foundation for enduring customer loyalty. Take, for instance, brands like Dollar Tailoring their offerings by focusing on lower-income groups and budget-conscious buyers—through competitive pricing strategies and ongoing promotional deals—how they have effectively captured their desired market segment. Having an acute awareness of cultural distinctions and local customs is necessary for businesses striving to create international appeal. This ensures that products are not only visible but also embraced across various cultures. Pricing Mastery: Developing Your Product Pricing Strategy Pricing extends beyond simply attaching a number to a product. It communicates the perceived value, quality, and position of the brand. To craft an effective pricing strategy, companies must possess comprehensive insights into their production expenses, competitors’ price points, and most importantly, how consumers perceive value and quality. This requires careful consideration, as organisations need to measure their own costs against what customers are prepared to pay while ensuring that their chosen pricing models complement the overarching marketing strategy. Executing a robust pricing strategy is needed for driving revenue growth and sustaining profitability. The process involves: Comprehending the fundamental cost associated with creating goods along with determining suitable markups that sustain financial objectives. Assessing consumer evaluations regarding both quality and worth. Confirming prices mirror how much consumers believe the product deserves. Psychological Pricing Tactics Understanding the mind is like becoming an expert in psychological pricing. Adopting strategies that set product prices at, say, $9.99 as opposed to a round $10 exploits consumer perception, fostering an impression of greater value and cost savings. This clever yet impactful approach significantly enhances purchase probabilities by appealing to consumers’ innate appetite for deals, thereby augmenting the effectiveness of sales promotions. For marketers, it’s imperative to delve into the psychological foundations that underlie pricing techniques. The essence lies not merely within digits adorning tags, but in how these figures are perceived and the emotions they incite. Within the promotional mix landscape, price wields considerable influence over consumer choices and satisfaction levels post-purchase. Hence it serves as an influential tool for shaping purchasing behaviours. Competitor Price Analysis In sectors where there is a high degree of similarity in products and services offered, the price often becomes the critical element that sways customers toward one brand over another. Conducting competitor price analysis enables companies to fine-tune their pricing strategies with careful consideration of what competitors are charging. This insight empowers them to competitively place themselves within the market by either matching value or setting themselves apart through unique selling points. The necessity for strategic placement amplifies in environments dense with competition, as carving out a distinct space can prove difficult. With insights gained from examining the prices set by their industry counterparts, businesses have the opportunity to: Revisit and refine their own pricing models Enhance profit margins Ensure that their product’s cost accurately mirrors both how they want the brand perceived and meets customer expectations. Placement Decisions: Optimising Distribution Channels The component of ‘Place’ within the marketing mix emphasises ensuring product availability when and where customers desire it. This entails identifying optimal selling points, discerning the preferred shopping venues of the target audience, and adeptly handling stock levels and delivery logistics to streamline and enhance the customer’s purchasing experience. Determining the appropriate retail platforms and determining whether to engage in B2B or B2C commerce are vital determinants affecting a product’s market performance. Ensuring that products are readily available at places frequented by potential buyers is essential—this strategic placement has a direct impact on satisfying consumer needs and providing accessible services. Digital Presence and E-commerce Maintaining a solid online presence and the ability to engage in e-commerce, now more than ever, are essential. As 93% of business-to-business purchasers show a preference for using online avenues when making buying choices, possessing an active digital footprint is now pivotal within effective distribution methodologies. This approach not only extends market access but also enhances the efficiency of transactions and offers instantaneous insights that empower companies to make adjustments to their marketing undertakings. By integrating diverse tools associated with digital marketing into systems such as Marketing Hub, particularly those focused on search, often referred to as “search engine marketing,” enterprises can significantly enhance their operational prowess. The array of instruments at one’s disposal includes: Content creation through blogging Search Engine Optimisation (SEO) Managing social media platforms Strategic email campaigns Monitoring advertisement performance Leveraging these resources allows organisations not just broader exposure but also provides them with opportunities for more profound engagement with audiences. Fostering conversions into sales while strengthening bonds with consumers, thereby maximising their overall outreach impact in the framework of modern-day commerce. Delivery Logistics and Physical Location Just as needed as digital approaches are the concrete aspects of positioning, which include the physical placement and the management of delivery logistics. Choosing a strategic physical location can significantly boost product sales and elevate the overall customer experience. The design and visual appeal of a place, be it for retail or providing services, is essential in both drawing customers in and keeping them coming back. How products are transported to customers—via shipping methods, transit systems, or options like picking up in-store—is essential to shaping their purchasing journey. Swift and competent handling of delivery logistics ensure that items reach clients quickly and undamaged, greatly affecting their impression of your brand along with their inclination to become repeat buyers. Amplifying Visibility: Crafting a Promotion Strategy Promotion is essentially the platform businesses use to introduce their offerings to the world. A well-crafted promotion strategy employs a variety of tactics, including: Advertising Public relations Social media marketing Content marketing These tactics work together to create compelling marketing messages that showcase the importance of marketing skills. These messages must resonate with the target audience and reinforce brand awareness, ultimately leading to increased lead generation and sales. Identifying the perfect timing and utilising the most efficient marketing channels for compelling advertising is key to engaging the targeted audience. An effective marketing strategy is not just about broadcasting messages; it’s about engaging in a dialogue with potential customers, understanding their needs, and providing them with reasons to choose your brand over others. Integrated Marketing Communications Integrated Marketing Communications (IMC) functions like a conductor leading an orchestra, ensuring all communication methods convey a unified brand message. IMC transcends the alignment of advertising strategies. It’s about crafting a cohesive experience for consumers across various platforms, such as: Email marketing Print media Social networking sites Television commercials Public relations initiatives Direct mailing campaigns This integration bolsters customer satisfaction and fosters loyalty through harmonised messages. Synchronising promotional activities not only extends reach but can also trim costs and amplify returns on investment. Digital marketing shines in this ensemble by providing targeted outreach and detailed analytics regarding campaign effectiveness. This empowers businesses to refine their engagements with clientele, elevating direct marketing efforts and other aspects of their overall strategy. Leveraging Social Media Marketing Social media has become an integral element of a brand’s marketing strategy. This medium allows brands to build a community by directly engaging with their customer base. Social media marketing stands as a vital pillar within the broader scope of digital for its real-time interaction capabilities. It provides platforms for customers to provide immediate feedback and allows brands to adjust their services or products according to customer needs. Social media affords marketers critical insights gathered from data analysis that can greatly enhance how they engage with customers and refine overall marketing efforts. Marketers are empowered through these insights to craft campaigns tailored specifically toward their audience, which helps drive deeper engagement and cultivate enduring loyalty towards the brand. Extending Beyond Basics: The Extended Marketing Mix or 7 P’s of the Modern Marketing Mix The traditional four Ps of marketing is enriched by adding three essential elements to form an extended mix. People: concentrating on the business’s human factor Process: emphasising efficient service provision Physical Evidence: acknowledging the concrete items that customers come into contact with These components broaden the scope of the traditional marketing mix and are vital in forging a holistic, consumer-focused marketing strategy that connects more profoundly with customers. When businesses incorporate people, processes, and physical evidence into their marketing approach, they don’t just satisfy customer expectations—they surpass them. These additional facets allow companies to set themselves apart from competitors, enhance customer delight, and cultivate a robust and enduring brand identity. People at the Heart of Your Business People form the lifeblood of any organisation, influencing the customer experience and fostering loyalty. A customer-centric organisational culture enhances product and service delivery and attracts and retains top talent. When employees are motivated and aligned with the company’s values, they’re more likely to go above and beyond in their roles, directly contributing to customer satisfaction. Businesses prioritising their people and cultivating a supportive company culture find that it pays dividends. Happy employees lead to happy customers, and when customers feel valued and understood, they’re more likely to become loyal brand advocates. This human-focused approach is crucial to any successful marketing strategy, as it ensures that every interaction reflects the company’s dedication to excellence. Process Optimisation for Customer Satisfaction The procedure functions as a guiding framework for providing products and services, with its fine-tuning being crucial for achieving consumer satisfaction. Successful methodologies provide ease, swift delivery, and outstanding service—each element shaping how customers view a brand. Companies can deliver individualised and impactful services by centring employees on key client-oriented processes. Marketing Hub exemplifies the simplification technology brings to marketing automation. It enables marketers to handle data and instruments more effectively while elevating customer satisfaction. Procedures ought to be customised according to product types and anticipated by the target audience to align in relevancy and productivity. The Role of Physical Evidence in Marketing In marketing, the concept of physical evidence goes beyond the product itself and includes all visible elements that a customer might encounter when engaging with a brand. This encompasses aspects such as branding, packaging, and even how a company’s physical location is designed—all crucial factors that can sway consumer perception and enhance the impact of an organisation’s marketing strategy. These concrete components act like mute promoters for the brand, transmitting messages about its values and quality without saying anything. The atmosphere provided by retail space, aesthetic choices in product packaging design, and consistent staff uniforms play key roles in forging memorable customer experiences. When businesses pay attention to these details and intentionally shape them, they can forge an attractive brand identity that connects deeply with their target audience and gives them an edge over the competition in today’s marketplaces. Summary The journey to business success is multifaceted. Mastering the 4 Ps—Product, Price, Place, Promotion—and incorporating People, Process, and Physical Evidence into the mix can create a powerful marketing strategy that resonates with consumers and drives business growth. Each element plays a crucial role, and when harmonised, they form a symphony of strategic decisions that captivate the target audience and cement a brand’s market presence. Let this be the catalyst for innovation and inspiration in your marketing endeavours. With the insights and strategies discussed, you’re now equipped to craft marketing campaigns that meet customer expectations and exceed them, fostering loyalty and carving out a distinctive place for your brand in the marketplace. Frequently Asked Questions What exactly is the marketing mix? The 4 Ps—Product, Price, Place, and Promotion—constitute the core framework of the marketing mix. This critical model steers businesses in formulating successful marketing tactics to satisfy customer needs and accomplish business objectives. How do psychological pricing tactics influence consumer behaviour? Employing psychological pricing strategies, like placing price points at $9.99 rather than an even $10, crafts the illusion of a better deal, which persuades customers to believe they are receiving greater value for their expenditure. Such methods can have a considerable effect on consumer purchasing choices. Why is an integrated marketing communications strategy important? A strategy for integrated marketing communications is crucial as it guarantees uniformity in the brand’s messaging across every marketing channel. This coherence results in a fluid customer experience that boosts overall contentment and fosters loyalty. Can social media marketing improve customer engagement? Marketing through social online platforms enhances the capacity for immediate communication and collection of instantaneous responses from clients, thereby providing an opportunity to elevate customer engagement substantially. What role do people play in the extended marketing mix? People play a central role in the extended marketing mix. They create the customer experience and contribute to loyalty and business success.

What we will cover: What is the Net Promoter Score?Calculation of Net Promoter ScoreHow to judge whether a Net Promoter Score is good or badUnderstanding the limitations and potential of NPSHow can a business improve its Net Promoter Score?Net Promoter Score: Key facts and FAQs What is the Net Promoter Score? NPS is a benchmarking tool for customer satisfaction. The NPS method, which is based on a two-minute survey, gives insights about customer loyalty by measuring customers’ willingness to recommend a business to a friend or acquaintance. NPS differs from other related benchmarks, such as the customer satisfaction score, by indicating a customer’s general sentiment about a brand as opposed to their opinion on particular interactions or purchases. Because of this, it crops up frequently in discussions about customer experience. In addition, net promoter score is a standard benchmark used by companies worldwide. This makes it a good way for businesses to gauge their performance as compared to their competitors. Calculation of Net Promoter Score Step One: Determine the number of promoters, passives and detractors The calculation of net promoter score is based on a two-minute questionnaire that asks customers to rate how likely they would be to promote a brand to their friends and acquaintances. “On a scale of 0-10, how likely is it that you would recommend us to friends, colleagues or business associates?” This basic question (give or take slight variations in wording) is the one upon which all net promoter score calculations are based. The survey participant is most often asked to provide a rating on a scale of 0-10. According to the number they choose, they are placed in one of the following three categories: 1. Promoters Promoters are people who assign a score from 9-10. They are deemed more likely to exhibit behaviours that generate value, such as buying more, returning to the brand over a long period and referring more people. They have what is known as a high “lifetime value”. 2. Passives Passives are people who give a score of 7 or 8. They are considered to be moderately satisfied. They might remain loyal to the brand, but also have the potential to switch allegiance to a competitor if the conditions are right. They won’t make special efforts to refer a prospect. 3. Detractors Detractors give a score between 0 and 6. They are (usually) actively dissatisfied customers who have the potential to damage your brand reputation through negative reviews, social media interactions or word-of-mouth. To calculate the net promoter score, you simply deduct the percentage of detractors from the percentage of promoters. The worst possible score – i.e., the score that would be achieved if every customer was a detractor, is -100. The best is 100. However, both of these scores are highly unlikely in real life. Passives count toward the total number of respondents, which decreases the percentage of detractors and promoters equally. This drives the overall score towards 0. Step Two: Drill down into the net promoter score To enable companies to drill down from the broader insight provided by the NPS, they are encouraged to follow the main question with a request that draws out the customer’s reasons. These questions might, for example, ask about a customer’s opinion on the customer service they have received. The responses can be translated into follow-up action and coaching measures. Since it can be tricky to analyse open-ended feedback objectively, companies often provide rating scales for these additional questions, too. The additional questions help companies understand the relative contribution of individual products, services and business areas to the NPS. Sample Net Promoter Score Calculation Let’s imagine a scenario where a company surveys 200 of its customers with the standard NPS question. Remember that the purpose of NPS to find out about general customer sentiment, so this question shouldn’t be targeted towards a particular product. After the responses are totalled, 125 of the respondents are promoters, 42 are passive and 33 are detractors. The first step is to calculate these amounts as percentage values: Promoters = ((200 – (42 + 33))/200) x 100 = 62%Passive = ((200 – (125 + 33))/200) x 100 = 21%Detractors = ((200 – (125 + 42 ))/200) x 100 = 16.5%Then, we apply the net promoter score formula:Company NPS = %Promoters – %DetractorsCompany NPS = 62.5 – 16.5 = 46 The interpretation of NPS is highly complex and context-dependent. To find out more, see the section “How can I judge whether a net promoter score is good or bad?”. Why measure net promoter score? As mentioned earlier, the NPS methodology is primarily intended to measure customer loyalty to a company or brand – in other words, how likely they are to buy again, act as a brand ambassador and resist pressure to defect. This last point can also be expressed as “churn rate” – that is, the likelihood of them cancelling a subscription or not repurchasing. This is important because it is cheaper to retain a customer than acquire a new one. There are a number of ways in which measuring NPS can be beneficial for your company. Closing the feedback loop: The net promoter system gives companies a chance to “close the loop” – that is, to go vertical and gather more information from respondents. It also gives them chance to change a negative impression. Since an NPS survey only takes a minute of a customer’s time, it’s relatively easy to get them to engage. Easy to use: You don’t need to be a trained statistician to administer an online NPS poll. In a similar vein, the survey is intuitive and simple for customers to complete. You can send it to them via email or include it on your website as a pop-up after a transaction. The formula can be calculated with a basic spreadsheet. A common language for the customer conversation: By breaking down customers down into promoters, passives and detractors, the NPS system makes it easy to differentiate between them. Everyone in the company has a common set of definitions to work with Easier benchmarking: NPS is a standard metric used by companies globally. As such, it lets you place your score in the context of other scores in your industry and see how you measure up. NPS is also ideal for presenting to senior management as a big-picture snapshot of customer loyalty at a given moment. Driving growth: When companies take on the NPS question and begin to study it as a key metric, it helps them channel their customer service efforts and grow revenue through referrals and upsells. This is covered in more detail later in the article. How to judge whether a Net Promoter Score is good or bad What is a “good” net promoter score? To answer this question, it’s important to understand that there isn’t one “holy grail” number to strive for. The results vary highly from industry to industry. Technically, any score above 0 can be considered a “good” score, since it means you have more promoters than detractors. According to global NPS standards, a score of above 50 is good, and above 70 is outstanding. However, both of these scores are rare. A good way to use NPS is to look at the score of a close competitor and see how yours matches up. However, it’s equally as important to look at the scores for your industry as a whole. In many sectors, a score in the 30s or 40s is something to aspire to. The average score for department and speciality stores (58) is higher than for airlines (35), which in turn is higher than for internet service providers (2). If your score indicates that you are having more success with customer relationships than industry competitors, you can reasonably assume that your customers will stick around. Understanding the limitations and potential of NPS However, there are also many factors that are out of your control. Research has shown repeatedly that customers are more likely to report a bad experience than a good one. Your best and most loyal customers might simply not bother to score you at all! For this reason, it’s important not to treat the number as an end in itself, but to look at the direction in which your NPS is trending. What story does it tell about your customer relationships? If your number of promoters is growing, this could mean that brand image is on the up. On the other hand, a decrease in promoters relative to the number of passives could be a red flag for a potential increase in churn. With this in mind, the key question for companies is: how can you act quickly on customer insights to improve customer experience, grow NPS and reduce the number of defections? How can a business improve its net promoter score? There’s no silver bullet for improving NPS, but there are a number of best practices you can use to enhance the quality of your customer experience and achieve happier customer interactions. Get everyone involved Make sure the whole company is aware of NPS and why it is important – that is, not just as an end in itself. A low NPS is an issue for everyone. Ask sales, marketing and customer service to think of ways in which they could refine communication with prospects and get them excited about the business. Find a connection Empathy is key – after all, customers are humans. They want to feel that a brand resonates with them and their values. If your company has hard-hitting values or a unique culture, think about how you could show it off. Ask promoters for their opinion These customers already like you and will be willing to help. Where do they get their sense of connection? Ask detractors how you could improve Detractors have real and genuine value to add. Accept their criticisms and use them as the basis for sincere reflection. Promote customer advocacy Customer advocacy schemes use personalised interactions to encourage existing customers to become promoters. Create an online community space for customers and offer appealing incentives for referrals. Think of creative ways to encourage them to share positive experiences on social media. Improve front-line communication The job of customer service reps is to build the human connection and show customers that their business is valued. Channel customers to the right rep and make sure that reps are equipped to offer personalised, efficient care. Consider whether your product is at fault Use focus groups and customer visits to see how people interact with your product and understand whether it actually meets the needs of your customer base. The most important thing to remember is that NPS shouldn’t be a meaningless “vanity metric”. By following the correct into a stepping stone for improving customer service. The point of measuring NPS is not merely to make the competitive range for your industry, but to transform customer orientation into a central part of your company culture. That’s a lot of info! Here’s what you should take away from this article: What is a net promoter score? Net promoter score, or ‘NPS’, is a way of measuring customer sentiment based on a simple two-minute survey. How is a net promoter score calculated? A net promoter score reflects how likely customers are to promote a brand. The score is simply the percentage of detractors subtracted from the percentage of promoters. Why measure net promoter score? When companies measure their net promoter score, it helps them properly channel their customer service efforts and grow revenue. How can I tell if a net promoter score is good or bad? According to global NPS standards, a net promoter score above 50 is good, and above 70 is outstanding. What are the limitations of net promoter score? Customers are more likely to report bad experiences, so the actual net promoter score isn’t as important as its direction. How can businesses improve their net promoter score? Businesses can improve their net promoter score by: Communicating empathetically Perfecting the product Promoting customer advocacy Listening to detractors Frequently Asked Questions What is the Net Promoter Score? Net promoter score, or ‘NPS’, is a way for businesses to measure customer satisfaction. To find it, customers answer a simple survey gauging how likely they are to recommend a business to a friend or an acquaintance. Their answers are then fed into a formula to produce a single figure used for universal benchmarking. Who uses Net Promoter Score (NPS)? Net promoter score is a standard benchmark used by companies around the world. Businesses use their net promoter score, or ‘NPS’, to measure customer satisfaction and loyalty to a brand. Net promoter score is a helpful tool for organisations to see how their customer service is perceived and where improvements might be made. Why is Net Promoter Score important? Net promoter score helps businesses gauge the quality of their customer service, particularly in relation to their competitors. Organisations can use their net promoter score to address any problems areas, improve the experience of their customers, monitor loyalty trends, and grow revenue through referrals and upsells.

Understanding the elasticity of demand is critical for businesses looking to make informed pricing decisions and optimise revenue. Throughout our exhaustive guide, we’ll take a closer look at the concept of elasticity, its importance in pricing strategies, and how LIKE.TG can provide valuable insights into demand analysis. We will explore the different types of elasticity, including cross elasticity and advertising elasticity, and provide practical examples to illustrate these concepts. What is price elasticity of demand? Within economics, understanding consumer behaviour is key for businesses seeking to optimise revenue and make informed pricing decisions. A major concept in this pursuit is elasticity of demand, which measures the responsiveness of consumer demand to changes in price. Simply put, it assesses how sensitive consumers are to price fluctuations. Price elasticity measures further refine this concept by quantifying the responsiveness of demand to price changes, computed as the percentage change in quantity demanded or supplied divided by the percentage change in price, and categorising it as elastic, inelastic, or unitary based on the responsiveness to price changes. Elasticity of demand is expressed as a percentage of absolute value, indicating the proportional change in quantity demanded in response to a given percentage change in price. A higher elasticity value indicates that consumers are highly responsive to price changes, while a lower value suggests a more inelastic demand. This economic concept holds significant importance for businesses as it provides insights into consumer preferences, market dynamics, and revenue potential. By understanding elasticity, businesses can effectively set prices, develop pricing strategies, and anticipate consumer reactions to price adjustments. Elasticity vs. Inelasticity of Demand In economics, demand elasticity is often contrasted with its opposite—inelasticity of demand, including the extreme case of perfectly inelastic demand where demand remains unchanged regardless of price changes. Inelastic demand occurs when consumer demand remains relatively unchanged even in response to significant price fluctuations. This means that consumers are less sensitive to price changes and will continue purchasing a product or service despite price increases or even price decreases. Inelastic demand is often observed in industries where consumers rely on essential products or services, such as healthcare, utilities, or staple foods. For instance, if the price of electricity increases, consumers may have little choice but to pay the higher price since electricity is a necessity for daily life. When price rises in the context of inelastic demand, the quantity demanded does not decrease significantly, highlighting the consumers’ dependency on these essential goods or services. Conversely, elastic demand occurs when consumer demand is highly responsive to price changes. In such cases, consumers are more likely to adjust their consumption patterns based on price fluctuations. Elastic demand is commonly found in industries where consumers have multiple options or can easily substitute one product for another. For example, if the price of a particular brand of clothing increases, consumers may switch to a different brand or a cheaper alternative. The elasticity of demand is measured using a formula that calculates the percentage change in quantity demanded divided by the percentage change in price. A coefficient of elasticity greater than 1 indicates elastic demand, while a coefficient less than 1 represents inelastic demand. Understanding the concept of elasticity vs. inelasticity of demand is all-important for businesses as it helps them make better decisions regarding pricing strategies and revenue optimisation. By analysing elasticity, businesses can determine how price changes will impact consumer behaviour, market demand, and overall revenue. This knowledge allows companies to set prices that maximise profits while considering consumer preferences and market dynamics. Formula for calculating elasticity of demand To calculate the elasticity of demand, economists use the following formula: “` Ed = (% Change in Quantity Demanded) / (% Change in Price) “` In this formula, Ed represents the elasticity of demand. The percentage change in quantity demanded refers to the change in the quantity of a product or service that consumers are willing and able to buy in response to a change in price. The percentage change in price refers to the change in the price of the product or service. To calculate the elasticity of demand, you first need to determine the initial price increase, quantity demanded and the initial price. Then, you need to calculate the percentage change in quantity demanded and the percentage change in price. Finally, you can divide the percentage change in quantity demanded by the percentage change in price to find the elasticity of demand. For example, let’s say that the initial quantity demanded for a product is 100 units and the initial price is $10. If the price of the product in demand increases to $12, the quantity demanded decreases to 80 units. The percentage change in quantity demanded is (80 – 100) / 100 = -20%. The percentage change in price is (12 – 10) / 10 = 20%. The elasticity of demand is -20% / 20% = -1. In this example, the elasticity of demand is -1, which means that demand is a perfectly inelastic amount. This means that a 20% increase in price causes only a 20% decrease in quantity demanded. Cross Elasticity of Demand Cross elasticity of demand measures the responsiveness of demand for one product to a change in the price of another product. It is a valuable metric for businesses that offer multiple products or services, as it helps them understand how changes in the pricing of one product may impact the demand for other products in their portfolio. The cross-elasticity of the demand curve is calculated using a formula similar to the one used for elasticity of demand: “` Cross Ed = (% Change in Quantity Demanded of Good X) / (% Change in Price of Good Y) “` Where: * Cross Ed is the cross elasticity of demand. * The percentage change in quantity demanded of good X refers to the per cent change in the quantity of good X that consumers are willing and able to buy in response to a change in the price of good Y. * The percentage change in price of good Y refers to the change in the price of good Y. A positive cross-elasticity of demand indicates that goods X and Y are substitutes, meaning that consumers are likely to switch to good X if the price of good Y increases. A negative cross-elasticity of demand indicates that goods X and Y are complements, meaning that consumers are likely to buy less of good X if the price of good Y increases. Understanding cross elasticity of demand is essential for businesses that want to optimise pricing strategies and maximise revenue. By analysing cross elasticity, businesses can identify opportunities for product bundling, price discrimination, and other pricing strategies that can increase sales and profitability. Advertising Elasticity of Demand This section discusses advertising elasticity of demand, a measure of how responsive demand is to changes in advertising expenditure. Calculating advertising elasticity of demand involves determining the initial quantity demanded, advertising expenditure, and calculating the percentage change in both. The formula is similar to elasticity of demand: “` Ea = (% Change in Quantity Demanded) / (% Change in Advertising Expenditure) “` Positive advertising elasticity indicates that increased advertising leads to increased demand. Conversely, negative advertising elasticity suggests that increased advertising has an adverse effect on demand. Understanding advertising elasticity of demand helps businesses optimise advertising budgets and allocate resources effectively. Factors influencing advertising elasticity include product type, market competition, brand loyalty, and advertising effectiveness. Businesses must consider these factors when making advertising decisions to maximise return on investment. In conclusion, advertising elasticity of demand is a major concept that businesses should consider when developing marketing strategies. By analysing and understanding advertising elasticity, businesses can make decisions about their advertising investments, optimise their marketing mix, and achieve their desired business objectives. What Are the 4 Types of Elasticity? There are four main types of elasticity: price elasticity, income elasticity, cross elasticity, and advertising elasticity. Price elasticity of demand measures the responsiveness of quantity demanded to changes in price. It is calculated by dividing the percentage change in quantity demanded by the percentage change in price. A price elasticity of demand of -1 indicates that a 1% increase in price will lead to a 1% decrease in quantity demanded. A price elasticity of demand of 0 indicates that a change in price will not affect quantity demanded. A price elasticity of demand greater than 0 indicates that a change in price will lead to a more than proportionate change in quantity demanded. Income elasticity of demand measures the responsiveness of quantity demanded to changes in consumer income. It is calculated by dividing the percentage change in quantity demanded by the percentage change in income. An income elasticity of demand of 1 indicates that a 1% increase in a consumer’s income will lead to a 1% increase in quantity demanded. An income elasticity of demand of 0 indicates that a change in income will not affect quantity demanded. An income elasticity of demand greater than 0 indicates that a change in income will lead to a more than proportionate change in quantity demanded. Cross elasticity of demand measures the responsiveness of demand for one product to changes in the price of another product. It is calculated by dividing the percentage change in quantity demanded for one product by the percentage change in price of the other product. A cross-elasticity of demand of 1 indicates that a 1% increase in the price of one product will lead to a 1% increase in the quantity demanded for the other product. A cross-elasticity of demand of 0 indicates that a change in the price of one product will not affect the quantity demanded for the other product. A cross-elasticity of demand greater than 0 indicates that a change in the price of one product will lead to a more than proportionate price change, in the quantity demanded for the other product. Advertising elasticity of demand measures the responsiveness of quantity demanded to changes in advertising expenditure. It is calculated by dividing the percentage change in quantity demanded by the percentage change in advertising expenditure. An advertising elasticity of demand of 1 indicates that a 1% increase in advertising expenditure will lead to a 1% increase in quantity demanded. An advertising elasticity of demand of 0 indicates that a change in advertising expenditure will not affect the quantity demanded. An advertising elasticity of demand greater than 0 indicates that a change in advertising expenditure will lead to a more than proportionate change in quantity demanded. What Does a Price Elasticity of 1.5 Mean? This section discusses the meaning of a price elasticity of 1.5. It explains that this relatively elastic demand indicates that demand is relatively elastic, meaning that consumers are sensitive to changes in price and will adjust their consumption accordingly. A price elasticity of 1.5 means that for every 1% change in price, the quantity demanded will change by 1.5%. In other words, if the price of a good increases by 1%, the quantity demanded will decrease by 1.5%. Conversely, if the price of a good decreases by 1%, the quantity demanded will increase by 1.5%. This information is valuable for businesses because it helps them understand how consumers will respond to changes in price. If a business knows that demand for its product is a perfectly elastic demand, it may be more likely to raise prices, as it knows that consumers will not significantly reduce their consumption. Conversely, if a business knows that demand for its product is inelastic, it may be less likely to raise prices, as it knows that consumers will not significantly increase their consumption. Businesses can use price elasticity to make informed choices about pricing, product development, and marketing. By understanding how consumers will respond to changes in price, businesses can maximise their total revenue, and profits. Demand Elasticity and LIKE.TG Understanding demand elasticity is crucial for businesses to optimise pricing strategies and maximise revenue. LIKE.TG, a leading customer relationship management (CRM) platform, offers powerful tools and features to analyse and understand elasticity. By leveraging LIKE.TG, businesses can gain valuable insights into customer behaviour and market dynamics, enabling them to make data-driven decisions about pricing. One key aspect of LIKE.TG’s elasticity analysis capabilities is its ability to track and analyse customer data. LIKE.TG collects and stores comprehensive information about customer interactions, including purchasing history, product preferences, and communication channels. This data can be leveraged to identify patterns and trends in customer behaviour, helping businesses understand how price changes affect demand. LIKE.TG also enables businesses to conduct A/B testing and controlled experiments to measure the impact of price changes on demand. By creating different pricing scenarios and analysing customer responses, businesses can quantify the elasticity of demand and determine the optimal pricing strategy. This data-driven approach minimises the risk associated with pricing decisions and maximises revenue potential. LIKE.TG provides robust reporting and visualisation tools to present cross-price elasticity and analysis results in an easily understandable format. Businesses can generate reports and dashboards that illustrate the relationship between price and demand, allowing them to identify price points that optimise revenue and customer satisfaction. These insights empower businesses to make informed pricing decisions, ensuring long-term success and profitability. LIKE.TG plays a vital role in helping businesses understand and analyse demand elasticity. By leveraging its exhaustive data collection, A/B testing capabilities, and reporting tools, businesses can make data-driven pricing decisions that optimise revenue and customer satisfaction.

Demand forecasting is an essential business practice in which companies are able to anticipate future market demands for their products or services. By accurately predicting these demands, businesses can optimise their operations, minimise costs, and effectively meet customer needs. Throughout this blog, we’ll take a closer look into the concept of demand forecasting, explaining its significance and exploring the various factors that influence it, whilst also discussing the benefits and providing practical methods and models to help businesses forecast demand more effectively. We’ll also continue to take a look at the latest trends in demand forecasting and how LIKE.TG can be leveraged to enhance and improve demand forecasting accuracy. What is demand forecasting? Demand forecasting is a key business process that enables companies to predict future market demands for their products or services. It involves analysing historical data, current market conditions, and other relevant factors to make informed predictions about future demand patterns. By accurately anticipating demand, businesses can optimise their operations, minimise costs, and effectively meet customer needs. Demand forecasting is essential for businesses of all sizes and across all industries. It plays a pivotal part in the decision-making processes related to production, inventory management, sales and marketing teams, and overall resource allocation. Effective demand forecasting helps businesses avoid overproduction, which leads to excess inventory and increased costs, as well as underproduction, which results in lost sales and dissatisfied customers. Demand forecasting techniques range from simple to complex, depending on the availability of data and the level of accuracy required. Some common techniques include historical data analysis, passive demand forecasting, trend analysis, market research, and econometric modelling. Businesses can also leverage advanced analytics and machine learning algorithms to enhance the accuracy of their passive demand forecasts. Accurate demand forecasting provides businesses with a competitive edge by enabling them to respond swiftly to changing market dynamics and customer preferences. It helps businesses optimise their supply chains, reduce inventory holding costs, and allocate resources efficiently. Effective demand forecasting supports data-driven decision-making, leading to improved overall business performance and profitability. Demand Forecasting Explained Within business, the ability to anticipate future demand for products or services is essential. Demand forecasting serves as a compass, guiding businesses through the ever-changing currents of the market. By predicting demand accurately, companies can optimise production schedules, maintain and manage inventory levels to their optimal amount, and craft effective marketing strategies. The significance of demand forecasting lies in its power to illuminate the path ahead. Armed with insights into future demand, businesses can make choices that propel them towards success. They can anticipate market trends, identify shifts in consumer behaviour, and navigate economic fluctuations with agility. Effective demand forecasting lays a solid foundation for strategic decision-making, enabling businesses to expand production capacity, increase operational efficiencies, introduce new products, and venture into new markets with confidence. The process of demand forecasting involves a meticulous examination of historical data, market trends, and a multitude of other relevant factors. Businesses employ a range of methodologies, from time-tested statistical models to cutting-edge machine learning algorithms, to make informed predictions about future demand. The choice of method hinges on the complexity of the product or service, the availability of data, and the desired level of accuracy. However, the path of demand forecasting is not without its challenges. Uncertainty looms as demand can be swayed by a myriad of factors – economic shifts, evolving consumer preferences, technological disruptions, and the actions of competitors. To navigate these uncertainties, businesses incorporate flexibility into their quantitative demand forecasting models, ensuring they can adapt swiftly to unforeseen market changes. Despite the challenges, demand forecasting remains an invaluable tool for businesses seeking to gain a competitive edge in the marketplace. By harnessing historical data, conducting thorough market research, and employing sophisticated analytical techniques, businesses can enhance the precision of their demand forecasts. This empowers them to make better decisions, optimise operations, and stay ahead of the curve in the ever-evolving business landscape. Why Is Demand Forecasting Important for Businesses? In a business setting, demand forecasting is an essential. Equipped with insights into future demand, companies can anticipate market trends, identify shifts in consumer behaviour, and navigate economic fluctuations with agility. This foresight allows them to optimise operations, reduce costs, and meet customer demand effectively. One of the key benefits of demand forecasting is its ability to improve efficiency and reduce costs. By accurately predicting future demand, businesses can optimise their production schedules, inventory levels, and workforce planning. This reduces the risk of overproduction, which can lead to waste and increased costs, as well as the risk of stockouts, which can result in lost sales and customer dissatisfaction. Another important benefit of demand forecasting is increased responsiveness to market changes. As business environments are ever-changing, companies that can quickly adapt to changing market conditions have a significant competitive advantage. Demand forecasting helps businesses identify emerging economic trends, and shifts in consumer preferences, enabling them to adjust their strategies and product offerings accordingly. This agility allows companies to stay ahead of the competition and capitalise on new opportunities. Enhanced customer satisfaction is another key outcome of effective demand forecasting. By accurately predicting demand, businesses can ensure that they have the right products and services available to meet customer needs. This reduces the likelihood of stockouts and backorders, which can lead to customer frustration and dissatisfaction. Demand forecasting helps companies optimise their customer service operations, ensuring that they have the resources in place to handle customer inquiries and complaints efficiently. Effective demand forecasting also supports better financial planning and budgeting. By having a clear understanding of future demand, businesses can more accurately forecast their revenue and expenses. This enables them to make sound financial decisions, allocate resources efficiently, and manage cash flow effectively. Accurate demand forecasting reduces the risk of financial surprises and helps businesses maintain financial stability. Finally, demand forecasting is a key player in improving supply chain management. By sharing demand forecasts with suppliers, businesses can ensure that they have the necessary raw materials and components to meet production requirements. This collaboration helps optimise the entire supply chain, reducing lead times, minimising inventory levels, and improving overall efficiency. Effective demand forecasting enables businesses to build strong relationships with suppliers and gain a competitive advantage in the market. What Factors Impact Demand Forecasting? Various factors influence the precision of demand forecasting, a crucial component of effective business planning. These factors can be broadly categorised into external and internal elements. External factors encompass the overarching economic landscape. Economic indicators like GDP growth, inflation rates, interest rates, and consumer confidence greatly impact consumer purchasing behaviours. When economic conditions are favourable, consumer demand for products and services flourishes, while economic downturns can lead to decreased demand. Market and consumer trends are another significant external influence. Changing consumer preferences, innovative product introductions, and technological advancements can reshape market dynamics and alter product demand. Businesses must continuously monitor market trends to stay ahead of demand shifts. Seasonal patterns can also affect demand and weather conditions contribute to demand forecasting. For instance, seasonal products like winter clothing or summer beverages experience predictable fluctuations in demand. Businesses must account for these seasonal variations to optimise their own inventory planning and production strategies. Competitor activity is another external factor that can impact the demand for a product. The introduction of competing products or services, changes in pricing strategies, or shifts in marketing campaigns can influence consumer choices. Businesses need to closely monitor their competitors’ actions to mitigate any negative impact on their demand for a product. Internal factors also contribute to demand forecasting accuracy. Production capacity, inventory levels, and marketing efforts all contribute to demand forecasters. Ensuring sufficient production capacity to meet demand, maintaining optimal inventory levels to avoid stockouts, and effectively promoting products through marketing channels are essential for managing demand successfully. By comprehending and analysing these external and internal factors, businesses can enhance the accuracy of their demand forecasts. This enables them to make well-informed decisions regarding production planning, inventory management, marketing strategies, and overall resource allocation, ultimately driving business growth and profitability. Benefits of Demand Forecasting Effective demand forecasting serves as a guiding compass, empowering businesses to navigate the ever-changing currents of the marketplace with precision and agility. One of its benefits lies within resource allocation. Through accurate demand projections, businesses can meticulously plan their production schedules, ensuring that they possess the essential resources – raw materials, skilled labour, and state-of-the-art equipment – to satisfy future customer demand, without the perils of overstocking or shortages. This strategic approach translates into reduced costs and enhanced operational efficiency, laying the foundation for sustainable growth. Another highlight of demand forecasting is its ability to cultivate customer delight. By maintaining optimal inventory levels, businesses ensure that their customers can effortlessly obtain the products or services they desire, when they desire them. This proactive approach minimises the frustrations of stockouts, backorders, and interminable wait times, fostering customer loyalty and satisfaction. Meeting customer demand with precision not only strengthens the business’s reputation but also arms it with a formidable competitive advantage. Demand forecasting also serves as a catalyst for enhanced profitability. By using sales forecasting and aligning production and inventory levels with anticipated demand, businesses can effectively combat waste and maximise revenue streams. This strategic alignment allows them to produce the right products, in the ideal quantities, and at the opportune time, thereby diminishing the risks of overproduction or underproduction. Armed with accurate demand forecasts, businesses can engage in strategic negotiations with suppliers, securing favourable pricing and terms that further bolster their financial position. Beyond its immediate impact on resource allocation, customer satisfaction, and profitability, demand forecasting also elevates decision-making across all echelons of an organisation. Armed with reliable demand projections, businesses can chart their course with confidence, making plans regarding product development, marketing strategies, and expansion plans. This enables them to cease market opportunities, introduce products or services that resonate with customer needs, and venture into new markets with a calculated approach. By aligning their decisions with the compass of demand forecasting, businesses can mitigate risks and amplify their chances of success, propelling them towards sustained growth and industry leadership. How to Forecast Customer Demand To derive accurate demand forecasts, businesses must embark on a series of meticulous steps. The initial phase of demand forecasting often involves comprehending the product lifecycle and industry trends that affect demand now. It’s to recognise the stage of the product’s lifecycle (introduction, growth, maturity, or decline) and understand how industry trends may influence the demand forecast. The next step entails identifying and analysing historical demand data. This involves gathering data on past sales, customer demand, and market trends. Analysing this historical sales data can reveal patterns and trends that can inform future sales and demand forecasts. Selecting the appropriate and accurate forecasting method is also critical. There are various forecasting methods available, each with its own strengths and weaknesses. Some common methods include moving averages, exponential smoothing, and regression analysis. The choice of method depends on the nature of the product, the availability of data, and the level of accuracy required. Collecting and analysing relevant data is another key step in forecasting sales further. This may involve gathering data on various economic trends and indicators, consumer behaviour, competitor activity, and other factors that can influence demand. Analysing this data can provide valuable insights into future demand trends. Finally, it is essential to make adjustments through active demand forecasting based on market conditions. Demand forecasts are not static; they need to be continuously monitored and adjusted based on changing market conditions and customer expectations. This may involve incorporating real-time data, such as sales figures and customer feedback, into the forecasting process. By following these steps and employing robust demand forecasting techniques, businesses can enhance the accuracy of their predictions that drive success. 10 Demand Forecasting Methods This section provides an overview of 10 demand forecasting methods, encompassing time series analysis, causal methods, judgmental methods, simulation, quantitative methods, and machine learning methods. 1. Time Series Analysis Time series analysis involves analysing historical demand data to identify patterns and trends. Common techniques include moving averages, exponential smoothing, and seasonal decomposition. 2. Causal Methods Causal methods establish a relationship between demand and various influencing factors, such as economic indicators, consumer behaviour, and competitor activity. Regression analysis and econometric models are commonly used causal methods. 3. Judgmental Methods Judgmental methods involve incorporating expert opinions and market insights into the forecasting process. These qualitative methods may include the Delphi method, executive opinion, and customer surveys. 4. Simulation Methods Simulation methods use computer models to simulate real-world conditions and generate demand scenarios. Monte Carlo simulation and system dynamics are examples of simulation methods. 5. Machine Learning Methods Machine learning algorithms can analyse large datasets and identify complex patterns. Artificial neural networks, decision trees, and random forests are commonly used machine learning methods for demand forecasting. 6. Moving Averages Moving averages calculate the average demand over a specified period, smoothing out short-term fluctuations. Simple moving averages (SMAs) and exponential moving averages (EMAs) are commonly used. 7. Exponential Smoothing Exponential smoothing assigns exponentially decreasing weights to past demand data, giving more importance to recent data. Single exponential smoothing (SES), double exponential smoothing (DES), and triple exponential smoothing (TES) are different types of exponential smoothing techniques. 8. Seasonal Decomposition Seasonal decomposition separates demand into seasonal, trend, and residual components. Seasonal indices are used to adjust demand forecasts for seasonal variations. 9. Regression Analysis Regression analysis establishes a statistical relationship between demand and one or more independent variables (e.g., price, advertising, economic indicators). Linear regression, multiple regression, and logistic regression are common regression techniques. 10. Econometric Models Econometric models are advanced statistical models that account for the interdependencies and dynamics of various economic factors influencing demand. These models often require extensive data and specialised expertise. Demand Forecasting Models Demand forecasting models are vital tools for predicting future demand and aiding businesses in making better choices. Several models can be employed for various types of demand forecasting, each with its own advantages and applications. Here are some commonly used demand forecasting models: Moving Average Model: The moving average model is a simple yet effective technique that calculates the average of past project sales and future demand, over a specified period. It assumes that future demand will follow a similar pattern to past demand. This model is suitable for stable demand patterns with minimal fluctuations. Exponential Smoothing Model: The exponential smoothing model is an extension of the moving average model that assigns exponentially decreasing weights to past demand data. This model gives more importance to recent demand data, making it more responsive to changing demand patterns. It is suitable for forecasting demand patterns that exhibit gradual trends or seasonal variations. Seasonal Autoregressive Integrated Moving Average (SARIMA) Model: The SARIMA model is a sophisticated time series analysis technique that combines autoregressive, integrated, and moving average components. It is beneficial for forecasting seasonal demand patterns. The SARIMA model identifies and accounts for seasonality, making it suitable for businesses with pronounced seasonal fluctuations in demand. Machine Learning Model: Machine learning algorithms, such as regression, decision trees, and neural networks, can be employed for demand forecasting. These models leverage historical demand data, along with other relevant factors, to make predictions. Machine learning models are particularly effective in capturing complex relationships and non-linear patterns in demand data. The choice of demand forecasting model depends on various factors, including the nature and types of demand forecasting the product or service, the availability of historical data, and the level of accuracy required. Businesses may use a combination of different models to enhance the accuracy of their demand forecasts. Demand Forecasting Examples Demand forecasting is used in various industries to predict future demand for products or services. Here are a few examples short term demand forecasting: Retail: Retailers use demand forecasting to optimise inventory levels and avoid stockouts or overstocking. By accurately predicting demand, retailers can ensure that they have the right products in the right quantities to meet customer demand. This helps reduce costs associated with holding excess inventory and improves customer satisfaction by ensuring that products are available when customers want them. Manufacturing: Manufacturers use demand forecasting to plan production schedules and manage supply chains. By accurately predicting demand, manufacturers can avoid production disruptions and through an efficient supply chain, they can ensure they have the necessary resources to meet customer demand. This helps reduce costs associated with production overruns or shortages and improves customer satisfaction by ensuring that products are available when customers need them. Transportation: Transportation companies use demand forecasting to plan routes and schedules and allocate resources. By accurately predicting demand, transportation companies can optimise their operations and ensure that they have the necessary capacity to meet customer demand. This helps reduce costs associated with empty vehicles or overloaded routes and improves customer satisfaction by ensuring that goods are delivered on time. Healthcare: Healthcare providers use demand forecasting to plan staffing levels, manage patient flow, and allocate resources. By accurately predicting demand, healthcare providers can ensure that they have the necessary staff and resources to meet patient needs. This helps reduce costs associated with understaffing or overstaffing and improves patient satisfaction by ensuring that patients receive timely and efficient care. Financial services: Financial institutions use demand forecasting to manage risk, plan investments, and allocate resources. By accurately predicting demand, financial institutions can look into how to allocate their capital and manage their risk exposure. This helps reduce costs associated with bad investments or excessive risk-taking and improves customer satisfaction by ensuring that customers have access to the financial services they need. Demand Forecasting Trends This section discusses the latest trends in demand forecasting, including the use of artificial intelligence and machine learning, real-time data and analytics, collaborative forecasting, and sustainability and ethical considerations. Artificial intelligence (AI) and machine learning (ML) are transforming demand forecasting by enabling businesses to analyse vast amounts of data and identify patterns and trends that would be difficult or impossible for humans to detect. By leveraging AI and ML algorithms, businesses can create more accurate and reliable demand forecasts, leading to better decision-making and improved business outcomes. Real-time data and analytics are a major component in modern demand forecasting. With the advent of the Internet of Things (IoT) and other data-generating technologies, businesses can now collect real-time data on various factors that influence demand, such as customer behaviour, market trends, and supply chain disruptions. By analysing this real-time data, businesses can make more informed and agile decisions, quickly adapting to changing market conditions. Collaborative demand forecasting method involves bringing together different stakeholders within an organisation to contribute their expertise and insights to the demand forecasting process. This approach combines the knowledge of sales, marketing, production, and other departments, resulting in more comprehensive and accurate forecasts. Collaborative internal demand forecasting also fosters a culture of shared responsibility and improves communication and alignment across the organisation. Sustainability and ethical considerations are increasingly becoming important factors in demand forecasting. Businesses are recognising the need to minimise the environmental impact of their operations and ensure ethical practices throughout the supply chain. Demand forecasting plays a large part in optimising resource allocation, reducing waste, and promoting sustainable practices. By considering sustainability and ethical factors in demand forecasting, businesses can align their operations with their values and meet the expectations of environmentally conscious consumers. These trends are revolutionising the field of demand forecasting, enabling businesses to make more accurate predictions, like the ability to predict demand, optimise their operations, and gain a competitive advantage in a data-driven business environment. Demand Forecasting with LIKE.TG Demand forecasting is an essential business process for optimising operations, reducing costs, and meeting customer demand effectively. LIKE.TG, a leading customer relationship management (CRM) platform, provides a variety of tools and capabilities to help businesses create accurate demand forecasts. One of the key features of LIKE.TG for demand forecasting is Einstein Discovery, a powerful artificial intelligence (AI)-powered tool that helps businesses analyse historical data and identify trends and patterns that can be used to predict future demand. Einstein Discovery uses machine learning algorithms to automatically detect relationships between different variables and generate accurate forecasts. LIKE.TG also allows businesses to leverage historical sales data to create demand forecasts. By analysing past sales data, businesses can gain insights into seasonal trends, sales trends, market fluctuations, and customer behaviour patterns. This historical data can be used to build statistical models and time series analysis to predict future demand. In addition to historical data, LIKE.TG enables businesses to incorporate predictive analytics into their demand forecasting process. Predictive analytics uses advanced statistical techniques and machine learning algorithms to analyse a variety of data sources, including customer demographics, market trends, economic indicators, and social media sentiment, to further generate revenue forecasts. LIKE.TG also allows businesses to integrate external data sources into their demand forecasting process. This can include data from market research firms, industry reports, and social media platforms. By combining internal data with external data points, businesses can gain a more comprehensive view of the market and make more accurate demand forecasts. LIKE.TG provides a user-friendly interface that makes it easy to create and manage demand plans and forecasts collaboratively with team members. Different users can access and update forecasts, share insights, and discuss assumptions, ensuring a collaborative and transparent demand planning process. Finally, LIKE.TG provides tools to monitor sales forecasts, make sales projections and track the performance of demand forecasts against actual results. This allows businesses to continuously evaluate the accuracy of their forecasts and make adjustments as needed. By analysing forecast errors and identifying the factors that influence demand, businesses can continuously improve their forecasting accuracy and optimise their operations.