Customer analytics, or customer data analytics, is the use of customer data to track, analyze and make informed decisions about customer needs and expectations. 

Customer data can inform everything a company does, from how it conducts marketing campaigns to what business decisions they make, and how they prioritize product development. 

Customer analytics is a key component of customer experience, or CX, the account of perceptions that result from all customer interactions with a business. Companies can use customer analytics tools to improve CX through better customer interactions. It can help companies improve all aspects of CX, from the customer journey from customer acquisition to customer retention to ultimately improve a company’s bottom line. 

Customer experience creates an emotional bond with a customer base and helps companies build a competitive advantage through the capture of more customers, increase of sales, deeper customer loyalty.

A customer data-driven company can make more informed decisions quicker and respond to new opportunities and challenges. Therefore, companies should learn and analyze everything they can about their customers.

Companies can use metrics like purchase histories and survey data to better understand customer habits and perceptions. They can track customers across multiple touchpoints and create actionable insights. They can study how customers feel about their products and the industry and the economy at large. 

Customer analytics also help understand the return on investment for marketing efforts and product design decisions. For example, studying customer information can unearth relevant demographic information, such as which segments of customers buy the most products. It can then conduct customer segmentation, where it can personalize messages and spend more ad budget to reach those high-value customers.

Using analytics to better convert and retain customers improves profitability and potentially drives increased revenues through positive word-of-mouth. 

• Identification
• Collection
• Organization
• Analysis
• Action

Companies first need to identify which customer data they want to collect and how they collect it. Examples of customer data that companies should consider include geographic, transactional, feedback, customer support information and more.

Once companies have finalized the data that they want to track, they then need to set up the systems to capture it. That can include sign-up forms, surveys, website and social media monitoring tools and more. A company should take care to only collect the data they need and store it in a way that protects customers.

Many companies have a customer data platform (CDP) that can help them organize their data. This is especially important if the company is pulling data from multiple sources. The company also needs to restrict access to their data platform only to those who need it, potentially providing read-only access in a way that can be easily revoked.

Next, companies need to conduct customer data analysis, which they can do with a mix of AI and human actions. They need a platform that provides data visualizations so their teams can better understand the data. And machine learning can perform many more computations per hour than humans, providing deeper insights that employees can turn into actionable insights.

Now, the company needs to use the data and insights they’ve collected to make the correct decisions. They can decide whether to change their marketing strategy, the products they make and the industries in which they operate, and how they respond to customers, among other actions.

Organizations can view several important data points when determining the success of their overall customer experience strategy. There are four main categories of business intelligence analytics that also pertain to customer analytics.

• Descriptive analytics
• Diagnostic analytics
• Predictive analytics
• Prescriptive analytics

Descriptive analytics refer to historical data points that a company can review to understand what happened. Things like annual reports, sales reports and customer feedback can help companies understand how customers decisions influenced results. This particular set of analytics is only concerned with what happened, not why or what should happen next.

Diagnostic analytics relate to conducting data analysis on historic information to better understand the root cause of something. For example, a company that experienced a sharp decline in renewals can look at usage data to see whether customers stopped using the solution. If a company’s net promoter score (NPS) decreased, it might identify an issue with customer service. Possibly customer care calls took too long or did not produce enough favorable outcomes for the callers.

Predictive analytics uses historic and current data to power predictive modeling that estimates how customer behaviors and preferences can change in the future. Examples might be understanding how an increase in prices affect customer habits. It might predict what might happen to sales if a product includes a new feature. It can help understand how price increases impact demand. Or, if customer habits change, what the company needs to do to pivot to reach those customers’ needs.

Prescriptive analytics takes predictive analytics one step further. It doesn’t just predict what will happen, it helps organizations understand what they should do. Prescriptive analytics functions increasingly use machine learning and other AI tools to crunch many different data points and provide recommendations. If the company is going to raise prices, it can offer suggestions that help it minimize the decrease in customers who do not want to pay the new price. It can suggest new segments that can help fill the gaps of those customers who were price sensitive.

There are several types of data that companies should track across different categories.

• Demographic
• Psychographic
• Purchase data
• Survey results

Companies can ask customers several questions that help them understand their true feelings about their products. For example, Customer Satisfaction Score (CSAT) asks people to rank their satisfaction from 1 to 5, and calculate those who answer 4 or 5 divided by all responses. Net Promoter Score (NPS) is a percentage that is calculated by asking whether people would recommend companies or products to their networks. Finally, the Customer Effort Score (CES) tracks how difficult it was for a customer to achieve what they were trying to do.

This includes where customers live, what they do for a living, their ages, genders and other information about themselves. This data can help companies better allocate their marketing budget to specific regions and customer segments.

This data relates to how customers think and feel about issues like their values, personality and how they see the world. Examples of psychographic information can include hobbies, personalities and their consumer preferences.

Companies can track several key purchase information, such as tracking sales over a time period. They can calculate customer churn rates, which identify how many customers churn, or leave, in a given time period. They can calculate customer lifetime value (CLV), which identifies how much a customer will spend with a company over time.

Companies can track several key purchase information, such as tracking sales over a time period. They can calculate customer churn rates, which identify how many customers churn, or leave, in a given time period. They can calculate customer lifetime value (CLV), which identifies how much a customer will spend with a company over time.

Customer data collection is an important part of any customer analytics practice. Here are some of the areas where companies can access insights to make more data-driven decisions.

• Cookies
• CRM dashboards
• Email
• Social media
• Surveys
• Websites

Advertising cookies track online activity on the open web. There are two types: first- and third-party cookies. First-party cookies is data websites get directly from customers, such as email addresses, locations and shopping preferences. Third-party cookies instead track user activity across different websites, passing semi-anonymized information between parties. For example, a person who considers buying a wedding ring on a website, but does not complete the purchase, might see an ad for that same website when browsing CNN.com.

Companies can keep records of their customers and pertinent information in a CRM. These are especially valuable for business-to-business (B2B) companies that tend to have a smaller group of clients. CRMs can track communications records, sales information, the date they were entered into the database, and much more.

Email is often a major component of a company’s customer engagement. Many ask customers to supply their email address to get access to discounts or unique offers. As a result, many companies send two or three emails to customer per week. Companies should track whether customers are opening those emails and clicking on links to gauge customer interest.

Companies can track conversations about themselves and their products on social media sites. They can also monitor customer sentiment, understanding what customers are saying about the brand and its products, even if it is not writing directly to the company.

Companies can specifically ask customers and prospects questions relevant to their products and brand perspective. Customers can give honest feedback about the strengths and weaknesses of the company and its products.

Companies can track website data to answer several pressing questions. They can identify whether website visits are increasing or decreasing. They can track how long customers spend on the website and which pages they are frequenting. For example, if the FAQ page is among the most visited pages, the company might need to do a better job explaining how their solutions work.

There are several hallmarks of a modern customer analytics practice.

• AI-driven insights
• Integration with product development
• Real-time analysis

Companies can use AI tools like machine learning to crunch customer data to produce even richer insights. Machine learning can crunch more data points quicker to unearth key insights. Generative AI can help employees think deeper about how they should conduct marketing and respond to customers needs.

Companies need to not only collect customer analytics, but also turn those insights into actionable next steps. Advanced companies use information from customers to make improvements to their existing products and potentially launch new ones to meet demand.

Companies need to make more decisions quicker, so they can adapt strategies on the fly to meet changing customer needs. The process of collecting real- or near-real-time analytics provide valuable insights that can create a competitive advantage. For example, if customer preferences change and they prefer spending less even for lower quality, a brand might need to temporarily drop prices.

Customer analytics provides companies with several benefits, all aligned to knowing more about their customers. It helps them better serve their existing and new customers in several ways and drive business goals. They can use it to avoid churn, more easily recruit new customers, and figure out new growth opportunities. All positives from the use of customer analytics can help a company reduce costs and ultimately boost profitability.

• Increase sales
• Minimize churn
• Recruit new customers

Companies with advanced customer analytics can identify several ways to improve sales, from better targeting, more efficient sales cycles and identifying new product opportunities.

Companies that study customer analytics can identify ways to keep customers happy. They might know what previous issues caused more customers to leave, which they can then prioritize fixing. They can use that customer data to improve their customer service function, which also slows churn.

Companies can use existing customer data to better target new customers. For example, they can target specific segments with content they know appeals to that audience.

Companies that collect customer analytics need to safeguard that information. While there are many benefits, it also introduces several challenges.

• Cost of storage
• Customer uneasiness
• Potential for data theft
• Privacy regulations

Companies need to invest in the right tools and technology to capture and store customer data safely and securely. To stay competitive, businesses must continuously evaluate how they collect and store their customer data, optimize their infrastructure, and adopt scalable solutions that balance cost with performance.

Cookies track customers across the open web, which makes some customers uncomfortable. A customer who notices that ads are following them across websites might feel uncomfortable with that tracking.

There has been a recent move away from cookies as some browsers do not support them and customers are using privacy controls to block them. Companies understand they might not have as much third-party cookie data as before, and need to rely on first-party data and other signals to understand and successfully target their customers.

Bad actors try to steal customer data from companies globally. An IBM® report found the average global cost of a data breach in 2024 reached USD 4.88 million in 2024. There are hard costs if companies need to compensate customers or pay fines. But there are also reputational costs if the media covers the breach, especially if the company does not handle the aftermath well. Companies need to act to protect the customer data they have by using cybersecurity tools and measures.

Federal and local governments enact laws to help protect customers, and companies need to adhere to those regulations. Failing to protect customer data can have catastrophic implications, from fines to legal issues. Executives need to understand what legislation and regulations their companies are subject to and have the right processes in place to adhere to them.