Customer Analytics: What It Is and How It Works

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In a world where businesses are driven by data, customer analytics has become increasingly important. As businesses are beginning to collect data on their customers, the process by which the company analyzes this data is constantly being developed and improved, causing more companies to recognize the value of customer analytics. In fact, a study by Mordor Intelligence reported that the customer analytics market is expected to grow from USD 3.74 billion in 2020 to USD 10.2 billion by 2026. 

Customer analytics is a method in which market segmentation and predictive analytics are used to help companies make important strategic decisions based on customer activity data. Businesses use this data to help with direct marketing, web placement, and customer relationship management. The goal of customer analytics is to understand more about customer behavior to discover how companies can attract new customers, interact with customers, and predict how customers would act in a certain situation. Customer analytics can be split into three categories: descriptive analytics, predictive analytics, and prescriptive analytics.

Descriptive analytics is the process where data is gathered, organized, and presented in a clear and understandable manner. It is used to showcase past trends in a business rather than to draw projections or forecasts of the future. Typically, descriptive analytics is presented in the form of a line graph, pie chart, table, or other visualizations.  

Predictive analytics is a category of data analytics that aims to make predictions about future outcomes by analyzing past data patterns. It uses statistical and machine learning techniques to extract useful information from data sets and make predictions. With these predictions, businesses are able to plan effectively, avoid risks, and set goals.

Prescriptive analytics, the final stage of the business analysis process, focuses on using historical data to evaluate the best course of action for a business. This stage of analysis utilizes information from descriptive and predictive analytics and further applies the new information to decision-making. This stage of customer analytics tells businesses how to best appeal to customers and recommends the actions a company should take to improve customer relations. 

One example of a company that utilizes customer analytics is Netflix. As a company with hundreds of millions of subscribers, Netflix gathers tons of data from customers all over the world. In order to organize all the information it collects, Netflix utilizes big data analytics to evaluate customer analytics (a customer’s search and watch history), allowing Netflix to offer more personalized suggestions. 

Another company that consumes loads of customer data on a daily basis is Amazon. Being the largest online shopping platform, Amazon has developed a range of techniques to attract and retain loyal customers through customer analytics. It collects information on what products a customer views, their search history, and other shopping trends. These analytics allow Amazon to offer product recommendations that give customers a more convenient and efficient buying experience. 

To conclude, customer analytics is becoming an imperative process in many companies and will continue to expand in the long term. With many larger firms that consume heaps of customer data on a daily basis already taking advantage of customer analytics, customer analytics is  becoming a new business standard and is one of the fastest growing data analytics trends.