Win, Serve And Retain Customers With 15 Customer Analytics Methods

Customer analytics takes center stage in the age of the customer for firms trying to understand and predict customer behavior. From descriptive to predictive methods, customer insights (CI) professionals can apply a wide array of analytics methods to behavioral customer data. CI professionals have a lot to consider when deciding on the right portfolio of methods to drive customer understanding – what dependencies exist between analytics methods, what investment levels are required, where to get help and what business value do these methods drive.

To make it easier, we identified 15 key customer analytics methods that help firms win, serve and retain their customers. In our latest report, “TechRadar™: Customer Analytics Methods, Q1 2014” (subscription required), we evaluate each of these methods in detail taking into consideration their current adoption as well future potential. These methods, ranging from behavioral customer segmentation, lifetime value analysis, next-best offer analysis to recommendation analysis, allow firms to analyze customer data and use the analytical insight to drive acquisition, retention, cross-sell/upsell, loyalty, personalization and contextual marketing.

Our analysis shows that:

  • Methods that drive contextual insights are in early stages. Emerging methods such as sentiment analysis, location analysis, and device usage analysis are in early stages of development, but they have the potential to provide valuable context around behavior and other customer analytics methods.
  • Methods that drive personalization will enjoy significant success. Analytical methods that drive personalization, such as next-best-offer analytics, will enjoy more success because they enable the vision of executing insights in real-time and on a one-to-one basis.
  • Established methods fall into the Growth phase. More well-known analytics methods like behavioral customer segmentation and customer churn and attrition analysis will gain more adoption because of their potential to use enhanced customer data from Big Data sources as well as leverage new data science approaches.

This research will help you decide which customer analytics methods your company should cast in the lead roles of your advanced analytics play. The show must go on, but you have the opportunity to direct the future.  

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