Customer Churn Augmented Analytics Use Case

Predictive Analytics for Customer Churn

The Objective

Customer churn is something every business wants to avoid. The cost of acquiring and interacting with customers is one a business must fund and, every time the business loses a customer (customer churn), it must spend more money to replace that customer. Every business wishes to identify the issues that most often cause a customer to leave. Dissatisfied customers often close an account or choose another service provider without explaining their decision. The business wants to use predictive analytics to identify those customers who were most likely to leave and develop processes and strategies to improve customer retention and reduce customer churn.

The Solution

The business will use the tools provided in assisted predictive modeling to identify customers who are likely to leave, and their issues, improve services and processes and increase customer retention.

This Technique Can Be Used in Many Industries, Including

Banking, financial institutions, utility companies, business to consumer (B2C) and business to business (B2B)


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