How to Identify Customers Likely to Churn

October 19, 2016
2:00 pm - 5:30 pm
Hall C

Track: General
Type: Posters
Level: All

We describe our innovative approach to predicting churn in the B2B context. We combine peers' churn analysis with customers' individual buying pattern to identify the most valuable customers at the risk of churn. We applied our method on the data from a medical equipment distributor with thousands of customers which revealed a return of investment of saving 3-8M dollars (1-3% saved revenue).


, PhD, PROS, Inc.