Reducing Customer Churn with SAP Predictive Analytics

Case studies

WMS

Reducing Customer Churn with SAP Predictive Analytics

A/V/E GmbH deployed SAP Predictive Analytics on HANA and BW to predict contract termination risk for utility clients and enable targeted retention actions.

Published

June 4, 2026

Read time

3 min read

Source

SAP

A/V/E GmbH serves over 45 German utility providers and faced rising customer churn after energy market deregulation. The company implemented the gisa.Customer Insight application, built on SAP Predictive Analytics, SAP HANA, and SAP Business Warehouse. The solution converts unstructured customer complaint data into structured variables, applies logistic regression, and generates individual churn probability scores. These scores allow energy suppliers to design segment-specific loyalty programs and reduce contract terminations.

Key takeaways

Logistic regression on complaint severity and volume predicts termination risk with 80% accuracy

Individual customer churn scores generated in under 3 seconds

Unstructured text converted to structured variables for modeling

Targeted retention campaigns based on probability rankings

Near-real-time data modeling supports rapid loyalty measure design

Market overview

SCR methodology note

Vendor landscape

Leaders

Implementation considerations

Important consideration

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