Case Studies

Predicting Prepaid Customers’ Churn for Telecom Operators

European & North-Africa Telecom Operators


Tech Solution

Case Study Details


Retention of customers is hard (no-contract compared to postpaid customers); no clear definition of churn/churners; short-term nature of prepaid products; customers moving among providers; False identification of churners; Seasonality.


New definitions for “active customer base” and “churn” are required, as well as new features for better modeling of churn.


Targeting of customer sub-groups with optimal combination of predictive value and potential financial benefit, feature engineering that takes into account customer profile and history, product usage patterns, recharge history, recharge type / package selected, voice/data profiles, complaints etc. and advanced ML for identification of highly probable churners.

Deficient Predicting prepaid customers’ churn for telecom operators



Ability to correctly identify thousands of churners with less than 10% false positives. Automated pipeline for data preprocessing, labeling, feature engineering and model training that shares predictions with marketing department to coordinate relevant offers and incentives for retention of identified customers.

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