Predicting Churn in Mobile Money

East Africa


Client Brief

  • Churn is hard to define and assess in Mobile Money, as the customer can stop using the service without unsubscribing. Some users can also stop using the service for a while, just not being regular users, and are not churners

  • Predicting who is about to churn is key for MNOs to retain their clients by conducting targeted marketing actions


Solution

  • After defining clearly what was considered as churn with the MNO, Masae developed a prediction model, using past transactional data, GSM usage and KYC data (socio-demographic information on customers) to predict the likelihood of each user to churn in several time horizons (in 1, 3, 6 months...)


Deliverable

  • Source code of the algorithm that allows the MNO to apply the prediction model internally on new data to conduct real-time prediction