Behavioural Modelling for Churn Prediction

Behavioural Modelling for Churn Prediction

Khan, Manoj, Singh, Blumenstock. 2015 (View Paper → )

Churn prediction, or the task of identifying customers who are likely to discontinue use of a service, is an important and lucrative concern of firms in many different industries. As these firms collect an increasing amount of large-scale, heterogeneous data on the characteristics and behaviours of customers, new methods become possible for predicting churn.

In this paper, we present a unified analytic framework for detecting the early warning signs of churn, and assigning a “Churn Score” to each customer that indicates the likelihood that the particular individual will churn within a predefined amount of time. This framework employs a brute force approach to feature engineering, then winnows the set of relevant attributes via feature selection, before feeding the final feature-set into a suite of supervised learning algorithms.

Using several terabytes of data from a large mobile phone network, our method identifies several intuitive - and a few surprising - early warning signs of churn, and our best model predicts whether a subscriber will churn with 89.4% accuracy.

This paper introduces an analytical framework for detecting early warning signs of churn. Identifying customers who are likely to stop using your product or service will help you devise effective strategies to improve customer retention and the customer experience.