Behavioral Modeling for Churn Prediction: Early Indicators and Accurate Predictors of Custom Defection and Loyalty
This work addresses the problem of customer retention for firms in various industries, but it is incremental as it applies existing methods to new data.
The paper tackled churn prediction by developing a unified analytic framework that identifies early warning signs and assigns churn scores to customers, achieving 89.4% accuracy in predicting subscriber churn using data from a large mobile phone network.
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 behaviors 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.