The effect of social affinity and predictive horizon on churn prediction using diffusion modeling

Abstract

The social influence of people on their peers in the selection of products and services is frequently modeled as a diffusion process. Recently, such processes have been successfully applied as a tool for predicting customer turnover, or churn, in mobile communication carriers. These predictions are most accurate when specific social ties are used in the diffusion process, and are primarily useful when they provide a long forecast horizon, so as to enable a service provider to take mitigating actions. Here, we investigate several measures of social affinity and compare their performances for churn prediction, using data from two large mobile phone carriers. Our analysis demonstrates that the various measures of social ties capture different calling and texting patterns, and that a significant improvement in the accuracy of prediction is reached by combining them. We study the predictive horizon of diffusion processes and show that it deteriorates significantly as the horizon increases. Our findings underline the usefulness of diffusion processes for enhancing churn prediction while providing insights to their limitations.