Evaluating the Effect of Leading Indicators in Customer Churn Prediction

Customer churn prediction is needed as it is one of the preventive solutions employed to retain customers that are of high value and better prospects for future sales. This churn is preventable if service providers can identify the root cause of churn using data analysis. However, since the objective is to retain those customers after prediction, it is imperative that a significant lead time is available for the service providers to engage with their customers and react in a positive way to retain them. So, early detection of churn candidates is critical for the success of such applications. It is our hypothesis that this additional lead time to engage can be derived from analyzing data sources that have the characteristic quality of being leading indicators rather than lagging indicators. In this paper, we attempt to address the issue, by modeling leading indicator sources of temporal information that are relevant to the customer namely sentiment data and socio-economic data. We also evaluate the importance of using such data sources to address the problem of having a longer time horizon to react and respond to customer churn prediction applications. We present the results of experiments using open datasets that have been adopted to evaluate our hypothesis. Our study shows that customer sentiment and socio-economic indicators are statistically significant (P-value < 0.05) and improve churn prediction accuracy up to 20% compared to conventional approaches.