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This is a case to illustrate the data analytics requirement of a telecom client to identify likely churners in advance in order to plan their customer’s retention.
The primary objective of the study was to identify the patterns in customer behaviour indicative of churn potential using data-driven methods. For this, client had provided historical usage data of 5 months, focussing on individuals who had already severed ties with client along with existing customers data. Longitudinal data of more than 400K customers across 150+ data points was provided.
A notable correlation between specific usage behaviour and likelihood of churn was discovered. These behaviours included significant changes in decrease in voice call minutes, increase in calls to competition call centres, change in SMS sending behaviour, dip in calling pattern to top-3 numbers , recharging after validity expiration. Based on these findings, a predictive model that utilized real-time usage metrics to identify potential churners was deployed which enabled the client to proactively address customer dissatisfaction and implement strategies to reduce the overall churn rate.
Algorithms such as Random Forest and Boosting Trees were used to get the best fit and predictions. Improvement of 25% was achieved after digging deep into the data with an accuracy of 74%. The tool used to carry out these analyses was Statistica Dataminer. Here’s the graphical representation of the model:
