Predicting customer churn gives the opportunity to stem the leak in revenue base. It has the same impact as making marketing engine more effective. Reducing churn has the following strategic benefits:
1. Reduces marketing cost: Acquiring a new customer costs five times or more than that of retaining a customer.
2. Provides retention insights: Churn analysis provides important cues on retention allowing you to keep a tab on customers changing preferences.
3. Foster long-term relationships and loyalty: By acting on insights from churn analysis, you remove bottlenecks and foster long-term customer relationships.
This churn prediction case study was worked on Music service provider data. We found that transactional attributes better helped identify customer churn when compared to user or log attributes. We developed 10 base models and a two layered ensemble models. The ensemble model was the best and it predicted customers who are likely to churn with an Accuracy of 96% and F1-Score of 86.5%.
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