Identify customers who are likely to churn by analyzing cross-sectional (demographics, behavioral) and historical transactional data.
- 1.Automated the process of analyzing customer transaction data and derived features with respect to customer behavior.
- 2.Provided rationale to the client as to why the customer would churn, based on behavioral traits.
- 1.The challenge here is to use historical transactional data and patterns to understand the customer’s proclivity to churn.
- 2.A multi layer deep learning network was constructed to analyze the financial transactions and identify the behavioral patterns.
- 3.Based on the behavior, the propensity of the customer to churn was estimated using a deep learning network.
89% of the risky customers were bucketed in top 3 Decile for the entire dataset.