Client
Major online bank in Russel 2000
Problem
Bank had no way to predict which of its customers were likely to stop using its services. The goal was to determine which user behaviors were predictive of customer churn so that the bank could intervene and retain more customers.
Strategy & Approach
Retention Analysis
We conducted a comprehensive retention analysis for the bank, utilizing advanced analytics and machine learning to predict customer churn. By examining transaction histories, customer interactions, and demographic data, we identified patterns and at-risk customers. Features were used to train several machine learning models which were combined and deployed on the cloud. The model enabled the bank to both understand and predict customer churn and implement proactive measures to enhance customer satisfaction and retention.
Tailored Campaigns
Based on the insights, we suggested targeted campaigns designed to address the specific needs and preferences of at-risk customers. Personalized communication, special offers, and loyalty programs were developed to re-engage these customers and improve their banking experience. By leveraging data-driven strategies, the bank effectively reduced churn, boosted customer loyalty, and drove long-term growth.
Tools & methods used
- Predictive Analytics
- Data Visualization & BI Tooling
- Segmentation Analysis
- Machine Learning
