Credit Card Churn Prediction: Predictive analytics to reduce attrition and strengthen customer loyalty
Project Overview
This project focused on building a predictive analytics solution to identify credit card customers at risk of churn. By analysing transactional behaviour and customer attributes, the system enables banks to anticipate disengagement early and take proactive retention actions. The solution empowers marketing and relationship teams to intervene at the right time with targeted strategies, improving customer lifetime value and reducing revenue leakage.
Problem Statement
Banks traditionally relied on manual reviews and lagging indicators to identify customers likely to stop using their credit cards. This reactive approach resulted in delayed engagement, missed retention opportunities, and avoidable revenue loss. The absence of predictive insights also led to inefficient marketing campaigns, higher operational costs, and reduced return on investment.
Key Findings
- Behavioural Signals as Strong Predictors: Transaction frequency and recency of spending consistently emerged as the most reliable indicators of churn risk, enabling early identification of disengaging customers across portfolios.
- Early Warning Window: Customers showing declining engagement patterns were highly likely to churn within a 90-day period, providing a critical window for proactive retention and personalised outreach.
- Effectiveness of Targeted Interventions: Personalised, data-driven retention offers significantly increased customer reactivation and continued card usage, outperforming broad campaigns and improving overall retention efficiency.
Implemented Solution
A scalable machine learning framework was developed to support early churn detection and targeted retention efforts:
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Churn Prediction Pipeline:
Designed a robust machine learning pipeline that processes transaction history and demographic data to generate accurate churn probability scores for each customer, supporting scalable and repeatable risk assessment.
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Automated Risk Scoring:
Integrated churn scoring into existing CRM workflows, enabling real-time visibility into at-risk customers and eliminating manual identification delays, while improving response speed and prioritisation.
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Marketing Enablement:
Enabled marketing teams to launch customised reactivation campaigns based on churn risk segments, ensuring timely, relevant, and cost-effective outreach aligned with customer behaviour and value.
Results
The churn prediction model achieved 90% accuracy, providing banks with a reliable early-warning system for customer disengagement. Timely, targeted interventions led to a 20% reduction in customer churn, while significantly improving marketing efficiency and campaign ROI. Overall, the solution transformed churn management from a reactive process into a proactive, data-driven retention strategy that strengthened customer relationships and long-term profitability.