Case studies | Customer churn forecasting

Project goals

Identify at-risk clients and prevent retention

Result

Customer churn decrease

About the client

A leading financial institution in Azerbaijan, serving both domestic and international customers.
With a wide scope of operations, the bank offers a comprehensive range of financial services, including corporate banking, retail banking, investment banking, and asset management.

Project goals

Develop a robust customer churn prediction model to identify at-risk customers among periodic loan takers and implement targeted retention strategies to reduce churn rates;

Analyze key features influencing churn and leverage this insight to help refine marketing and onboarding processes, attracting and retaining more new customers;

Utilize churn analysis findings to help enhance existing loan products, tailoring them to better meet customer needs and preferences, ultimately boosting customer satisfaction and loyalty.

Key challenge

Dynamic Model Development: Crafting a churn prediction model adaptable to evolving customer behaviors and fluctuating economic conditions.

Complex Feature Engineering: Extracting actionable insights from diverse datasets like transaction histories, demographics, and market trends.

Personalization at Scale: Ensuring loan product customization for a vast clientele without compromising efficiency or user experience.

Our solutions

Our team crafted a dynamic churn prediction model tailored to adapt to changing behaviors and market conditions. We harnessed diverse data, from transaction histories to market trends, to refine and personalize loan offerings. This approach not only mitigated customer churn but also enhanced the banking experience for a vast clientele.

Result

Churn Reduction: Successfully reduced customer churn through a tailored prediction model.
Customer Acquisition: Attracted new clients via data-driven marketing strategies.
Product Personalization: Enhanced loan offerings, boosting satisfaction and cross-selling opportunities.