Understanding Customer Product Interest through Propensity Analysis


This case study explores the application of propensity analysis to determine customer interest in various product sets. Focused on developing machine learning (ML) pipelines, the study highlights the profiling of each customer for different products, ranking customers without specific products, and the integration of propensity analysis as a service within bank premises.


Identifying customer preferences and interests to personalize product offerings and improve customer satisfaction.
Developing ML pipelines capable of profiling customers for multiple products efficiently.
Integrating propensity analysis as a service within bank premises to enable real-time insights and decision-making.


Development of ML Pipelines

Design ML pipelines to analyze customer data and profile each customer's propensity for different products.
Incorporate feature engineering techniques to extract relevant features and insights from customer behavior and transaction history.

Ranking Customers without the Product

Utilize propensity scores to rank customers who do not currently have specific products.
Identify high-potential customers with a strong likelihood of being interested in the product based on their profile and behavior.

Integration of Propensity Analysis as a Service

Develop a scalable and reliable service for propensity analysis within bank premises.
Implement real-time monitoring and reporting functionalities to enable bank personnel to access propensity insights seamlessly.


Successfully developed ML pipelines to profile customers for various products, enabling personalized recommendations. Ranked customers without specific products based on propensity scores, facilitating targeted marketing and product promotion.
Integrated propensity analysis as a service within bank premises, empowering bank personnel with real-time insights for customer engagement and retention.


This case study demonstrates the effectiveness of leveraging propensity analysis to understand customer product interests and improve marketing strategies within banking institutions. By developing ML pipelines, ranking customers without specific products, and integrating propensity analysis as a service, organizations can enhance customer engagement, satisfaction, and retention, ultimately driving business growth and profitability.