Data-Driven Next-Best-Offer Recommendation System for Indian Retail Bank Customers

Objective: To design and implement a recommendation engine that identifies personalized cross-sell and next-best-offer opportunities for bank customers using transactional and behavioral data.

Problem & Context: Indian retail banks face challenges in maximizing customer value and product penetration due to low cross-sell rates and generic marketing campaigns. With increasing competition from fintechs and digital-first banks, identifying the right product to offer the right customer at the right time is crucial for retention and growth.

Approach & Methodology: The project involves collecting and analyzing anonymized customer data (demographics, transaction history, product holdings) from a simulated or open-source dataset (such as Kaggle’s Bank Marketing or synthetic Indian datasets). The team will apply data mining, segmentation, and machine learning techniques to build a recommendation model for next-best-offers and cross-sell targeting, tailored to the Indian banking context (e.g., savings, credit cards, loans, insurance).

Deliverables & Analysis: Key deliverables include a working prototype of the recommendation system (dashboard or API), detailed analysis of model performance (precision, recall, ROI impact), and documentation on business logic. The team will also provide a customer journey map and marketing action recommendations based on insights.

Business Impact & Decision: The solution will enable Indian banks to improve campaign conversion rates, increase wallet share, and enhance customer satisfaction by making data-driven, personalized product recommendations. It informs marketing and product teams on actionable cross-sell strategies for targeted segments.