A Top-Tier European Bank Increased Product Cross-Sell by 75% Using AI-Powered Segmentation and Recommendation Engine

Challenges

The bank lacked a robust mechanism to segment B2B customers based on income, product usage, and Lifetime Value (LTV). This led to:

• Limited ability to personalize offerings and optimize marketing strategies.
• Missed opportunities to detect shifts in customer behavior.
• Low cross-sell rates and delayed product recommendations for high-value clients.

Solution

Coforge implemented an AI-powered segmentation and recommendation engine to optimize product targeting and boost customer engagement:

• Applied K-Means clustering on financial and demographic features for precise customer segmentation.
• Integrated external agency datasets (registrations, credit portfolios) with internal variables for richer segmentation insights.
• Created cohorts and buckets based on clustering patterns to identify potential product match opportunities.
• Mapped products across cohorts and applied collaborative filtering to detect common purchase trends.
• Developed a multi-class recommender algorithm to suggest the next best offer based on customer profiles and product performance.
• Conducted A/B testing to measure the uplift and validate performance against key business KPIs.

The Impact

• 15% uplift in customer movement to higher-value segments.
• 75% increase in product cross-sell to customers.
• Faster campaign execution through a streamlined batch data segmentation process.