Coforge

Who We Are

About Us Newsroom Leadership Partners Locations Careers Awards & Recognitions ESG Learn more about Coforge
Coforge: Where AI engineering meets industry expertise.

Learn about our company, our vision and values, and the 45,000+ professionals enabling businesses to harness the power of AI.

Learn more about Coforge
Composable Enterprise

CASE STUDY

Driving Personalized Financial Offerings with AI-Powered Propensity Modeling

Industry

Banking & Financial Services

Our Contributions

Advanced Analytics, Machine Learning, Customer Personalization

Technologies

Machine Learning Models, Data Analytics Platforms

Online_Banking
Coforge enabled a financial services organization to enhance customer engagement and sales effectiveness by implementing an AI-driven product propensity modeling solution. The objective was to leverage customer data to predict product affinity and deliver personalized financial offerings.

By applying advanced machine learning techniques, Coforge developed a scalable solution that generated actionable insights into customer behavior. This enabled marketing and sales teams to deliver targeted campaigns, improve cross-sell outcomes, and strengthen customer relationships through data-driven personalization.


Phase 1
Business objective definition and data assessment
Phase 2
Model design and machine learning development
Phase 3
Propensity scoring and visualization enablement
Phase 4
Campaign activation and continuous optimization

Drag

Coin

The Challenge

The client sought to improve customer engagement and cross-sell effectiveness but lacked a data-driven mechanism to predict customer preferences and product affinity. Existing marketing approaches relied on broad segmentation and generic campaigns, resulting in low conversion rates and inefficient use of resources.

Customer data existed across multiple systems, making it difficult to generate a unified view of behavior and intent. Additionally, the absence of predictive analytics limited the organization’s ability to proactively identify high-probability sales opportunities.

The organization required a scalable, intelligent solution to analyze large volumes of customer data, generate actionable insights, and enable targeted, personalized engagement strategies.

Our Approach

AI-Driven Propensity Modeling

Developed advanced machine learning models to analyze customer behavior, transaction history, demographics, and product interactions to predict product affinity.

Propensity Scoring Engine

Generated dynamic propensity scores for each customer across multiple financial products, indicating the likelihood of near-term purchase or engagement.

Insight Visualization Layer

Delivered an intuitive visualization interface enabling business users to easily identify top product recommendations for each customer.

Campaign Activation & Personalization

Enabled marketing and sales teams to activate insights through targeted campaigns, personalized offers, and prioritized outreach based on predicted customer intent.

Continuous Model Optimization

Implemented ongoing model refinement to ensure predictions remain accurate and relevant as customer behavior evolves.

Partner / Technology Ecosystem

  • Machine Learning & Analytics Platforms 
  • Data Integration & Processing Frameworks 
  • Visualization & Reporting Tools

CiberSecurity

Impact to Date

+25–35%

Improvement in Cross-Sell Conversion Rates

+30%

Increase in Campaign Effectiveness

+20%

Improvement in Customer Retention

Reduced

Marketing Waste through Targeted Campaigns

Business Impact

  • Enhanced customer engagement through personalized product recommendations
  • Improved sales effectiveness by focusing on high-probability opportunities
  • Optimized marketing spend with targeted, data-driven campaigns
  • Strengthened customer relationships and long-term loyalty 
  • Enabled proactive decision-making through predictive analytics

Coforge transformed customer engagement from broad-based marketing to precise, data-driven personalization. By leveraging AI-powered propensity modeling, the organization now identifies high-value opportunities with greater accuracy, improves conversion outcomes, and delivers more relevant financial offerings, enabling sustainable revenue growth and stronger customer relationships at scale.