60% higher product uptake
Improved marketing ROI
Stronger customer engagement
Smarter recommendations
The challenge
Enhancing customer engagement with AI-driven product recommendations
A leading retail bank sought to improve customer engagement and satisfaction. Its existing analytical models for product recommendations needed better accuracy to incorporate crucial online and offline interactions. The goal was effective arbitration among multiple competing product offers, for better customer experiences and higher response rates.
Key challenges
Scope for better response rates and customer experience
Need for more accurate product propensity models
Need for key online and offline interaction data
Objective arbitration among competing offers
The solution
AI-powered personalization
AI recommendations
Developed AI recommender
Predicted top 3 choices
Used deep learning
Data insights
Integrated 4K+ attributes
Analyzed transactions
Created a unified view
Implementation approach
1
Data processing
Merged online and in-bank data
Enhanced decisions
Processed transactions
2
Model testing
Tested on select users
Validated accuracy
Refined predictions
3
Deployment
Integrated with scoring
Centrally deployed
Enabled real-time use
The impact
Maximizing marketing ROI with predictive analytics
Higher sales
60% uptake boost
Better engagement
More conversions
Smarter marketing
Improved targeting
Higher ROI
Lower costs
Data-driven growth
Sharper insights
Better segmentation
Personalized offers
Looking ahead
Continuous optimization
Enhance AI models for better accuracy
Personalized engagement
Expand tailored recommendations
Scalable integration
Deploy across more banking channels
