Higher product uptake
Optimized use of marketing resources
Refined customer experience strategy
Refined recommendation outputs
The challenge
Enhancing customer engagement with AI-driven product recommendations
A 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
Identified likely top choices
Used deep learning
Data insights
Integrated 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
Driving marketing efficiency through data-driven forecasting
Higher sales
Increased adoption support
Improved user interaction
Improved conversion outcomes
Smarter marketing
Refined targeting strategies
Stronger alignment between spend and value
Reduced cost exposure
Data-driven growth
Improved business intelligence
Optimized approach to segmenting audiences
Personalized offers
Looking ahead
Continuous optimization
Enhance AI models for better accuracy
Personalized engagement
Expand tailored recommendations
Scalable integration
Deploy across more banking channels
