'Fee' as attrition driver
Client valued upskilling
Satisfaction acknowledged
The challenge
Enhancing customer retention for higher CLV
A leading bank had a dominant share of young adults aged 18 to 26, but the proportion declined in older age groups. As customer profitability peaks in the 35-45 age group, where customer lifetime value (CLV) is highest, this trend raised concerns and highlighted a significant opportunity to acquire and retain more profitable customer segments.
Key challenges
Maximizing customer profitability, particularly in the 35-45 age group
Declining proportion of customers in older age groups
Addressing customer retention to capture higher CLV
The solution
Customer insights and data integration
Unified customer view
Improved service
Identified key events
Focused on timely retention
Data integration and hypothesis
Integrated data sources
Developed cross-vertical hypotheses
Analyzed target customers
Implementation approach
1
Modeling and sequence mining
Used Java for modeling
Analyzed retention impact
Mined attrition events
2
Customer profiling
Profiled customers
Tracked attrition behaviors
Identified triggers
3
Event detection and retention
Identified attrition events
Focused on behaviors
Applied retention strategies
The impact
Actionable insights and client empowerment
Client upskilling
Improved team skills
Monthly training
Acknowledged for development
Insights on attrition
Identified ‘fee’ as driver
Provided retention insights.
Improved behavior understanding
Analysis outcome
Strengthened analytics
Guided future strategies
