Business Objective
Our client is a leading financial institution, offering banking, wealth management, and brokerage services to its customers.
Their most valuable customers have a larger contribution to the client’s business, so it is very important for the client to actively manage attrition, and prevent the top customers from leaving.
Challenges
- Identify the customers who are likely to leave within the next 30-45 days with special attention toward the valuable customers – identify the possible reasons for their attrition
- Although there was a scoring mechanism in place, the outputs were not satisfactory. So our challenge was to significantly improve the mechanism
- In this specific case, attrition was a rare event with monthly attrition numbers as low as <0.01%
- Deliver the analysis to the sales team in such a way that they can take specific actions for the identified customers.
Solution Methodology
- Linked the life events in a consumer’s life with attrition, and collated data pointing to those events from internal and external data sources
- Accommodated all the past transactions’ history and the point of contact information to create enhanced features predicting attrition
- Developed a comprehensive sampling framework to accommodate the need to predict attrition for the next 30-day window
- Used various text analytics algorithms to convert email, phone, and chat communications into features that capture early warning flags
- Developed an ensemble of models alongside business context and filters to identify attrition risk
- Developed an actionable plan that the sales reps could use to proactively manage attrition risk.
Business Impact
- Increased accuracy of existing model by more than 50%.
- Identified the at-risk valuable customers contributing more than USD 500MM to the client
- Potential savings worth USD 100MM identified by engaging 20% of the at-risk segment with minimal effort.
