Churn Model To Identify Attrition Risk Guide Interventions And Save USD 100MM

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 churn 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.

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Churn Model To Identify Attrition Risk Guide Interventions And Save USD 100MM

Business Objective

Challenges

Solution Methodology

Business Impact

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