Identify major customer events using analytics
The Big Picture
A leading bank had the highest share of young adults in the 18 to 26 years age group, but the proportion tails off in the subsequent older age groups. Given that customers’ profitability peaks in the older age groups, with 35-45 age group customers having the highest customer lifetime value (CLV), it was a cause of concern and needed an investigation, as well as a significant opportunity to acquire and hold on to more profitable customer segments.
The company decided to create a unified view of the customer to improve customer service, and identify and determine the impact and time duration between events of a customer journey to retain customers. Through an exhaustive review of the bank’s requirements, a comprehensive list of business hypotheses was drawn from different product verticals, and a customer base was identified to be analyzed. Data of varying sources was integrated into a single view of the customer.
Modeling occurred using Java, and the sequences were mined for events that would result in an attrition outcome. Upon identifying likely attrition events in the customer journey, the customers were profiled month-to-month on key KPIs to identify specific behaviors prior to attrition.
Although the sequence pattern mining analysis pointed towards ‘fee’ as a driver of attrition, the bank was not convinced enough to take the analysis to production. The client, though, was highly satisfied with the upskilling of its internal team through monthly meetings and sessions and was acknowledged as such in the final meeting.