Success Story Financial Services
Asset Management Company Analyzes Churn to Retain 50% of Passive Customers
Churn is a significant concern for companies, where an increase in rate indicates problems that need immediate attention.
Companies must consistently monitor churn rates to identify which customers are likely to drop off and why. Only accurate and relevant analysis can deliver insights into churn rates. The customer, an asset management company, wanted to analyze their churn rate to come up with a robust intervention strategy.
Formulating a strategy for geo-analysis at state, segment, and high net worth level, Apexon came up with a solution that involved:
An in-depth analysis of the customer’s processes revealed various challenges:
Differentiate Churn Rates A need to differentiate likely active and passive churn rates, as well as pinpoint when it will likely occur
High Churn Rates High churn rate of ~8% per annum for high net worth individuals
Intervention Strategy A need to formulate an effective intervention strategy
Lack of Engagement Passive churn resulting from a lack of engagement
Apexon utilized our capabilities in data science, R, and open-source library OpenNLP to devise a solution where we:
Variable importance model Used variable importance model at individual investor level to identify the levers that impacted the decision to churn
Customer Life-Time Value Provided the life-time value of the customer based on past transactions, related accounts, etc.
Time-Series Regression Model Built a time-series regression model to predict the timeframe within which churn may happen
Multi-variate model Created a multi-variate model using Xgboost algorithm to identify the probable churns
