1. / Asset Management Company Analyzes Churn to Retain 50% of Passive Customers
Overview

Churn is a significant concern for companies, where an increase in rate signals that there are problems that need immediate attention. Companies have to 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 and the reasons for churn.
The customer, an asset management company, wanted to analyze their churn rate to come up with a robust intervention strategy.

Problem

An in-depth analysis of the customer’s processes revealed various challenges.

  • High churn rate of ~8% per annum for high net worth individuals
  • Passive churn resulting from a lack of engagement
  • A need to differentiate likely active and passive churn rates, as well as pinpoint when it will likely occur
  • A need to formulate an effective intervention strategy
Solution

Apexon utilized our capabilities in data science and R and open source library OpenNLP to devise a solution where we:

  • Created a multi-variate model using Xgboost algorithm to identify the probable churns
  • Used variable importance model at individual investor level to identify the levers that impacted the decision to churn
  • Built a time-series regression model to predict the timeframe within which churn may happen
  • Provided the life-time value of the customer based on past transactions, related accounts, etc.
Impact

Formulating a strategy for geo-analysis at state, segment, and high net worth level, Apexon came up with a solution that involved:

  • Early identification of likely churn to enable proactive retention measures to trigger and retain 50% of passive customers
  • Targeted campaigns around high value customers
  • Improved CSAT

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