From Churn to Loyalty: A Case Study in Using Business Analytics to Improve Customer Retention

Introduction

Industry: Financial Services  

Company: Asset Management Company

Company Description

The company is one of the leading Asset Management Companies of Pakistan managing USD 800 million in investment solutions like open-ended mutual funds, pension funds, and investment advisory portfolios.

Business Objective

The business objective of improving customer retention involves identifying strategies and interventions to reduce churn and increase customer loyalty. This can be achieved through the use of data analytics to analyze customer data and identify patterns and trends, as well as developing targeted strategies and interventions to address identified issues and improve customer satisfaction. This can help to drive business growth and success by maximizing the value of existing customer relationships.

Business Challenge

The company was experiencing the risk of increasing rate of customer dormancy, therefore it was critical for the company to retain existing individual investors and take preventative measures to limit investor churn rates. Further to develop strategies to reactivate the 45% of inactive customers.

In this respect, the company commissioned the BI Team to predict the following data so that implementation of solutions to mitigate the concentration risk can take place beforehand:

  • Likelihood of a customer or group of customers discontinuing their business relationship with the company in future
  • Estimation of expected revenue based on prediction of future churn rates
  • Individual investors to be targeted for retention activities who are likely to discontinue relationship with the company

Available Data Points

The following key investor data points were available for analysis captured in the core system at the time of investor account opening:

Unique Identifiers:

  • Account number
  • Unique identification number

Investor Details:

  • Customer Name
  • Gender, Age, Occupation
  • Client Type
  • City Name
  • Country Name

Investment Details:

  • Product(s)
  • Sub Products
  • Amount
  • Transactions conducted

Services Availed:

  • Value-added services activation status

Data Analysis for Key Insights

The BI team used the published data sources scheduled for daily update compiling data from multiple database tables accessible for big data analysis using data warehousing (Oracle SQL Server) and analytical tools (Tableau Server and Tableau Desktop).

The analysis of key investors’ data points revealed that;

  • Majority of customers who discontinued their relationship with the company made a life time investment starting from Rs. 5,000 up to Rs. 500,000
  • The first products they invested their funds in were Asset Allocation, Income & Money Market
  • They only invested in single fund and never explored other products for investment
  • Most of them never availed value added services like digital services, debit card, e-transactions
  • They remained active by doing transactions for less than a year and then redeemed their investments

The insights were shared in a formal report with key stakeholders in the company for taking timely preventive actions.

Solutions

On the basis of the above insights, the key stakeholders of the company were able to develop and implement strategies for customer retention which involved implementing the following solutions: 

  • An independent consultancy firm was engaged to perform detailed customer survey analysis for further retention activities
  • Personalized services were offered to investors for improving their experience while using the company services and strengthening the bond with the company brand
  • A proactive approach was followed for customer service to eliminate investors’ problems earlier on
  • A formal process was developed for obtaining regular customer feedback and sharing of that information with relevant stakeholders
  • Social media platforms were utilized to build relationships with investors and to provide them with an informal medium for sharing experiences
  • Efforts were made to become the customers’ trusted advisor to gain trust and build loyalty
  • Robust customer support and customer awareness and referral programs were offered
  • Endeavors to establish a divide between the company and its competitors were taken

Results

The ability to predict that a particular customer is at a high risk of churning, while there is still time to do something about it, represents a huge additional potential revenue source for every business. Besides the direct loss of revenue that results from a customer abandoning the business, the costs of initially acquiring that customer may not have already been covered by the customer’s spending to date. (In other words, acquiring that customer may have actually been a losing investment.) Furthermore, it is always more difficult and expensive to acquire a new customer than it is to retain a current paying customer.

As a result of this activity, the company was able to achieve the following outcomes:

  • Cross-selling of the products increased from 20% to 45% over the period of 1 year
  • Based on new data-driven strategy, additional 25% of the existing investors invested in other products, thereby increasing the retention and profitability over the period
  • The new marketing strategy helped in increasing the value-added services activation ratio from 15% to 55%, which also enhanced the operational efficiency, as 67% of the transaction were performed using digital channels (VAS)
  • Customers who were using the value-added services of the organization, their dormancy ratio was 50% lower than those who were not using the services

Key Highlights of the Analysis