FROM CONFUSION TO CLARITY: A CASE STUDY IN USING BUSINESS ANALYTICS TO UNDERSTAND INVESTMENT WITHDRAWALS

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 identifying redemption patterns among products and customers involves using data analytics to analyze data on customer purchases and redemption behaviors in order to identify trends and relationships. This can help to inform marketing and sales strategies, as well as product development and pricing decisions, in order to optimize customer engagement and drive business growth and success.

Business Challenge

The company faced challenges in maintaining customers’ investments in their accounts for the long term. The average redemption-to-investment ratio per investor showed an increasing trend. To identify the root cause of this increase in redemption to investment ratio, the company commissioned the BI team to analyze customers’ transaction data and identify patterns in redemption transactions to determine the reasons of the high ratio.

Answers for the following key business questions were to be determined:

1. What is the change in product composition of assets under management (AUM) over the last 4 years?

2. Can the change in AUM (Equity) be attributed to market growth or sales growth?

3. Identifying the customers with high redemptions?

4. What are the transaction patterns over the years?

5. What are the reasons of high redemptions?

6. Is the high redemption due to charging of Front End Fees (Sales Load)?

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, registration of complaints or processing of transactions:

Unique Identifiers:

  •  Account number
  •  Unique identification number

Investor Details:

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

Transaction Details:

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

Services Availed:

  • Value-added services activation status

Complaints Data:

  • Status of Complaints and resolution TAT


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 (MS SQL Server) and analytical tools (Tableau Server and Tableau Desktop).

A)   Analysis Approach

The following points were analyzed step wise:

  •  AUM movement over the last 4 years
  •  AUM movement during the fiscal year
  •  Profiling of customers and customer dormancy analysis
  •  Redemption transactions analysis of retails clients
  •  Transaction analysis over the period under review

Further, for profiling of retail customers with high redemptions during the period, the following approach was followed;

i)    The customers were divided in two groups Existing Customers (who opened accounts before the fiscal year start Jul 20XX) and New Customers (who opened accounts after the fiscal year start Jul 20XX).

  •  The movement in assets under management (AUM) of both customer groups was analyzed
  •  The net AUM of existing customer reduced while the new customers added funds in the total AUM.

ii) The existing customers were further classified into two groups:

  • Positive AUM Movement (Net Sales)
  • Negative AUM Movement (Net Redemption)

iii. Customer profiling of the two groups was performed based on the following criteria;

  • Personal traits (Age, Gender, Marital Status, Occupation, City)
  • Account level (Number of Accounts, Funds, Dormancy Status, AUM Bands, Sales Load)
  • Service level (Complaint resolution, Value Added Services, Insurance)

B)   Key Insights

The analysis of key investors’ data points revealed that;

1. Change in AUM over the last 4 years

  •  During the last 4 years, the assets under management (AUM) increased by 65%
  •  The increase was attributed 105% to “Sales Growth” and -5% to “Market Growth”
  •  Retail Clients contributed 38% to the closing AUM, while the Corporate and Trust Clients contributed 61% to the total Closing AUM

2. Change in AUM (Equity Funds)

  •  During the last 4 years, the assets under management (AUM) of Equity Related Funds showed a negative growth of (32%)
  •  The decrease in Equity Related Funds assets under management (AUM) over the 4 years, was attributed 73% to “Negative Sales Growth” and 27% to “Negative Market Growth”

3. Change in AUM over the fiscal year

  •  During the fiscal year, the assets under management (AUM) increased by 26%
  •  The growth in assets under management (AUM) was attributed 86% to “Sales Growth” and 14% to “Market Growth”

4. Profiling of Existing Customers

Customer profiling revealed that the company can classify its customers in the following types based on changes in assets under management (AUM);

Customer with Positive Change

– Such customers comprised of 40% and their AUM increased by 49% over the fiscal year (Jul-Jun)

Customers with Negative Change

– Such customers comprised of 22% and their AUM decreased by 71% over the fiscal year (Jul-Jun)

Neutral Customers

– Such customers comprised of 3% and their AUM increased minimally by 4% over the fiscal year (Jul-Jun)

Customer With No Change in AUM

– Such customers comprised of 35% and they had zero assets under management during the fiscal year (Jul-Jun)

5. Redemption and Transaction Analysis

  •  Transaction analysis revealed that retail clients had the highest redemption to investment ratio of 97% as compared to the company average of 79%.
  •  Among product categories, average redemption to investment ratio in Money Market fund was the highest i.e. 105% over seven years period
  •  On average, amount wise composition of redemption transactions was the highest in Income funds i.e. 43%
  •  Transactions without sales load were more prone to redemption and had redemption to investment ratio of 84% as compared to transactions with load having a ratio of 65%
  •  Existing clients had the higher redemption to investment ratios as compared to new clients

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

Solutions

Based on the above insights, the key stakeholders of the company understood the criticality of the situation and decided to hire external consultant to conduct thorough interviews with customers with high redemption to investment ratio and low assets under management for obtaining their feedback to improve the company’s product and services.

In this regard, an external consultant was hired and the survey was conducted through face to face and telephonic interviews involving quantitative and qualitative questions. The subjects were sampled from the data of three big cities from groups of customers with both low and high redemption to investments ratio.

The survey conducted had the following objectives:

  •  Understanding customer behavior about savings and investment
  •  Identification of avenues consumers use for savings
  •  Determining understanding level of customers about products and services of the company
  •  Identification of reasons for customer motivation to invest in mutual funds
  •  Obtaining views of customers about the company and its service


Results

As a result of this activity, the company was able to target its efforts toward customers who were prone to redeeming funds based on data analysis. The survey results were utilized to set the direction of the strategy to better cater to the needs of the customers.

Key Highlights of the Analysis