2. Problem Statement
โข Redemption of invested funds by customers is a
scenario in which capital market players such as
Securities firms or Mutual Funds have to prepare for
and face on a daily basis.India Mutual fund Industry
size is $14 Trillion with close to 80% volume traded in
Open Ended Fund according to SEBI Report.
3. Objective
โข To study Mutual fund market life cycle of a open-
ended fund, and based on their characteristics and
transaction history predict Redemption in
future(Current Model:-90 days)
โข Create Segmentation of Investors on basis of their
Behavioural characteristics and Demography
5. Analytics objective
Customer may Redeem due to following
โข Lower Returns in portfolio
โข High Risk due to volatility of market
โข Poor Fund services
โข Higher capital taxes and change in interest rates
6. Data Available
Historical Transaction Data
โข Transaction type, date, Mutual Fund plan and Schemes
โข No of units purchased, amount, NAV, Price
โข Unique Identifier:- ClientCode, CommonClientCode
Customer Data
โข Demographic details
โข Account Opening Date, balance, ledger details
7. Analytics Approach
โข Data Cleansing:
โ Removing outliers
โ Removing usage records after Redemption.
โข Data Integration:
โ For each ID combine data for client, ledger
โ Merge dataset with Sensex and NIFTY historical data
โ Apply Redemption date if customer has Redeem fund
โ Calculate derived variables to capture user behaviour
10. Derived Variables
โข Scheme Returns
โข Asset ratio
โข Beta
โข R Squared
โข Scheme Risk
โข Market returns
โข Market Risk
โข Vintage
โข Broker profit
โข SIP Tenure
โข STP Tenure
โข Transaction count
โข Age of customer
โข Switch Out Ratio
โข Mean NAV
Total Variables calculated: 23
Variables considered in Model: 10
11. Building Model
Eight Year Transaction Data
(2007-15)
60 % Training Data
40 % Testing Data
Validate Data
Total Available Data
Training Data
Testing Data
Validation Data(Aug-OCT)
12. Logistic Regression
๏ง To find predictive variables
๏ง To predict redemption of fund by
the customer
0
1
No Redemption
Redemption
Model: Setup
17. Confusion Matrix
Predicted
Actual 0 1 Sum
0 320060 26480 346540
1 11705 51064 62769
Sum 331765 77544 409309
Predicted
Actual 0 1 Sum
0 18272 524 18796
1 1037 3879 4916
Sum 19309 4403 23712
Test Data Validation Data
22. Future Work
To optimize Mutual fund sales ,reduce costs, customer
acquisition and to enhance customer satisfaction and
retention using uplift Modelling technique.
Focus on customers who will purchase after price
reduction
24. Hypothesis
Customers
with only
EQ
Customers
with both EQ
and MF
Characteristics of cross-sell
customers
Characteristics of customers
with only EQ
The people who bought only EQ but they have similar characteristics (in terms of
variables) to the people who bought both EQ and MF, are more likely to buy MF also.
25. Age frequency
maximum between
33 to 43
Approach-1
Based on Demographics of customer
Vintage frequency
maximum between
4 to 6
Vintage
Graduation has the highest
frequency amongst education
Maharashtra has the
highest frequency
amongst all states
State
26. Approach-2
Based on the activity of customers
Table 1 for customers
with Equity or Both EQ
and MF
Table 2 for customers
with Both EQ and MF
๐ธ๐๐ข๐๐ก๐ฆ ๐๐๐ก๐๐ฃ๐๐ก๐ฆ ๐ ๐ =
๐=1
๐
๐ฅ๐
๐
๐ผ๐๐๐๐ก๐๐ฃ๐๐ก๐ฆ ๐๐๐ก๐๐ ๐ ๐ =
๐=1
๐
๐ฆ๐
๐
From customers of table 1 find distance of each customer from the
centroid ๐ ๐.
Based on this distance rank the customers from minimum distance to
maximum distance.