The document segments over 1.3 million customers into distinct groups for targeted marketing. Overall, 3 macro segments ("Aspirers", "Pragmatic", "Affluent") and 10 micro segments are identified. Key variables like income, age, occupation are considered. For each segment, profiles detailing demographics, transactions, product holdings are provided. Decision rules are also derived to segment new customers based on important variables like income, age. The document concludes with segmentation analyses for specific loan products like two-wheelers, personal loans, consumer durables.

1. CUSTOMER SEGMENTATION By
Tuhin Chattopadhyay, Ph.D.

2. 2
1.Business & Research Objectives
2.Executive Summary
3.Analytics Approach - Overview
4.Overall & Product Specific Segmentation
5.Decision Tree and Decision Rules
6.Appendix
Two Wheeler Loan Segment
Personal Loan Segment
Consumer Durable Loan Segment
Personal Loan Cross – Sell
Product and Overall Segments Mapping
Table of Contents

3. 3
BUSINESS & RESEARCH OBJECTIVES
Segment the customers into unique segments to enable targeted
marketing activities.Business
Objective
•Segment the customers into unique clusters.
•Segmentation to be done for all customers of the client as well
as within each product category.
•Provide distinct segments of customers along with their profile.
Research
Objectives
Objective – Agent
Profiling

4. 4
Executive Summary
• Segmentation done for 1.31 million customers
• Demographic and Transactional variables considered based on business relevance and data availability
• Variable transformation, outlier treatment and missing value imputation done based on requirement
• Profiles of macro and micro segments
• Map products purchased by each of the segments
• Decision Rules to Segment New Customers
Key Takeaways
Misclassification Error through
Discriminant analysis
Model Validation
Agglomerative Hierarchical
Clustering Methods & K-Means
clustering used in tandem.
Statistical Modelling
Derive key variables and build rules
to segment new customers
Decision Tree
Output
Overall Segmentation

5. 5
ANALYTICAL APPROACH - OVERVIEW
Interpreting the
Characteristics of the
segment based on
modelling output
Segment Profile
Statistical Modelling,
Evaluation & Profiling
Discriminant analysis
Misclassification Error
Validation Techniques
Agglomerative
Hierarchical Clustering
Method (Wards) & K-
Means clustering in
tandem.
Model Development
Age, Education, #
Children, Work
Experience, Gender,
Marital Status,
Occupation, Current
Province, Income
Descriptive Analytics
and Pattern
Recognition
Variables Considered -
Demographic
Exploratory Data
Analysis
Data Understanding
Loan Amount, EMI,
Interest Rate, Tenure, #
of Contracts, DPD, SBV
Bucket (G1, G2, G3, G4
& G5), Sales Channel,
Interest Amount
Variables Considered -
Transactional
Data Preparation
Data Set Creation
Created 5 data sets for
modelling –
• Overall Customer
base
• Two Wheeler Loan
• Consumer Durable
Loan
• Personal Loan
• Cross Sell & Up Sell
Variables
Transformation
• Education in Years
• Real Income
Data considered for all
active customers from
1st January 2014 till
31st August 2015
Time Period
• In case of multiple
loans the most recent
contract considered
• Closed contracts
considered in cases
where customer has
not taken an
additional loan
• Separate analysis is
done for charged off
customers
Data Preparation

6. Customer Segmentation

7. 7
Overall Customer Base Segmentation
Total Customers segmented: 1,314,582
Aspirers
434,802 (33.1%)
Desperate
275,274 (63.3%)
Mature
83,295 (19.16%)
Successful
76,233 (17.53%)
Pragmatic
358,771 (27.3%)
Wise
144,947 (40.4%)
Accumulator
213,824 (59.6%)
Affluent
521,009 (39.6%)
Homogeneous
Segment
Note – The three macro and five micro segments have
been identified after multiple iterations, to ensure that
each segments are unique.

8. 8
Product Mapping – Aspirers Segment
Aspirers
434,802 (33.1%)
Desperate
275,274 (63.3%)
Mature
83,295 (19.16%)
Successful
76,233 (17.53%)
Product Category No of Customers
Consumer Durable 272907 (99.14%)
Product Category No of Customers
Consumer Durable 59193 (71.06%)
Two Wheeler 13780 (16.54%)
PL New-to-bank 6547 (7.86%)
PL X-sell and Top-up 3775 (5.53%)
Product Category No of Customers
Two Wheeler 48978 (64.25%)
PL New-to-bank 11616 (15.24%)
Consumer Durable 7891 (10.35%)
PL X-sell and Top-up 7748 (10.16%)

9. 9
Pragmatic
358,771 (27.3%)
Wise
144,947 (40.4%)
Accumulator
213,824 (59.6%)
Product Mapping – Pragmatic Segment
Product Category No of Customers
Consumer Durable 106470 (74.45%)
Two Wheeler 18215 9 (12.57%)
PL New-to-bank 16604 (11.46%)
PL X-sell and Top-up 3658 (2.52%)
Product Category No of Customers
PL New-to-bank 74029 (34.62%)
Two Wheeler 52319 (24.47%)
PL X-sell and Top-up 49248 (23.03%)
Consumer Durable 38338 (17.88%)

10. 10
Product Mapping – Affluent Segment
Affluent
521,009 (39.6%)
Homogeneous Segment
Product Category No of Customers
PL New-to-bank 314659 (60.39%)
PL X-sell and Top-up 166828 (32.02%)
Two Wheeler 32873 (6.31%)
Consumer Durable 6649 (1.28%)

11. 11
Overall Segmentation Dashboard
• The “Aspirers” segment is home to the
youngest customers with the lowest
income. Active in their finances and
comfortable making tough financial
decisions as shown with the high
interest rate.
• “Pragmatic” segment comprises the
oldest group of customers. Low
interest & below average tenure show
a thought through approach to
financing
• The “Affluent” segment has the
highest income consuming the highest
amount of loan and with the longest
tenure.

12. 12
Overall Segmentation Dashboard
Occupation
Marital Status
• Highest number of students
within “Aspirers” segment.
• Majority of the “Pragmatic”
segment are self employed
with a conservative approach
to consume loans which is
evident through loan amounts,
interest rate and tenure
• “Affluent” group has the largest
group of customers who hold a
job (Blue Collar, White Collar)
making them a secure
segment. They also have the
least number of students
31.18%
42.70%
18.58%
27.02%
25.75%
22.47%
18.71%
11.58%
9.17%
15.24%
12.02%
23.44%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
Aspirer Pragmatic Affluent
SELF-EMPLOYED BLUE-COLLAR STUDENT WHITE-COLLAR
52.30%
76.16%
63.23%
39.38%
10.86%
29.21%
0%
10%
20%
30%
40%
50%
60%
70%
80%
Aspirer Pragmatic Affluent
Married Single

13. 13
ASPIRERS
• The “Desperate” segment forms 63%
of the “Aspirer” group. This group has
the highest interest rates and lowest
incomes amongst “Aspirers”
• Interest amount paid by the
“Successful” segment is 3.6 and 4
times higher than the other micro
segments

14. 14
PRAGMATIC
• “Accumulator” segment is the oldest
segment among all the micro segments
• Loan amount issued to “Accumulator” is
1.86 times that of the “Wise” segment”
despite having an significantly higher
interest rate.
• Given that the EMI to Income ration for
“Accumulator” and “Wise” segment is 23%,
and 18% respectively, they are good
candidates for cross sell / up-sell.

15. 15
PRAGMATIC
34%
49%
28%
24%
16%
9%
15%
10%
0%
10%
20%
30%
40%
50%
60%
Wise Accumulator
SELF-EMPLOYED BLUE-COLLAR STUDENT WHITE-COLLAR
• The “Accumulator” segment has the
highest number of married customers
at 84%. Well settled with family makes
them an attractive segment for
additional loans.
• 49% of “Accumulators” are self
employed indicating the need for large
loans.
• High Education levels among “Wise”
segment shows their discretion in
availing loans.
Occupation
65%
84%
23%
3%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
Wise Accumulator
Married Single
Marital Status

16. Rules for Segmenting New Customers

17. 17
Decision Tree - Overview
What is a Decision Tree?
• Decision tree is a type of supervised learning algorithm (having a pre-
defined target variable) that is mostly used in classification problems. It
works for both categorical and continuous input and output variables.
• Decision trees generate the importance of variables for classification.
These variables are used to define rules that will help classify customers.
• In this technique, we split the population or sample into two or more
homogeneous sets (or sub-populations) based on most significant splitter
/ differentiator in input variables.
• The objective is to understand in which cluster a new customer will belong to.
• The 6 clusters viz. Desperate, Mature, Successful, Wise, Accumulator and Affluent are considered as the
levels of the dependent variable.
• The demographic variables like age, income, education, number of children, work experience, occupation
etc. as the independent variables.
Application of Decision Tree for New Customer Profiling
Order of
Importance
Variable
First Income
Second Age
Third Work Experience
Fourth # Children
Fifth Occupation
Sixth Education (Yrs)

18. 18
Indicative Rules for Segmenting New Customers
Aspirers
Desperate
Mature
Successful
IF INCOME>=2,000,000 INCOME<= 5,122,277 AND AGE >=
27 AND AGE <= 31
IF INCOME>=5,122,278 TO INCOME <=6,049,832 AND
AGE>=24 TO AGE <=29
IF INCOME>= 6,049,833 TO INCOME <=7,000,000 AND
AGE>=22 TO AGE<=28
Pragmatic
Wise
Accumulator
IF INCOME>=5,080,561 TO INCOME <= 6,448,612 AND
AGE>=31 TO AGE<=40
IF INCOME>= 6,066,263 TO INCOME<= 7,353,570 AND
AGE >= 41 TO AGE <= 65
Homogenous
Segment
IF INCOME >= 6,511,105 AND AGE >= 29 TO AND
AGE <= 34Affluent
Note: Decision Tree throws number of rules for each of the segments. The indicative rules are
presented here. The exhaustive list are provided in the Technical Document.

19. Thank you !
19

20. Appendix

21. Product Wise Customer Segmentation

22. 22
Product Specific Segmentation
Two Wheeler
(215260, 16.37%)
Young Turks
(46320, 21.52%)
Diligent
(84402, 39.21%)
Satisfied
Entrepreneurs
(28825, 13.39%)
Risky Seniors
(55713, 25.88%)
CDL
(560920, 42.66%)
High Spenders
(283230, 50.49%)
Affluent Young
(159064, 28.36%)
Status Seekers
(118626, 21.15%)
Personal Loan
(466161, 35.46%)
High Earning
Opportunists
(132517, 28.43%)
Promising
(202121, 43.36%)
Middle Aged
Conservatives
131523, 28.21%)
Top up & Cross
Sell
(235634, 17.9%)
High Rollers
(66050, 28.03%)
Up and Coming
(111696, 47.40%)
Traditionalists
(57888, 24.57%)
Note – The individual product level segments have been
identified after multiple iterations, to ensure that each
segments are unique.

23. Two Wheeler Loan Segment

24. 24
TW Segment Profile - Overview
• “Young Turks” segment is a target for
marketing activities as this is one of
the youngest clusters with the
second highest average income.
• “Diligent” have the highest EMI to
income ratio leading to the lowest
disposable income within the TW
category.
• “Satisfied Entrepreneurs” have the
highest disposable income within the
Two Wheeler product category.
• The “Risky Seniors” and “Diligent”
have similar Income and Loan
appetite even though their average
age is 42.78 and 26.67 respectively.
Similarly “Satisfied Entrepreneurs”
and “Young Turks” have similar
transaction history given their
average age is 42.9 and 27.11
respectively
2. Diligent

25. 25
Occupation
TW Segment Profile
Province
• Over 50% of the older segments (Satisfied
Entrepreneurs & Risky Seniors) are self
employed compared to the younger segments
who hold blue / white collar jobs
• “Young Turks” and “Satisfied Entrepreneurs”
who have the highest income are primarily from
Ho Chi Minh city compared to the “Diligent”
and “Risky Seniors” who are from Binh Duong
• Over 80% of “Satisfied Entrepreneurs” and
“Risky Seniors” are married with children.
Marital Status
36%
34%
55%
52%
25% 24% 24% 24%
16% 18%
9%
15% 13%
8%
0%
10%
20%
30%
40%
50%
60%
Young Turks Diligent Satisfied
Entrepreneurs
Risky Seniors
SELF-EMPLOYED BLUE-COLLAR STUDENT WHITE-COLLAR
11%
9%
10%
7%
5%
14%
4%
10%
7% 7% 7% 6%
0%
2%
4%
6%
8%
10%
12%
14%
16%
Young Turks Diligent Satisfied
Entrepreneurs
Risky Seniors
Ho Chi Minh City Binh Duong Dong Nai
52% 53%
85% 86%
41% 40%
5% 5%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Young Turks Diligent Satisfied
Entrepreneurs
Risky Seniors
Married Single

26. Personal Loan Segment

27. 27
Segment Profile - Overview
• At 21% the “High Earning
Opportunists” have the lowest EMI
to Income ratio - High disposable
income.
• “High Earning Opportunists”
consume the largest loans amongst
the PL group with a significantly
larger tenure.
High Earning
Opportunist
Promising Middle Aged
Conservatives
High Earning
Opportunist
Promising Middle Aged
Conservatives

28. 75%
56%
75%
15%
38%
13%
0%
10%
20%
30%
40%
50%
60%
70%
80%
High Earning Opportunist Promising Middle Aged Conservatives
Married Single
28
Segment Profile - Overview
Occupation
• 75% of the “High Earning
Opportunists” segment hold a job
where as only 20% are self employed
• 90% of the “Promising” Segment
hold jobs where as only 5 % is self
employed
• The % of students within all the
segments is low indicating that most
of the customers within the Personal
Loan category are earning and not
dependent on others
Marital Status
20%
5%
31%
25%
22% 21%
5% 4%
5%
23%
31%
20%
0%
5%
10%
15%
20%
25%
30%
35%
High Earning Opportunist Promising Middle Aged Conservatives
SELF-EMPLOYED BLUE-COLLAR STUDENT WHITE-COLLAR

29. Consumer Durable Loan Segment

30. 30
Segment Profile - Overview
• “High Spenders” segment have the
highest interest rate in the entire
customer universe. This coupled
with
• The “Affluent Young” segment
enjoys a significantly lower
interest rate (30.6 %) when
compared to the other two
segments, despite sharing a
comparable income.
• Loans availed by “Affluent Young”
are higher by over 50% compared
to “High Spenders” and “Middle
Aged Conservatives”
Affluent
Young
High
Spenders
Status
Seekers
Affluent
Young
High
Spenders
Status
Seekers

31. 31
Segment Profile
• 81% of the “Status Seekers” segment
are married compared to the “High
Spenders” and “Affluent Young”
where the percentage is significantly
lower.
• “Status Seekers” being the oldest
group, also have the highest work
experience.
Marital Status
48%
56%
81%
45%
33%
3%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
High Spenders Affluent Young Status Seekers
Married Single

32. Personal Loan – Top up & Cross Sell Segment

33. 33
Segment Profile - Overview
• Loan amount of “High Rollers” twice
that of “Up and Coming” and
“Traditionalists”
• The “Traditionalists” are 13.7 years
older than “Up and Coming” and
8.7years older than the “High
Rollers”
High
Rollers
Up and
Coming
Traditio
nalists
High
Rollers
Up and
Coming
Traditio
nalists

34. 19%
48%
35%
21% 22% 23%
13% 13%
19%
22%
10%
14%
0%
10%
20%
30%
40%
50%
60%
High Rollers Traditionalists Up and Coming
SELF-EMPLOYED BLUE-COLLAR STUDENT WHITE-COLLAR
34
Current Region
Occupation
• The younger groups, “High Rollers” and “Up and
Coming” hold Blue / White collar jobs where are the
“Traditionalists” are self employed.
• The top three regions for all the segments is Ho Chi
Minh City, Binh Duong and Dong Nai
• 86% of the “Traditionalists” segment is married with
an average of almost 2 children
Segment Profile - Overview
Marital Status 24%
17%
14%
16%
9%
8%
11%
7% 6%
0%
5%
10%
15%
20%
25%
High Rollers Traditionalists Up and Coming
Ho Chi Minh City Binh Duong Dong Nai
73%
86%
57%
19%
5%
34%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
High Rollers Traditionalists Up and Coming
Married Single

35. Mapping of Product Segments to Overall Segments

36. DESPERATE

37. MATURE

38. SUCCESSFUL

39. WISE

40. ACCUMULATOR

41. AFFLUENT

42. 42
Charge Off Customer Cluster
97%
3%
Charged Off Status
Non-Charged Off Charged Off
26%
28%
19%
16%
10%
Current Region
Centre
Mekong
North
South
East
Charged off Customers by Current
region
There are 3% charged off customers. Out of that
54% are from South.
48%
33%
7%
13%
Product Group
TW
PL X-sell and Top-up
PL New-to-bank
CDL
Charged off Customers
by Product Group
There are 3% charged off customers. Out of
that 48% are CDL and 33% are PL (81%
together).