Customer Segmentation

CUSTOMER SEGMENTATION

By

Tuhin Chattopadhyay, Ph.D.

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

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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

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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

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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

Customer Segmentation

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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.

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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%)



Two Wheeler 13780 (16.54%)

PL New-to-bank 6547 (7.86%)

PL X-sell and Top-up 3775 (5.53%)


Two Wheeler 48978 (64.25%)

PL New-to-bank 11616 (15.24%)



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Pragmatic 358,771 (27.3%)

Wise 144,947 (40.4%)

Accumulator 213,824 (59.6%)

Product Mapping – Pragmatic Segment



Two Wheeler 18215 9 (12.57%)

PL New-to-bank 16604 (11.46%)



PL New-to-bank 74029 (34.62%)

Two Wheeler 52319 (24.47%)



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Product Mapping – Affluent Segment

Affluent 521,009 (39.6%)

Homogeneous Segment


PL New-to-bank 314659 (60.39%)


Two Wheeler 32873 (6.31%)


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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.

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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

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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

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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.

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PRAGMATIC

34%

49%

28% 24%

16%

9%

15%

10%

0%

10%

20%

30%

40%

50%

60%

Wise Accumulator


• 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

Rules for Segmenting New Customers

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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)

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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 <= 34 Affluent

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.

Thank you ! 19

Appendix

Product Wise Customer Segmentation

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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.

Two Wheeler Loan Segment

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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

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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 SatisfiedEntrepreneurs

Risky Seniors


11%

9% 10%

7%

5%

14%

4%

10%

7% 7% 7% 6%

0%

2%

4%

6%

8%

10%

12%

14%

16%


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%


Risky Seniors

Married Single

Personal Loan Segment

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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

75%

56%

75%

15%

38%

13%

0%

10%

20%

30%

40%

50%

60%

70%

80%

High Earning Opportunist Promising Middle Aged Conservatives

Married Single

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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


Consumer Durable Loan Segment

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• “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

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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

Personal Loan – Top up & Cross Sell Segment

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• 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

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


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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


Marital Status 24%

17%

14%

16%

9% 8%

11%

7% 6%

0%

5%

10%

15%

20%

25%


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%


Married Single

Mapping of Product Segments to Overall Segments

DESPERATE

MATURE

SUCCESSFUL

WISE

ACCUMULATOR

AFFLUENT

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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).

Data & Analytics

Customer Segmentation