1. CUSTOMER ANALYTICS & SEGMENTATION
FOR CUSTOMER CENTRIC ORGANIZATION & MARKETING OPTIMIZATION
By Natthawan Apiratanapimolchai
Email: Natthawan.rat@tmbbank.com

2. CUSTOMER-CENTRIC
CUSTOMER INSIGHT

3. CONSUMER CENTRIC ORGANIZATION
CUSTOMER FOCUS
 Move beyond the service
 Re-oriented and align entire operating
models to focus the customer
 Increase customer satisfaction
 Understand customer value and value
to their the customer
 Carefully define and quantify customer
segmentation
 Tailor business streams (product
development, marketing, sale, supply chain,
operation, customer care, etc.) to deliver
the greatest value to the best customer
at the least cost

4. - Discrete transaction at a
point in time
- Event-oriented marketing
- Narrow focus
- Customer life-cycle orientation
- Work with customer to solve both
immediate and long-term issues
- Build customer understanding at
each interaction
PRODUCT-FOCUSED vs. CUSTOMER-CENTRIC
1. CUSTOMER ORIENTATION
Tailor recommendation by past
purchased and browsing behavior

5. PRODUCT-FOCUSED vs. CUSTOMER-CENTRIC
2. SOLUTION MINDSET
- Narrow definition of
customer value proposition
- Off-the-shelf products
- Top down design
- Broad definition of the customer
value proposition
- Bundles that combine products,
service and knowledge
- Bottom-up. Designed on the front
line
Shift from “Selling product” to
“Solve the problem”

6. - Perceived as outsider
selling in
- Push product
- Transactional relationship
- Individual to individual
- Working as an insider
- Solution focus
- Advisory relationship
- Team-based selling
PRODUCT-FOCUSED vs. CUSTOMER-CENTRIC
3. ADVICE ORIENTATION
Engage continuous dialogue with customers: Before-During-After

7. - Centrally driven
- Limited decision making
power in the field
- Incentives based on product
economics and individual
performance
- Innovation and authority at the
front line with the customer
- Incentive based on customer
economics and team performance
PRODUCT-FOCUSED vs. CUSTOMER-CENTRIC
4. CAN-DO CUSTOMER INTERFACE

8. - “ONE SIZE FIT ALL”
processes
- Customization adds
complexity. One-off work
arounds
- Tailored business streams
- Balance between customization
and complexity
- Complexity isolated within the
system
PRODUCT-FOCUSED vs. CUSTOMER-CENTRIC
5. BUSINESS PROCESSES
- Rigid organizational
boundaries
- Organizational silos control
resources
- Limited trust across
organizational boundaries
- Cross-organizational teaming
- Joint credit
- High degree of organizational
trust
6. ORGANIZATIONAL LINKAGES AND METRICS

9. CONSUMER INSIGHT IS VERY IMPROTANT
A deep “truth” about
the customer based on
their behavior,
experiences, beliefs,
needs or desires, that is
relevant to the task or
issue and “rings bells”
with target people
A customer-centric organization has customer insight and
orientation embedded throughout

10. HOW TO KNOW CUSTOMER INSIGHT
Internal
Customer
Data
Behavior
Usage
Data
Research Social
• Focus group
• Quantitative survey
• Segmentation study
• Interview
• Social research
• Mystery shopping
• Staff feedback
• Community web-board
• Social network e.g.
Facebook
• Company website
• Demographic
• Psychographic
• Geographic
• Legacy system
• Touch-point system
• Billing system
• Complaint system
• Data warehouse
360 OF CUSTOMER INFORMATION

11. Internal
Customer
Data
Behavior
Usage
Data
Research Social
HOW TO KNOW CUSTOMER INSIGHT
• Demographic
• Psychographic
• Geographic
DEMOGRAPHIC
SEX
INCOME
PSYCHOGRAPHIC GEOGRAPHIC

12. HOW TO KNOW CUSTOMER INSIGHT
Internal
Customer
Data
Behavior
Usage
Data
Research Social
• Legacy system
• Touch-point system
• Billing system
• Complaint system
• Data warehouse

13. HOW TO KNOW CUSTOMER INSIGHT
Internal
Customer
Data
Behavior
Usage
Data
Research Social
• Focus group
• Quantitative survey
• Segmentation study
• Interview
• Social research
• Mystery shopping
• Staff feedback

14. HOW TO KNOW CUSTOMER INSIGHT
Internal
Customer
Data
Behavior
Usage
Data
Research Social
• Community web-board
• Social network e.g.
Facebook
• Company website

15. Product &
Service
Sales
Branding
Portfolio
CONSUMER INSIGHT IS VERY IMPROTANT
Consumer Insight
• Differentiate
• Initiate the new one to serve
market segment
• Find hidden needs and make
improvements
•Identify the most & least
profitable customers
•Avoid unprofitable markets
•Increase brand loyalty and
decrease brand switching
•Create effectively fit your
consumers
•Find, understand and focus on
your best customers can make you
a market leader
•Target the right customer
• Improve the competitive positioning to be
more accurate and better differentiate
from the competition
• Reduce competition by narrowly defined
market and establishing a niche Market

16. CONSUMER INSIGHT TO IMPROVE SALE
Background: Customers in each segments have the different needed on
Insurance
Deliverable: Different offer
Different sale-talk
Different POSM

17. CONSUMER INSIGHT TO IMPROVE SALE
Savvy Insurers
Intelligent, Sophisticated risk-takers.
Fact finders who need to know things
for themselves, they buy their
insurance through an agent
Profile: Financially savvy senior
managers who are also caring parent.
They buy all sorts of insurance to
ensure their family is well protected.
25-44 skew

18. CONSUMER INSIGHT TO IMPROVE SALE
Casual Followers
Active, easy-going, and mature
individuals, who look after themselves.
They are less concerned about their
look and are not brand-oriented
Profile: Health conscious white collar
workers. They buy Critical Illness
insurance on recommendation. Urban,
white collar workers, 35+ skew

19. CONSUMER INSIGHT TO IMPROVE SALE
Family Protectors
Family oriented, wise, confident and
mature. Their work (benefits) covers
them well but they still like to plan
ahead for their family. They are brand-
oriented and like eating out and
shopping
Profile: High income, upper class
families. Life insurance secures the
family’s future. 35+ skew

20. CONSUMER INSIGHT TO IMPROVE SALE
Next Generation
Aspirational, optimistic, looking
forward to their life ahead: getting
married and promotion
Profile: They are very open to
insurance but without a family to look
after they have not yet made the
transition from intention to purchase
decision

21. CONSUMER INSIGHT TO IMPROVE SALE
POSM is differently developed based on consumer insight who are looking for
BANC ASSURANCE but different objective
Casual FollowersSavvy Insurers Next GenerationFamily Protectors
หาประกัน
เพิ่ม1
หาประกัน
เพื่อตัวเอง2
หาประกัน
เพื่อครอบครัว3
หาประกัน
แรก4

22. CONSUMER INSIGHT IS VERY IMPROTANT
Ability to transform their understanding of
their customer base. This Knowledge help us
to extract maximum benefit from customer
insight
DATABASE ANALYSIS
SEGMENTATION
DATA MINING & PREDICTIVE MODEL

23. SEGMENTATION

24. How to segment customer by social media data?MARKET SEGMENTATION

25. DIFINE AND SUBDIVIDE
A LARGE HOMOGENOUS
MARKET INTO CLEARLY
IDENTIFIABLE SEGMENTS
HAVING SIMILAR
NEEDS WANTS
DEMAND CHARACTERISTICS
WHAT IS MARKET SEGMENTATION?

26. WHAT IS MARKET SEGMENTATION?
Market Segment is an identifiable
group of individuals, families,
businesses, or organizations,
sharing one or more characteristics
or needs in an otherwise
homogeneous market. Market
segments generally respond in a
predictable manner to a marketing
or promotion offer.
Clear
Identification
Measurability
Accessibility
Align with
Strategy

27. Develop new product
Differentiate the product
WHY IS SEGMENTATION NEEDED?

28. SEGMENTATION
LIFE-STAGE
MARKET SEGMENTATION

29. Example of Market Segmentation
SEGMENTATION
OCCUPATION
Military
Payroll
Owner Operator
Student
Government
MARKET SEGMENTATION

30. Example of Market Segmentation
SEGMENTATION
SOCIAL-CLASS
MARKET SEGMENTATION

31. Example of Market Segmentation
SEGMENTATION
BEHAVIOR
DEMOGRAPHIC
PSYCHOGRAPHIC
USAGE-TRANSACTION
GEOGRAPHIC
AGE GENDER
SEX
MARITAL STATUS
EDUCATION
INCOME
LIFESTYLE
PREFERENCE
PERSONALITY REGION
CITY
NEIGHBORHOOD
VOLUME
RECENCY
FREQUENCY
CHANNEL
ATTITUDE
LOYALTY
MARKET SEGMENTATION

32. A viable target segment should satisfy these requirements:
Go No-Go
HOW TO EVALUATE SEGMENT?

33. DATA MINING &
PREDICTIVE MODELING

34. TRENDS LEADING TO DATA FLOOD
WHAT IS DATA MINING?
MORE DATA IS GENERATED
MORE DATA IS CAPTURED

35. DATA MINING HELPS EXTRACT
INFORMATION
WHAT IS DATA MINING?
Fraud detection
• Which types of transactions are
likely to be fraudulent, given the
demographics and transactional
history of a particular customer?
Credit ratings/targeted marketing:
• Given a database of 100,000
names, which persons are the
least likely to default on their credit
cards?
• Identify likely responders to sales
promotions
Customer relationship
management:
• Which of my customers are
likely to be the most loyal,
and which are most likely to
leave for a competitor?

36. WHAT IS DATA MINING?
The process of analyzing
data from different
perspectives and
summarizing it into useful
information - information that
can be used to increase
revenue, cuts costs, or both.
The process of finding correlations or patterns
among dozens of fields in large

37. DATA MINING PROCESS

38. Modeling Process
Modeling
DATA MINING PROCESS

39. DATA MINING TECHNIQUES
1. Prediction Methods
Use some variables to predict unknown
or future values of other variables
• Classification
• Regression
• Deviation Detection
From [Fayyad, et.al.] Advances in Knowledge Discovery and Data Mining, 1996

40. WHAT IS DATA MINING?
2. Description Methods
Description Methods
Find human-interpretable
patterns that describe the
data
• Clustering
• Association Rule
Discovery
• Sequential Pattern
Discovery
From [Fayyad, et.al.] Advances in Knowledge Discovery and Data Mining, 1996

41. Business Objective:
Next Best Offer Product
Goal:
- Identify items that are bought next by historical purchasing
- Separate customer by customer segment
Example Result on Mid-Income Customer
• Transactional Deposit & Saving Deposit -> Bancassurance
• Transactional Deposit & Saving Deposit, Bancassurance -> Mutual Fund
• Transactional Deposit & Home Loan -> Credit Card
• Credit Card -> Personal Loan
MARKET BASKET ANALYSIS

42. Business Objective/Industry:
X-selling Personal Loan
on Existing customer
Goal:
Define target customer who are high propensity to buy personal loan
Approach:
• Use “Regression” technique apply with 360 customer data
• We know which customers decided to buy and which decided otherwise.
This {buy, don’t buy} decision forms the class attribute
• Collect various demographic, lifestyle, and company-interaction related information
about all such customers e.g. transactional behavior, inflow/outflow/net-flow etc.
• Use this information as input attributes to learn a regression model
• Derive propensity to buy score
• Select only top score customer to proactively offer product
X-SELLING PERSONAL LOAN

43. Business Objective/Industry:
Churn prediction in credit card
Goal:
Identify who likely to stop usage with us
Approach (Type of Data & Data Mining Technique):
• Apply “Classification” technique with credit card/payment transactions and the
information on its account-holder as attributes
• When does a customer stop usage and who are they?
• Label past transactions as a transactions. This forms the class attribute
• Learn a model for the class of the churn
• Use this model to detect high propensity to churn by observing credit card/payment
transactions on an account
• Proactively offer promotion on usage program to high value & high churn score
CHURN MODEL – TMB CREDIT CARD

44. Business Objective/Industry:
Transactional behavior segmentation by Clustering
Goal:
Subdivide a transactional customer into distinct subsets of them where any subset
have the common transactional behavior
Approach (Type of Data & Data Mining Technique):
• Collect different attributes of customers based on their transactional behavior e.g. usage
channel, transaction type, ticket size etc.
• Find clusters of similar customers
• Measure the clustering quality by observing transactional patterns of customers in same
cluster vs. those from different clusters
BEHAVIOR SEGMENT BY CLUSTERING – TRANSACTION AL BEHAVIOR

45. MUTUAL FUND WHO ARE LIKELY TO BUY MORE - RFM
Existing MF - Hi Fee
Existing MF - New to Hi Fee
Recent
More recent,
More likely to
buy again
Number of months
since last purchase
any MF
Frequent
More frequent,
More likely to
respond this time
Counting the
month of purchase
any MF
Monetary
More money spent,
More likely to
spend more
All amounts
purchased any MF
in 12 months
Concept
กลุ่มเป้าหมาย
ในการศึกษา Concept
ช่วงเวลาในการศึกษา
ช่วงเวลาการ
กลับมาซื้อเพิ่ม
ช่วงเวลาที่ศึกษาพฤติกรรมของลูกค ้า
12 เดือนก่อนหน้า
เหมาะกับการหา
โอกาสการซื้อเพิ่ม
(Up-selling)

46. 46
DATA MINING & BIG DATA ANALYTICS (CLIP)
https://www.youtube.com/watch?v=f2Kji24833Y

47. DELIVER SEGMENTATION
THRU DIRECT MARKETING
CAMPAIGN

48. To individually offer customers with the product/service that matched
to their needs by delivering the right offer by the right
message/channel to the right person at the right time
• Maintain quality
customer to stay with us
longer and win-back if
they left
• Increase their wallet-size
on target customer
• X-selling more product to
increase share of wallet
• Direct to prospect target
who are in selective
segment
Acquisition X-selling
Retention
Up-Selling/
Deep-
Selling
WHAT IS DIRECT MARKETING?

49. Customer Product Channel
Right Target Right Offer
Time
Right Communication
5 key elements to deliver direct marketing campaign
HOW TO DELIVER DIRECT MARKETING CAMPAIGN
Right Time
Right Channel

50. Affluent
Mid-
Income
Mass
1. Segmentation
2. Targeting Propensity to buy score for
select top target
3. Positioning
Channel:
EXAMPLE OF DIRECT MARKETING CAMPAIGN

51. :EXAMPLE OF DIRECT MARKETING CAMPAIGN
X-sell BA Health on Credit Card Spending Based
Segment: Mid-Income
Target: Who have credit card spending
on Health, Medical and Hospital
Positioning:
- Offer: Health Insurance
- Promotion: Buy 1 year free 1 month
- Channel: Call + SMS
- Time: After credit card spending

52. EXAMPLE OF DIRECT MARKETING CAMPAIGN
X-sell Homeloan Refinance by using Internal data
Ever
submit HL
> 3 years
Credit Card
spending in
Home&Decore
category
Segment: Mid-Income
Target: Who ever submit
HomeLoan > 3years or have
credit card spending on
Home&Decore category
Positioning:
- Offer: Home Refinance
- Promotion: Special rate
- Channel: Direct Mail
- Time: Money Expo
Season

53. 95%
84%
50%
12.5%
Success rate = 5%
(on total lead)
Contact
Control
1
Success rate = 5%
(on total lead)
Success rate = 1%
(on total lead)
Success rate = 3%
(on total lead)
Control
2
4%
2%
%Uplift
Same profile not contact
Different Profile not contact
HOW TO MEASURE THE EFFECTIVENESS OF DIRECT MARKETING CAMPAIGN
REACH: LEAD UTILIZATION, LEAD QUALITY
RIGHT: #,% SUCCESS (PURCHASE) ON TOTAL LEAD, %UPLIFT
VOLUME: REVENUE PER CASE

55.  Collect:
Transactional data of 50 million consumers
(about 70 petabytes)
 Analyze:
Raise the bar from sampling-analysis to the
full customer set by using Big Data technology
To understand the customer across all
channels and interactions
Propensity to buy model
 Utilize:
 To appeal offers to well-defined customer
segments
Apply to ‘BankAmeriDeals’ program which
provides cash-back offers based on where the
customers have made payments in the past
The largest bank
in US
BIG DATA ANALYTICS CASE STUDY – BANKING & FINANCIAL SERVICE

56.  Collect:
9 millions transactions per day (40% of card
transactions in Australia)
12 million account profiles
 Analyze:
Real-time analytics scheme (In-memory
computing)
 Utilize:
Create better products and services; which
help:
o Providing more personalized service to
customers both in person and online
o Right pricing for an individual customer
Reduce Cheque Fraud by 50% and Internet
Fraud by 80%
BIG DATA ANALYTICS CASE STUDY – BANKING & FINANCIAL SERVICE

57.  Collect:
Customer Basic Profiles
Their services used
Their business
Market Trend
 Analyze:
The appropriate financial advice for
each customers
 Utilize:
Less frequent that customers have to
meet-up with the financial advisor
To ensure that we offer the right
product to wealth customers
Faster and more personalized
recommendations
BIG DATA ANALYTICS CASE STUDY – BANKING & FINANCIAL SERVICE

58.  Collect:
 Australian Bureau of Statistics Census data
Ubank customers’ transaction records
NAB customers transaction records
Additional input by users to perform a “financial
health check” (such as gender, age, income, living
situation, post code, rent or own their home)
 Analyze:
Average spending habits of people in that
demographic (such as monthly shopping, housing,
communication costs)
 Utilize:
[PeopleLikeU] application (which is not survey-
based, but it’s real transactional data) to compare
and benchmark the spending habits of different
types of people
BIG DATA ANALYTICS CASE STUDY – BANKING & FINANCIAL SERVICE

59. RETAIL IN 2020

60. • www.strategyand.pwc.com
• http://www.businessdictionary.com
• http://blog.qmee.com
• From [Fayyad, et.al.] Advances in Knowledge Discovery and Data Mining, 1996
60
REFERENCE