CUSTOMER ANALYTICS & SEGMENTATION FOR CUSTOMER CENTRIC ORGANIZATION & MARKETING OPTIMIZATION

By Natthawan Apiratanapimolchai

Email: Natthawan.rat@tmbbank.com

CUSTOMER-CENTRIC

CUSTOMER INSIGHT

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

- 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

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”

- 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 purchase

- 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

- “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

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

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

Internal

Customer

Data

Behavior

Usage

Data

Research Social

HOW TO KNOW CUSTOMER INSIGHT

• Demographic

• Psychographic

• Geographic

DEMOGRAPHIC

SEX

INCOME

PSYCHOGRAPHIC GEOGRAPHIC

HOW TO KNOW CUSTOMER INSIGHT

Internal

Customer

Data

Behavior

Usage

Data

Research Social

• Legacy system

• Touch-point system

• Billing system

• Complaint system

• Data warehouse

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

HOW TO KNOW CUSTOMER INSIGHT

Internal

Customer

Data

Behavior

Usage

Data

Research Social• Community web-board

• Social network e.g.

Facebook

• Company website

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

CONSUMER INSIGHT TO IMPROVE SALE

Background: Customers in each segments have the different needed on Insurance

Deliverable: Different offer

Different sale-talkDifferent POSM

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

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

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

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

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

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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

SEGMENTATION

How to segment customer by social media data?MARKET SEGMENTATION

DIFINE AND SUBDIVIDE

A LARGE HOMOGENOUS

MARKET INTO CLEARLY

IDENTIFIABLE SEGMENTS

HAVING SIMILAR

NEEDS WANTSDEMAND CHARACTERISTICS

WHAT IS MARKET SEGMENTATION?

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

Develop new product

Differentiate the product

WHY IS SEGMENTATION NEEDED?

SEGMENTATIONLIFE-STAGE

MARKET SEGMENTATION

Example of Market Segmentation

SEGMENTATIONOCCUPATION

Military

Payroll

Owner Operator

Student

Government

MARKET SEGMENTATION

Example of Market Segmentation

SEGMENTATION

SOCIAL-CLASS

MARKET SEGMENTATION

Example of Market Segmentation

SEGMENTATIONBEHAVIOR

DEMOGRAPHIC

PSYCHOGRAPHIC

USAGE-TRANSACTION

GEOGRAPHIC

AGE GENDER

SEX

MARITAL STATUS

EDUCATIONINCOME

LIFESTYLE

PREFERENCE

PERSONALITY REGION

CITYNEIGHBORHOOD

VOLUME

RECENCY

FREQUENCY

CHANNEL

ATTITUDE

LOYALTY

MARKET SEGMENTATION

A viable target segment should satisfy these requirements:

Go No-Go

HOW TO EVALUATE SEGMENT?

DATA MINING &

PREDICTIVE MODELING

TRENDS LEADING TO DATA FLOOD

WHAT IS DATA MINING?

MORE DATA IS GENERATED

MORE DATA IS CAPTURED

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?

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

DATA MINING PROCESS

Modeling Process

Modeling

DATA MINING PROCESS

DATA MINING TECHNIQUES

1. Prediction MethodsUse 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

WHAT IS DATA MINING?

2. Description MethodsDescription 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

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

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

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

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

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

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เหมาะกบัการหาโอกาสการซือ้เพิม่

(Up-selling)

46

DATA MINING & BIG DATA ANALYTICS (CLIP)

https://www.youtube.com/watch?v=f2Kji24833Y

DELIVER SEGMENTATION

THRU DIRECT MARKETING

CAMPAIGN

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?

Customer Product ChannelRight Target Right Offer

Time

Right Communication

5 key elements to deliver direct marketing campaign

HOW TO DELIVER DIRECT MARKETING CAMPAIGN

Right Time

Right Channel

Affluent

Mid-Income

Mass

1. Segmentation

2. Targeting Propensity to buy score for

select top target

3. Positioning

Channel:

EXAMPLE OF DIRECT MARKETING CAMPAIGN

: 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

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

95%

84%

50%

12.5%

Success rate = 5%(on total lead)

Contact

Control1

Success rate = 5%(on total lead)

Success rate = 1%(on total lead)

Success rate = 3%(on total lead)

Control2

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

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

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:

oProviding more personalized service to

customers both in person and online

oRight pricing for an individual customer

Reduce Cheque Fraud by 50% and Internet

Fraud by 80%

BIG DATA ANALYTICS CASE STUDY – BANKING & FINANCIAL SERVICE

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

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

RETAIL IN 2020

• 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