Customer segmentation approach

1. CUSTOMER SEGMENTATION
Analytics Framework
Sumit K Jha (MBA (Fin), LSSGB, PMP®)

2. Customer Segmentation in Complete Market
Development Cycle
Phase I: Customer
Segmentation
• Identify explicit or latent
customer need
• Outperform the
competition by
developing uniquely
appealing products and
services
Phase III:
Institutionalization
Inputs
Business Context
Phase II: Planning and
execution
Inputs
Inputs
• Divide the market into
meaningful and
measurable segments
according to customers'
needs, their past behaviors
or their demographic
profiles
• Determine profit potential of
each segment by analyzing
the revenue and cost impacts
of serving each segment
• Ideally this should answer
who, where, how, what,
how much and why
Outputs
• Target segments according
to their profit potential and
the company's ability to serve
them in a proprietary way
• Measure performance of
each segment and adjust
the segmentation
approach over time as
market conditions change
• Invest resources to tailor
product & services and
create marketing &
distribution programs to
match the needs of each
target segment
Outputs
Outputs
Project lifecycle is
explained in the next
slide
• Customer loyalty
• Service delivery programs
• Innovation
• Targeted marketing
• Unique customer segments
with defined characteristics
• Product development and
delivery
• Competitive advantage
• Increased share of wallet

3. Approach for Customer Segmentation
Analytics
Understanding
Business
Problem
Identify objective
function
(increasing
customer loyalty,
better marketing
ROI, etc.)
► Envisage
expected results
► Develop ingoing
hypotheses
Consultative% Time
approach spent
required
Activities
►
10%
Develop
Solution
Approach
Define model
specifications
► Expected output
framework
(dashboard)
► Develop project
plan and timeline
►
10%
Collate and
Manage
Data
Data request,
pull, transfer
► Data cleaning,
enrichment and
structuring
► Analysis
database creation
►
50%
25% of the time spent
with consultative
approach
Run
Analytics
Hypothesis
testing
► Multivariate
analysis
► Quality
assurance
►
20%
Deliver
Solution
Preview results
with stakeholders
► Feedback to rerun analytics
► Final delivery
►
10%

4. Customer Segmentation Analytics process
Data Preparation
/Model
Development
/Scoring
Define
Segmentation
Objective
Identify objectives
► Behavioral and
Marketing Segments
► Directed versus
Undirected
Segmentation
►
Segmentation
Variables
► Number of
Segments
►Segment scoring
► Segment profile
attributes
► Statistical tools for
segmentation
►
Segment
Profiling
Explanation of
customer segments
► Characteristics of
customers in each
segment
► Assigning
marketing customer
segment
►
Identification of
Segment
Strategies
Explain who, what,
why, how much,
when, how of
marketing

5. Segmentation Objective
Segmentation Objectives

Improving profitability through more effective marketing

Serving better to high priority customers

Changing the customer mix to provide a greater proportion of high-profit or highprofit-potential customers in the customer base

Provide global vision of company’s customers

Identify valuable customers

Identify cross-sell opportunities
Behavioral and Marketing Segments

Marketing segments - groups of customers who are similar to each other with
respect to some socio-economic-demographic factors like: age, sex, family status,
occupation, living area etc.

Behavioral segments - groups of customers who behave in a similar manner in
relation with the business
Directed versus Undirected Segmentation


Supervised (directed) segmentation – business analyst defines one or more target
variables that should drive the segmentation
Unsupervised (undirected) segmentation – analytical algorithm uncovers hidden
patterns that may be significant and useful for the given purpose

6. Data Preparation, Modeling and Scoring
Data Preparation – Segmentation Variables

Active variables - variables, which are expected to have an influence on clustering.

Descriptive variables - further profiling of the segments that are determined by active variables.
They are used for identification of the main characteristics of the clusters.
Number of Segments

Good Cluster Definition – Clusters whose members are very similar to each other while at the
same time the clusters themselves are well separated (by criteria selected for clustering)

Business Purpose – 5 to 12 segments

Trial and Error Process
Segment Scoring

Process of assigning the segment identification variable for each customer on the basis of some
pre-specified segment structure.

7. Segment Profile Attributes
Segment profiling attributes
Segmentation is normally performed along with the following demographic, geographic,
psychographic, and behavioral variables;

Demographic segmentation variables describe characteristics of customers and include age,
gender, race, education, occupation, income, religion, marital status, family size, children, home
ownership, socioeconomic status, and so on. Note that demographic segmentation normally refers
to segmentation with these demographic variables.

Geographic variables include various classification of geographic areas, for example, zip code,
state, country, region, climate, population, and other geographical census data. Note that this
information can come from national census data. For more, see geographic segmentation.

Psychographic segmentation variables describe life style, personality, values, attitudes, and so
on. Note that psychographic segmentation normally refers to segmentation with these
psychographic variables.

Behavioral segmentation variables include product usage rate and end, brand royalty, benefit
sought, decision making units, ready-to-buy stage, and so on.

Past business history, Customers' past business track records can be extremely useful for
segmentation. This may include total amounts purchased, purchasing frequency, (credit) default
records, (insurance) claims, responsiveness for marketing campaigns, and so on.

8. Statistical Tools Used for Segmentation
Statistical tools

Cluster analysis is a tool commonly used for customer segmentation. In cluster analysis, the
goal is to organize observed data into a meaningful structure. This type of analysis is
different from traditional statistical approaches such as linear regression in that cluster
analysis does not have a dependent variable

The tree building approach, CHAID, is also used for determining customer segments in a
market. A CHAID decision tree uses multi-class splits to segment the data into nodes.
Members of nodes tend to be very similar within the node as well as different from members
of other nodes. This tool often effectively yields many multi-way frequency tables when
classifying a categorical response variable, making it popular in marketing research

9. Segment Profiling
Segment Profiling

Explanation of the assigned customer segments

The result - characteristics of a typical customer within each segment

Utilization in assigning the correct marketing segment for each behavioral segment

The typical explanative variables used in profiling:

Age, Marital status, Occupation, Education level, Annual income, Postal code (or information
derived from that, such as city, town, or village), Activity level of customer, Life cycle of
customer, Customers market segment, Residence status code

10. Identification of Segmentation Strategies
Aspects of
Marketing
Explanation
Who?
Segment description. Differentiation comparing to other
segments.
What?
What products, bundles?
Why?
Segment’s needs based message.
How much?
Pricing rules.
When?
Time for interactions.
How?
Preferred distribution channels.

11. Example for Data Attributes for Banking
customer



Customer demographics (retail/corporate client)
Product information (number and types of
products, loyalty)
Transactional behavior


Balances





Channel usage number (ATM, online, branch) per
given time period.
Complaints
Risk profile


Average, opening and closing balances per defined
time period.
Utilization of distribution channels


Number, amount and types of transactions per
defined time period
Delinquency/claim (number and amount per defined
period)
Household relations
Contact History
Competitive information
Clustering Example- Partition data
into groups with similar
characteristics

12. Preliminary List of attributes to be Used for
Customer Segmentation for a IT company
Who
Where
How
What
How much
Why
Industry
Geography of
sale
Through sales
representative
Type of Product/s
sold
Size of each contract
Unique product/service
matching customer
requirement
Geography
Work place
Through channel
partners
Type of Service/s
sold
Overall sales within
a year
Effective marketing
campaign
Size of the
company
(Revenue,
employees)
Exhibition
Through key
account manager
Type of Software
sold
Share of wallet
Recommendation from
customers or any other
Market share in
the industry
Road show
New purchase
Specification to
product delivery
(Straight/Bundling)
Time taken (sales
cycle)
Relationship brought by
new account manager or
sales person
Public/private
Conferences
Repeat purchase
(Upsell or Crosssell)
Specification to
services delivery
(onsite, off-site)
Profitability of
contract/sales
Discounts given
Line of business
within the
company
Online
Through solution
partners
Specification to
software delivery
(Straight/Bundling)
Discounts given
Product/service
perception
Number of meetings
for one sale
Effective sales pitch
Number of
employees working
on each contract
Product/service part of
larger solution given by a
partner
Credit history
Relationship with
the company
(Integrated
/special accounts)
Through
competitive bids

13. Thank You