Market Segmentation
 Market segmentation is the
subdividing of a market into
distinct subsets of customers.
Segments
 Members are different between
segments but similar within.
Segment–1
Segmentation Marketing
Definition
Differentiating your product and
marketing efforts to meet the
needs of different segments, that
is, applying the marketing
concept to market segmentation.
Segment–2
Primary Characteristics
of Segments
 Bases—characteristics that tell us why segments
differ (eg, needs, preferences, decision
processes).
 Descriptors—characteristics that help us find and
reach segments.
(Business markets)
(Consumer markets)
Industry
Size
Location
Organizational
structure
Age/Income
Education
Profession
Life styles
Media habits
Segment–3
A Two-Stage Approach
in Business Markets
Macro-Segments:
 First stage/rough cut
 Industry/application
 Firm size
Micro-Segments:
 Second-stage/fine cut
 Different customer needs,
wants, values within macrosegment
Segment–4
Relevant Segmentation Descriptor
Variable A: Climatic Region
1. Snow Belt
2. Moderate Belt
3. Sun Belt
Fraction of
Customers
Segment 1
0
Segment 2
Segment 3
100%
Likelihood of Purchasing Solar Water Heater
(a)
Segment–5
Irrelevant Segmentation Descriptor
Variable B: Education
1. Low Education
2. Moderate Education
3. High Education
Fraction of
Customers
Segment 1
Segment 2
Segment 3
0
100%
Likelihood of Purchasing Solar Water Heater
(b)
Segment–6
Variables to Segment
and Describe Markets
Consumer
Industrial
Segmentation
Bases
Needs, wants benefits,
solutions to problems,
usage situation, usage rate.
Needs, wants benefits, solutions to
problems, usage situation, usage rate,
size*, industrial*.
Descriptors
Demographics
Age, income, marital status,
family type & size,
gender, social class, etc.
Lifestyle, values, &
personality characteristics.
Use occasions, usage level,
complementary &
substitute products used,
brand loyalty, etc.
Individual or group
(family) choice, low or high
involvement purchase,
attitudes and knowledge
about product class, price
sensitivity, etc.
Level of use, types of
media used, times of use,
etc.
Industry, size, location, current
supplier(s), technology utilization,
etc.
Personality characteristics of
decision makers.
Use occasions, usage level,
complementary & substitute
products used, brand loyalty, order
size, applications, etc.
Formalization of purchasing
procedures, size & characteristics
of decision making group, use of
outside consultants, purchasing
criteria, (de)centralizing buying,
price sensitivity, switching costs, etc.
Level of use, types of media used,
time of use, patronage at trade shows,
receptivity of sales people, etc.
Psychographics
Behavior
Decision Making
Media Patterns
Segment–7
Segmentation in Action
A Marriott Hotel used to be a Marriott Hotel: An
upscale hotel catering to business people, pleasure
seekers and international and group travelers.
Today, besides the Marriott Hotels (the company’s
major business), there are Marriott Suites,
Residence Inns, Courtyards by Marriott and
Fairfield Inns—each serving a smaller, targeted
segment of the market.
—Lenneman and Stanton, “Mining for
Niches,” Business Horizons.
Segment–8
Segmentation in Action
We segment our customers by letter volume, by postage
volume, by the type of equipment they use. Then we
segment on whether they buy or lease equipment.
Based on this knowledge, we target our marketing
messages, fine tune our sales tactics, learn which
benefits appeal to which customers and zero in on key
decision makers at a company.
—Kathleen Synnot, VP, Worldwide Marketing
Mailing Systems Division, Pitney Bowes, Inc.
[quoted in Marketing Masters (Walden and Lawler)]
Segment–9
Customers’ Diverse Needs Require
Diverse Channels
Kodak increases customer contact and support
with a three tiered distribution system.
. . . Business Imaging Division created three
avenues for marketing microfilm, supplies and
imaging systems and software:
 direct sales reps (for more complex systems);
 brokers and distributors (for film sales and delivery);
 Components Marketing Division (to sell to system
integrators and VARs).
—Business Marketing
Segment–10
Ad in London Newspapers, 1900
Men wanted for hazardous journey.
Small wages, bitter cold, long months of
complete darkness, constant danger, safe
return doubtful. Honor and recognition
in case of success.
—Ernest Shackleton, Arctic Explorer
Did it work?
Segment–11
Segmentation
If you’re not thinking segments, you’re not
thinking. To think segments means you
have to think about what drives customers,
customer groups, and the choices that are
or might be available to them.
—Levitt, Marketing Imagination
Segment–12
Segmentation Marketing Implies a
“Market”
A market consists of all the potential
customers sharing a particular need or
want who might be willing and able to
engage in exchange to satisfy that need
or want.
—Kotler, Marketing Management
Segment–13
Market Definition
Customer-Need
Set 1 (Market 1)
Product 1
Technology A
Customer-Need
Set 2 (Market 2)
Technology B
 Common customer needs define a market not a product.
Segment–14
Implications
1. Segmentation defines common customer needs.
2. Those common needs may be satisfied by similar or
dissimilar technologies or have different solutions.
Ex: Customer dissatisfaction at long delays at
supermarket checkout.
Solution 1: Faster UPC scanner systems.
Solution 2: Entertainment/Sales systems on checkout lines.
Note: Total solution defines (competitive)
market, not product or technology.
Segment–15
Market Definition Approaches
Customer-Behavior:
 Demand cross elasticity
 Brand/product switching
Perception/Judgment:
 Engineering/technological
substitution
 Customer judgments/
perceptual mapping
Segment–16
Why is Market Definition
Important?
 Strategy
(What to focus on).
 Resource allocation
(How much/where/when?).
 Feedback/performance measurement
(How well are we doing? How can we learn
from our actions?).
Segment–17
Electric Typewriter Market
1980
1981
1982
1983
1984
1985
A (Us)
403,027
495,192
548,905
550,351
541,388
515,000
B
369,916
388,520
349,396
323,005
342,197
297,000
Other
367,057
324,010
343,885
370,374
202,495
129,070
Total
1,140,000
1,207,722
1,242,186
1,243,730
1,086,080
941,070
Shipments
Market Shares (%)
A (Us)
35.4
41.0
44.2
44.2
49.8
54.7
B
32.4
32.2
28.1
26.0
31.5
31.6
Other
32.2
26.8
27.7
29.8
18.6
13.7
Segment–18
Word Processor Market
1980
1981
1982
1983
1984
1985
A (Us)
403,027
495,192
548,905
550,351
541,388
515,000
B
369,916
388,520
349,396
323,005
342,197
297,000
Other Electric
Electronic Word
367,057
324,010
343,885
370,374
202,495
129,070
60,040
112,220
209,800
392,352
733,699
1,372,016
1,200,040
1,319,942
1,451,986
1,636,082
1,819,778
2,313,086
Shipments
Processors
Total
Market Shares (%)
A (Us)
33.6
37.5
37.8
33.6
29.8
22.3
B
30.8
29.4
24.1
19.7
18.8
12.8
Other Electric
Electronic Word
Processors
30.6
24.5
23.7
22.6
11.1
5.6
5.0
8.5
14.4
24.0
40.3
59.3
Segment–19
Market Definition
by Switching Behavior
Current Purchase Occasion
Coke
Diet
Coke
Coke
53%
Last
Diet Coke
Purchase
Pepsi
Occasion
Diet Pepsi
Sprite
Diet Sprite
Pepsi
Diet
Pepsi
Sprite
Diet
Sprite
Total
9%
27%
4%
5%
2%
100%
12%
61%
4%
15%
2%
5%
100%
24%
3%
58%
9%
5%
1%
100%
4%
14%
11%
63%
2%
6%
100%
21%
2%
17%
3%
52%
6%
100%
2%
15%
2%
12%
7%
61%
100%
Segment–20
STP as Business Strategy
Segmentation
 Identify segmentation bases and segment the market.
 Develop profiles of resulting segments.
Targeting
 Evaluate attractiveness of each segment.
 Select target segments.
Positioning
 Identify possible positioning concepts for each target segment.
 Select, develop, and communicate the chosen concept.
… to create and claim value
Segment–21
How STP Adds Value to a Firm
Segmentation
Identify segments
Targeting
Select segments
Positioning
Create competitive
advantage
Marketing resources are focused to better meet customers
needs and deliver more value to them
Customers develop preference for brands that better meet
their needs and deliver more value
Customers become brand/supplier loyal, repeat purchase,
communicate favorable experiences
Brand/supplier loyalty leads to increased market share and
creates a barrier to competition
Fewer marketing resources needed over time to maintain
share due to brand or supplier loyalty
Profitability (value to the firm) increases
Segment–22
STPing the Market for Eggs
Segments:
Disinterested consumers
Casual Egg Users
Health conscious consumers
Enthusiastic users
Profiles:
Beliefs
Attitudes
Lifestyles
Health/Nutrition consciousness
Media habits
Consumption habits
Demographics
—Frank and Phillips,
Agribusiness, July 1990
Segment–23
Targeting and Positioning
Strategy
Casual Users
Health Conscious
Consumers
Positioning
Convenient and
useful in many
situations.
Ideal and natural
food, good for the
family.
Traditional food with
many applications.
Very convenient, good
for the family.
Copy
Visuals
Informal
settings.
Health-oriented
personality or
situation.
Larger family setting.
Major meal, possibly
with guests.
Copy
Tonality
Easy pace,
relaxed
atmospher.
Fresh, clean
setting, very
natural.
Reinforcing, emphasis
on benefits and wide
use.
Promotions
Reminders at
checkout, egg
display, or
dairy.
Matter-of-fact
information on the
nutritional value
and health
attributes of eggs in
recipes and leaflets.
Simple reminders to
buy eggs.
Enthusiastic Users
Segment–24
Overview of Marketing
Engineering Methods for STP
 Clustering and discriminant
analysis (PDA2001
exercise)
 Choice-based segmentation
(ABB Electric)
 Perceptual mapping
(G20 exercise)
Segment–25
Segmentation (for Carpet Fibers)
Perceptions/Ratings for one respondent:
Customer Values
Strength
(Importance)
A,B,C,D:
Location of
segment centers.
Typical members:
A: schools
B: light commercial
C: indoor/outdoor
carpeting
D: health clubs
.. . .
.A. .. ..
.
B. .
.. .. . .
.. . .
.
.
.
D. . .
... ....
.
C. .
.. . .. .
.. . .
.
.
Distance between
segments C and D
Water Resistance
(Importance)
Segment–26
Targeting
Segment(s) to serve
Strength
(Importance)
.. .
. . ....
.
.
.
.. ... .
.. . .
.
.
.. ... .
.. . .
.
.
.. ... .
.. . .
Water Resistance
(Importance)
Segment–27
Positioning
Product Positioning
.. .
Comp 1
Comp 2
Strength
(Importance)
.
.
.. ... .
.. . .
Us
.
.
.. ... .
.. . .
.
.
.. ... .
.. . .
Water Resistance
(Importance)
Segment–28
A Note on Positioning
Positioning involves designing an offering so that
the target segment members perceive it in a distinct
and valued way relative to competitors.
Three ways to position an offering:
1. Unique
(“Only product/service with XXX”)
2. Difference
(“More than twice the [feature] vs.
[competitor]”)
3. Similarities
(“Same functionality as [competitor];
lower price”)
What are you telling your targeted segments?
Segment–29
Steps in a Segmentation Study
 Articulate a strategic rationale for segmentation (ie,
why are we segmenting this market?).
 Select a set of needs-based segmentation variables
most useful for achieving the strategic goals.
 Select a cluster analysis procedure for aggregating
(or disaggregating customers) into segments.
 Group customers into a defined number of different
segments.
 Choose the segments that will best serve the firm’s
strategy, given its capabilities and the likely
reactions of competitors.
Segment–30
Total Customer Value =
Functional Value
Price/Performance
(What does this product do for me?)
+
Supplier/Service Value
Advertising
Selling
Service Efforts
What does the product mean to me?
(What is the insurance? service? psychological?
value of the product or supplier?)
Segment–31
Customer Needs and Customer Value Measurement
Customer Needs and Buying Process
Behaviors
Ignore
Present
State
Postpone
Functional
Perceived
and
Economic
Needs
and
Psychological
Needs
Desired
State
Engage in
Purchase Process
•Search for options
•Evaluate options
•Choose option
 •Purchase Option
•Use Option
Motivation
Customer
Value
Measurement
Approaches
Objective
Measures
of Value
Perceptual
Measures
of Value
Behavioral
Measures
of Value
Customer Value Assessment
Procedures
Customer
Value
Attitude-Based
Direct Questions
Unconstrainted
• Focus groups
• Direct survey questions
• Importance and attitude ratings
• Rule-based system/AI/expert
systems
Behavior-Based
• Choice models
• Neural networks
• Discriminant analysis
Inferential/Value Based
• Internal engineering assessment
• Indirect survey questions
• Field value-in-use assessment
Indirect/(Decompositional Methods)
• Conjoint analysis
• Preference Regression
Constrained/Compositional Methods
• Multiattribute value analysis
• Benchmarking
Segment–33
Segmentation: Methods Overview
 Factor analysis (to reduce data
before cluster analysis).
 Cluster analysis to form segments.
 Discriminant analysis to describe
segments.
Segment–34
Cluster Analysis for
Segmenting Markets
 Define a measure to assess the similarity of customers
on the basis of their needs.
 Group customers with similar needs. The software
uses the “Ward’s minimum variance criterion” and, as
an option, the K-Means algorithm for doing this.
 Select the number of segments using numeric and
strategic criteria, and your judgment.
 Profile the needs of the selected segments (e.g., using
cluster means).
Segment–35
Cluster Analysis Issues
 Defining a measure of similarity (or distance)
between segments.
 Identifying “outliers.”
 Selecting a clustering procedure
 Hierarchical clustering (e.g., Single linkage, average
linkage, and minimum variance methods)
 Partitioning methods (e.g., K-Means)
 Cluster profiling
 Univariate analysis
 Multiple discriminant analysis
Segment–36
Doing Cluster Analysis
a = distance from member
to cluster center
b = distance from I to III
•
Dimension 2
•
• •
•
Perceptions or ratings data
from one respondent
III
b
•
I
•
•
•
a
•
•
•
II
Dimension 1
Segment–37
Single Linkage Cluster Example
Distance Matrix
Co#1
Co#2
Co#3
Co#4
Co#5
0.00
1.49
3.42
1.81
5.05
0.00
2.29
1.99
4.82
0.00
1.48
4.94
0.00
4.83
0.00
Company #1
Company #2
Company #3
Company #4
Company #5
Resulting
Dendogram
1
2
Company
3
4
5
1
2
3
Distance
4
5
Segment–38
Ward’s Minimum Variance
Agglomerative Clustering Procedure
First Stage:
A = 2
Second Stage:
Third Stage:
Fourth Stage:
Fifth Stage:
B =
AB =
5
C = 9
4.5
BD = 12.5
AC = 24.5
BE = 50.0
AD = 32.0
CD = 0.5
AE = 84.5
CE = 18.0
BC =
DE = 12.5
8.0
CDA = 38.0
CDB = 14.0
AE = 85.0
BE = 50.5
ABCD = 41.0
CDE = 20.66
ABE= 93.17
D = 10
AB =
E = 15
5.0
CDE = 25.18
ABCDE = 98.8
Segment–39
Ward’s Minimum Variance
Agglomerative Clustering Procedure
98.80
25.18
5.00
0.50
A
B
C
D
E
Segment–40
Interpreting Cluster Analysis
Results
 Select the appropriate number of clusters:
 Are the bases variables highly correlated? (Should we reduce the
data through factor analysis before clustering?)
 Are the clusters separated well from each other?
 Should we combine or separate the clusters?
 Can you come up with descriptive names for each cluster (eg,
professionals, techno-savvy, etc.)?
 Segment the market independently of your ability to reach
the segments (ie, separately evaluate segmentation and
discriminant analysis results).
Segment–41
Profiling Clusters
Two Cluster Solution for PC Data: Need-Based Variables
1
Design
Means of
Variables
0
Business
–1
size
power office
use
LAN
color
storage wide
periph. budget
needs connect.
Segment–42
Which Segments to Serve?
—Segment Attractiveness Criteria
Criterion
I. Size and Growth
1. Size
2. Growth
Examples of Considerations
• Market potential, current market penetration
• Past growth forecasts of technology change
II. Structural Characteristics
3. Competition
4. Segment saturation
5. Protectability
6. Environmental risk
III. Product-Market Fit
7. Fit
8. Relationships with
segments
9. Profitability
• Barriers to entry, barriers to exit, position of
competitors, ability to retaliate
• Gaps in the market
• Patentability of products, barriers to entry
• Economic, political, and technological change
• Coherence with company’s strengths and image
• Synergy, cost interactions, image transfers,
cannibalization
• Entry costs, margin levels, return on investment
Segment–43
Selecting Segments to Serve
E
Strong
Firm’s
Competitive
Position
B
Medium
D
A
C
Weak
Low
Average
High
Segment Attractiveness
Segment–44
Discriminant Analysis for
Describing Market Segments
 Identify a set of “observable” variables
that helps you to understand how to
reach and serve the needs of selected
clusters.
 Use discriminant analysis to identify
underlying dimensions (axes) that
maximally differentiate between the
selected clusters.
Segment–45
Two-Group Discriminant Analysis
Price
Sensitivity
X-segment
x = high propensity to buy
o = low propensity to buy
XXOXOOO
XXXOXXOOOO
XXXXOOOXOOO
XXOXXOXOOOO
XXOXOOOOOOO
Need for Data Storage
O-segment
Segment–46
Interpreting Discriminant
Analysis Results
 What proportion of the total variance in the
descriptor data is explained by the statistically
significant discriminant axes?
 Does the model have good predictability (“hit
rate”) in each cluster?
 Can you identify good descriptors to find
differences between clusters? (Examine
correlations between discriminant axes and
each descriptor variable).
Segment–47
Behavior-Based Segmentation
 Traditional segmentation
(eg, demographic,
psychographic)
 Needs-based segmentation
 Behavior-based segmentation
(choice models)
Segment–48
Choice Models
1. Observe choice:
(Buy/not buy =>
direct marketers
Brand bought => packaged goods,
ABB)
2. Capture related data:
 demographics
 attitudes/perceptions
 market conditions (price, promotion, etc.)
3. Link
1 to 2 via “choice model” => model
reveals
importance weights of characteristics
Segment–49
Choice Models vs Surveys
With standard survey methods . . .
preference/
choice

predict

importance
weights

observe/ask

perceptions

observe/ask
But with choice models . . .
choice

observe

importance
weights

infer

perceptions

observe/ask
Segment–50
(ABB) Behavior-Based
Segmentation Model
Stage 1: Screen products using key attributes to identify the
“consideration set of suppliers” for each type of customer.
Stage 2: Assume that customers (of each type) will choose suppliers
to maximize their utility via a random utility model.
Uij = Vij + eij
where:
Uij
=
Utility that customer i has for supplier j’s product.
Vij
=
Deterministic component of utility that is a function of product and
supplier attributes.
eij
=
An error term that reflects the non-deterministic component of utility.
Segment–51
Attributes in ABB’s
Choice-Segmentation Model
 Invoice price
 Energy losses
 Overall product quality
 Availability of spare parts
 Clarity of bid document
 Knowledgeable salespeople
 Maintenance requirement
 Ease of installation
 Warranty
Segment–52
Specification of the Deterministic
Component of Utility
Vij =  Wk bijk
k
where:
i = an index to represent customers, j is an
index to represent suppliers, and k is an
index to represent attributes.
bijk = i’s perception of attribute k for supplier j.
wk = estimated coefficient to represent the
impact of bijk on the utility realized for
attribute k of supplier j for customer i.
Segment–53
A Key Result from this Specification:
The Multinomial Logit (MNL) Model
If customer’s past choices are assumed to reflect the principle
of utility maximization and the error (eij) has a specific form
called double exponential, then:
^
eVij
pij = ––––––
^
Vik
e
k
^
where:
pij = probability that customer i chooses supplier j.
Vij = estimated value of utility (ie, based on estimates of
bijk) obtained from maximum likelihood estimation.
Segment–54
What Does This Result Imply?
 Interval-level utility measurements are good
enough. That is:
^
eVij
^
eVij + a
pij = –––––– = ––––––
^
^
Vik
Vik + a
e
e
k
k
 The marginal impact of an attribute is highest
when the probability of choosing an option j
is 0.5.
Segment–55
What Does This Result Imply? (cont’d)
dPil
 wk Pil* (1  Pil* )
dbijk
Marginal Impact
of an Attribute on
the Probability of
Choosing an
Option
0.5
Probability of Choosing the Option
Segment–56
Applying the MNL Model in
Segmentation Studies
Key idea: Segment on the basis of
probability of choice—
1. Loyal to us
2. Loyal to competitor
3. Switchables:
loseable/winnable
customers
Segment–57
Switchability Segmentation
Loyal to Us
Losable
Winnable
Customers
Loyal to
Competitor
(business to gain)
Current Product-Market by Switchability
(ABB Procedure)
Questions: Where should your marketing efforts be focused?
How can you segment the market this way?
Segment–58
Using Choice-Based Segmentation
for Database Marketing
A
Customer
Purchase
Probability
B
Average
Purchase
Volume
C
Margin
D
Customer
Profitability
=ABC
1
30%
$31.00
0.70
$6.51
2
2%
$143.00
0.60
$1.72
3
10%
$54.00
0.67
$3.62
4
5%
$88.00
0.62
$2.73
5
60%
$20.00
0.58
$6.96
6
22%
$60.00
0.47
$6.20
7
11%
$77.00
0.38
$3.22
8
13%
$39.00
0.66
$3.35
9
1%
$184.00
0.56
$1.03
10
4%
$72.00
0.65
$1.87
Segment–59
Managerial Uses of Segmentation
Analysis
 Select attractive segments for focused effort (Can use
models such as Analytic Hierarchy Process or GE Planning
Matrix).
 Develop a marketing plan (4P’s and positioning) to target
selected segments.
 In consumer markets, we typically rely on advertising and channel
members to selectively reach targeted segments.
 In business markets, we use sales force and direct marketing. You
can use the results from the discriminant analysis to assign new
customers to one of the segments.
Segment–60
Checklist for Segmentation Studies
 Is it values, needs, or choice-based? Whose values and needs?
 Is it a projectable sample?
 Is the study valid? (Does it use multiple methods and multiple
measures)
 Are the segments stable?
 Does the study answer important marketing questions (product
design, positioning, channel selection, sales force strategy, sales
forecasting)
 Are segmentation results linked to databases?
 Is this a one-time study or is it a part of a long-term program?
Segment–61
Concluding Remarks
In summary,
 Use needs variables to segment markets.
 Select segments taking into account both the
attractiveness of segments and the strengths of the
firm.
 Use descriptor variables to develop a marketing
plan to reach and serve chosen segments.
 Develop mechanisms to implement the
segmentation strategy on a routine basis (one way
to do this is through information technology).
Segment–62
Choosing a
Value Assessment Method
Criterion
Amount of customer
information needed
Number of customers
Good in dynamic/
changing markets?
Past purchase data
available?
Analysis time frame
Cost
Value
Based
Method
Behavior Compositional or
Based Decompositional
Unconstrained
High
Low
Medium
Low
Low
Yes
High
No
Medium+
Partly*
Any
Partly*
Needed
Medium
Medium
Not
necessary
Long/Medium
High
Not
necessary
Short
Low
Medium
No
Moderate
High
Yes
Moderate
Low
No
Low
Not
necessary
Long
Very high/
respondent
Insight
Very high
Appropriate for lead users?
Yes
Predictive of behavior?
High
* If customers can reliably report how they will behave after change.
Segment–63
Related Models Described in the
Marketing Engineering Book
 To develop “needs” variables
 Conjoint Analysis (Chapter 7)
 Other segmentation methods
 Preference-based segmentation (PREFMAP
in Chapter 4)
 To help evaluate and select segments
 Analytic Hierarchy Process (Chapter 6)
 GE Planning Matrix (Chapter 6)
Segment–64