Principles - Ch3 - Segmentation and Targeting

Segmentation and Targeting
 STP (Segmentation,
Targeting, and
Positioning)
 Needs-based
segmentation
 Cluster Analysis
 Discriminant Analysis

Principles Chapter 3: Segmentation and Targeting - 2
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Market Segmentation
 Market segmentation is the subdividing of a
market into distinct subsets of customers.
Segments
 Members are different between segments but
similar within.

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STP is a Core Business Process
 To identify and select groups of potential customers...
 Organizations, Buying Centers, Individuals
 Whose needs within-groups are similar and whose needs between-groups
are different (S)
 Who can be reached profitably (T)
 With a focused marketing program (P)
STP - (Segmentation, Targeting, Positioning) is a
Decision Process

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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.

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Electric Typewriter Market
1980 1981 1982 1983 1984 1985
Shipments
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
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

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1980 1981 1982 1983 1984 1985
Shipments
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 367,057 324,010 343,885 370,374 202,495 129,070
Electronic Word
Processors
60,040 112,220 209,800 392,352 733,699 1,372,016
Total 1,200,040 1,319,942 1,451,986 1,636,082 1,819,778 2,313,086
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 30.6 24.5 23.7 22.6 11.1 5.6
Electronic Word
Processors
5.0 8.5 14.4 24.0 40.3 59.3
Word Processor Market

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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
How STP Creates Value

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The Many Uses of Segmentation
Short term segmentation applications:
 Salesforce allocation/call planning
 Channel assignment
 Communication program
 Pricing
 Today’s competitors and my current relative
advantage to the customer

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The Many Uses of Segmentation
Longer term:
 Emerging needs
 New and evolving market segments to serve
 Planning for segment development/growth
 Not in kind competition/threats (satisfying
customer needs in different ways)
 Lead user identification and management
 Market driving (vs. customer focused)

A Four-Phase Process for Conducting a
Successful Segmentation Project
Internal Assessment
& Planning
• Objective(s) of
segmentation
• Resources
• Constraints
Database
Review
• Primary data
already available
• Secondary data
• …
Prototype
Implementation
Exercises
• What ifs?
• Relevant groups
involved?
• …..
Qualitative
Research
• Interview
Materials
Development


• Qualitative Data
Collection


• “Deep needs”
Identification


• Decision-
Making Process
Assessment


Quantitative
Survey
• Sample Design



• Questionnaire
Development



• Data Collection



Segmentation
Analysis
• Cluster Analysis
• Portfolio Analysis
• Positioning Analysis
Classification Tool
Development
• Discriminant
function
• Binary (CART)
tree
• …
Implementation
Through
Database Tools
• Call Center
• Web
• Sales call
patterns
• Promotion
• ….
Phase I
Planning and Design
Phase II
Qualitative Assessment
Phase III
Quantitative Measurement
Phase IV
Analysis and Implementation
Basic Idea: Do segmentation analysis on a small (random) sample of
customers, but leverage the insights across the entire customer base.

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Needs-Based Segmentation
Distinguish Between Bases and Descriptors
 Bases—characteristics that tell us why segments
differ (e.g., needs, preferences, decision
processes).
 Descriptors—characteristics that help us find and
reach segments.
(Business markets) (Consumer markets)
Industry Age/Income
Size Education
Location Profession
Organizational Life styles
structure Media habits

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Relevant Segmentation Descriptor
Variable A: Climatic Region
1. Snow Belt
2. Moderate Belt
3. Sun Belt
Fraction of
Customers
Likelihood of Purchasing Solar Water Heater
(a)
0 100%
Segment 1
Segment 2
Segment 3

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Likelihood of Purchasing Solar Water Heater
(b)
Irrelevant Segmentation Descriptor
Fraction of
Customers
0 100%
Variable B: Education
1. Low Education
2. Moderate Education
3. High Education
Segment 1
Segment 2
Segment 3

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Variables to Segment
and Describe Markets
Consumer Markets B2B Markets
Segmentation Needs, wants, benefits, Needs, wants, benefits, solutions to
Bases solutions to problems, problems, usage situation, usage rate,
usage situation, usage rate. size*, industry*.
Descriptors Age, income, marital status, Industry, size, location, current
Demographics family type & size, supplier(s), technology utilization,
gender, social class, etc. etc.
Psychographics Lifestyle, values, & Personality characteristics of
personality characteristics. decision makers.
Behavior Use occasions, usage level, Use occasions, usage level,
complementary & complementary & substitute
substitute products used, products used, brand loyalty, order
brand loyalty, etc. size, applications, etc.
Decision Making Individual or group Formalization of purchasing
(family) choice, low or high procedures, size & characteristics
involvement purchase, of decision making group, use of
attitudes and knowledge outside consultants, purchasing
about product class, price criteria, (de)centralizing buying,
sensitivity, etc. price sensitivity, switching costs, etc.
Media Patterns Level of use, types of Level of use, types of media used,
media used, times of use, time of use, patronage at trade shows,
etc. receptivity of sales people, etc.

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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

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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?

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..
.
D
.
.
..
.
.
Segmentation (for Carpet Fibers)
A,B,C,D:
Location of
segment centers.
Typical members:
A: schools
B: light commercial
C: indoor/outdoor
carpeting
D: health clubs
Distance between
segments C and D
.
..
. .
.
Strength
(Importance)
Water Resistance
(Importance)
.....
.
..
A
.....
.
..
. .
.
.
.....
.
..
. .
.
.
...
..
.. C
B
Perceptions/Ratings for one respondent:
Customer Values
.

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.....
.
..
. .
.
.
.....
.
..
.
.
. .
Water Resistance
(Importance)
Targeting
Segment(s) to serve
.....
.
..
.
.
.....
.
..
. .
.
.
Strength
(Importance)

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Example Criteria for Determining
Target Segments to Serve
Criterion Examples of Considerations
I. Size and Growth
1. Size • Market potential, current market penetration
2. Growth • Past growth forecasts of technology change
II. Structural Characteristics
3. Competition • Barriers to entry, barriers to exit, position of
competitors, ability to retaliate
4. Segment saturation • Gaps in the market
5. Protectability • Patentability of products, barriers to entry
6. Environmental risk • Economic, political, and technological change
III. Product-Market Fit
7. Fit • Coherence with company’s strengths and image
8. Relationships with • Synergy, cost interactions, image transfers,
segments cannibalization
9. Profitability • Entry costs, margin levels, return on investment

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Variable Typical Breakdown
Age Under 6, 6-11, 12-19, 20-34, 35-49, 50-64, 65+
Gender Male, female
Family size 1-2, 3-4, 5+
Family life Young, single; young, married, no children; young, married, youngest child
under six;
cycle young, married, oldest child over six; older, married, with children;
older, married, no children under 18; older, single; other
Income Under $10,000; $10,001-$15,000; $15,001-$20,000; $20,001-$30,000;
$30,001-$50,000; $50,001-$100,000; over $100,000
Demographic Segmentation
Examples

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Occupation Professional and technical; managers, officials, and proprietors; clerical,
sales;
craftspeople; farmers; laborers; retired; students; housewives; unemployed
Education Grade school or less; some high school; high school; some college;
college graduate; graduate degree
Religion Catholic, Protestant, Jewish, Muslim, Hindu, other
Race White, black, Asian, Hispanic
Nationality American, British, French, German, Italian, Japanese
Demographic Segmentation
Examples

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Lifestyle Activities, interests, opinions
Personality Compulsive, gregarious, authoritarian,
ambitious
Values Sense of belonging, excitement,
warm relationships with others,
self-fulfillment, security,
being well respected,
fun and enjoyment of life,
self-respect, sense of accomplishment
Psychographic Segmentation
Examples

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Usage situation Regular occasion, special occasion
Benefits Quality, service, economy, speed
User status Nonuser, ex-user, potential user,
first-time user, regular user
Usage rate Light user, medium user, heavy user
Loyalty status None, medium, strong, absolute
Readiness stage Unaware, aware, informed, interested,
desirous, intending to buy
Attitude toward Enthusiastic, positive, indifferent,
product negative, hostile
Behavioral Segmentation
Examples

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Respondent
Selection/Aggregation Issues
 Who makes the purchasing decision:? DMU (decision
making unit)?
 Roles of individuals
 Purchasing agent?
 User?
 Specifier?
 Gatekeeper?
 Financial analyst?
 How many respondents per unit?
 If more than one, how to aggregate?

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4. Define Market Segments
 Assume the following data matrix includes the responses from six customers on four
key components of value (10 point rating scale of importance)
 How would you segment this “market”?
Customer
Importance
of
Durability
Importance
of Service
Importance
of Ease of
Use
Importance
of Price
Cluster
Assignment
1 9 4 5 6
2 3 5 10 5
3 4 5 5 10
4 10 6 7 6
5 5 5 7 10
6 6 3 9 5
“Market” 6.2 4.7 6.3 7.0 X

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Segmentation Process Summary
1. Articulate a strategic rationale for segmentation (i.e., why are
we segmenting this market?).
2. Select a set of needs-based segmentation variables most
useful for achieving the strategic goals.
3. Select a cluster analysis procedure for aggregating (or
disaggregating customers) into segments.
4. Group customers into a defined number of different segments
and describe them…and target them with descriptor data.
5. Target the segments that will best serve the firm’s strategy,
given its capabilities and the likely reactions of competitors.

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Segmentation: Methods Overview
 Factor analysis (to reduce data before cluster
analysis).
 Cluster analysis to form segments.
 Discriminant analysis to describe segments.

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Cluster Analysis for
Segmenting Markets
 Select variables for analysis – these should be based on their
potential for providing meaningful ways to define the needs of
customers.
 Define an overall measure to assess the similarity of customers
on the basis of the needs variables.
 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).

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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.
 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).

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Doing Cluster Analysis
Dimension 2
Dimension 1
•
•
•
•
•
•
•
•
•
•
• •
Perceptions or ratings data
from one respondent
III
a
I II
b
a = distance from
member to cluster
center
b = distance from I to III

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Cluster Analysis: Measures of
Association
 Choosing Variables
 Variables with similar values
 Variables with different values
 Defining Measures of similarity between individuals
 Scaled data  distance type measures
 Nominal data  matching-type measures
 Mixed data  Automatic Interaction

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Matching Coefficients
Essential Features? (Yes or No)
F1 F2 F3 F4 F5 F6 F7 F8
Org.
A
Y Y N N Y Y Y Y
Org.
B
N Y N N N Y Y Y
Org.
C
Y N Y Y Y N N N
Org.
D
Y N N N Y Y Y Y

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________________
Number of Matches
Total Possible Matches
Similarity Coefficient
=
A B C D
A 1
B 6/8 1
C 2/8 0/8 1
D 7/8 5/8 3/8 1

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Distance Measures
 Measures of Dissimilarity
 Euclidean distance
 Absolute distance
 Measures of Similarity
 Correlation coefficient rxy
2
2
1
1 )
(
....
)
( nj
ni
j
i x
x
x
x 




nj
ni
j
i x
x
x
x 



 ......
1
1

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Euclidean Distance Measure
Scaling Problem
Standardize Data : To subtract mean and
divide by Std Dev
2
2
1
1 )
(
...
)
(
tan nj
ni
j
i x
x
x
x
ce
is
EuclideanD 





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Euclidean Distance Measure
Income
(Thousands)
Age
(years)
A 34 27
B 23 34
C 55 38
A B C
A 1.00
B 13.04 1.00
C 23.71 32.25 1
mean 37.33 33.00
Std Dev 16.26 5.57
Income
(Thousands)
Age
(years)
A -0.205 -1.078
B -0.882 0.180
C 1.087 0.898
A B C
A 1.00
B 1.43 1.00
C 2.36 2.10 1
 Normal Data Standardized data
 Euclidean Distance

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Scaling Problem
Income
(Thousands)
Age
(Months)
A -0.205 -1.078
B -0.882 0.180
C 1.087 0.898
A B C
A 1.00
B 1.43 1.00
C 2.36 2.10 1
Income
(Thousands)
Age
(Months)
A 34 324
B 23 408
C 55 456
A B C
A 1.00
B 84.72 1.00
C 133.66 57.69 1
 Normal Data Standardized data
 Euclidean Distance
mean 37.33 33.00
Std Dev 16.26 5.57

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Clustering Methods
 Hierarchical Methods
 Build-up (Agglomerative)
► Joining clusters using:
• Single Linkage
• Complete
• Average
• Ward’s
 Split-down (Divisive)
► Automatic Interaction Detection (AID)
 Partitioning Methods
 K-means clustering

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 The single linkage method is based on minimum distance, or the
nearest neighbor rule. At every stage, the distance between two
clusters is the distance between their two closest points.
 The complete linkage method is similar to single linkage, except
that it is based on the maximum distance or the furthest neighbor
approach. In complete linkage, the distance between two
clusters is calculated as the distance between their two furthest
points.
 The average linkage method works similarly. However, in this
method, the distance between two clusters is defined as the
average of the distances between all pairs of objects, where one
member of the pair is from each of the clusters.
Conducting Cluster Analysis
Select a Clustering Procedure – Linkage Method

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Linkage Methods of Clustering
Single Linkage
Minimum Distance
Complete Linkage
Maximum Distance
Average Linkage
Average Distance
Cluster 1 Cluster 2
Cluster 1 Cluster 2
Cluster 1 Cluster 2

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Other Agglomerative Clustering Methods
Ward’s Procedure
Centroid Method

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Single Linkage Cluster Example
Distance Matrix
Co#1 Co#2 Co#3 Co#4 Co#5
Company #1 0.00
Company #2 1.49 0.00
Company #3 3.42 2.29 0.00
Company #4 1.81 1.99 1.48 0.00
Company #5 5.05 4.82 4.94 4.83 0.00 Resulting
Dendogram
1
2
3
4
5
1 2 3 4 5
Company
Distance

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Ward’s Clustering Procedure
First Stage: A = 2 B = 5 C = 9 D = 10 E = 15
Second Stage: AB = 4.5 BD = 12.5
AC = 24.5 BE = 50.0
AD = 32.0 CD = 0.5
AE = 84.5 CE = 18.0
BC = 8.0 DE = 12.5
Third Stage: CDA = 38.0 CDB = 14.0 CDE = 20.66 AB = 5.0
AE = 85.0 BE = 50.5
Fourth Stage: ABCD = 41.0 ABE= 93.17 CDE = 25.18
Fifth Stage: ABCDE = 98.8

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A
98.80
Ward’s Minimum Variance
Agglomerative Clustering Procedure
B C D E
25.18
5.00
0.50

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Segmentation Example
 The single linkage method was used to create the following
dendogram. Group the elements into segments as follows:
1. Two segments. List elements in each segment
2. Three segments. List elements in each segment
3. Five segments. List elements in each segment

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Segmentation Example

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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 we come up with descriptive names for each cluster (e.g., professionals,
techno-savvy, etc.)?
 Typically, the market is segmented first, and then you
independently evaluate your ability to reach the segments (i.e.,
separately evaluate segmentation and discriminant analysis results).
However, such an approach may not necessarily result in segments
that can be targeted.

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Profiling Clusters
 Give a description of the clusters based on:
 Variables used for clustering (bases)
 Variables withheld from clustering, but will be
used to identify and target segments (the
descriptors)
 Report the average value of both

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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.

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 Discriminant score (Z-score) – basis for predicting to which
group the particular individual belongs and is determined by a
linear function. Each respondent is assigned a score by the
calculated discriminant function.
Zi = b1X1i + b2X2i ⋅ ⋅ ⋅ + bnXni
 Zi = ith individual’s discriminant score
 bn = discriminant coefficient for the nth variable
 Xni = individual’s value on the nth independent
variable
Analysis of Dependence
17-50

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Behavior-Based Segmentation:
Choice Models
Expected (gross) customer profitability =
Probability of purchase
x Likely purchase volume if a purchase is made
x Profit margin (for this customer)

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Behavior-Based Segmentation:
Choice Models
A B C D
Average Customer
Purchase Purchase Profitability
Customer Probability Volume Margin = 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

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A Key Result from this Specification:
The Multinomial Logit (MNL) Model
eVij
pij = ––––––
 eVik
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.
^
^
^

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Key idea: Segment on the basis of probability of
choice—
1. Loyal to us
2. Loyal to competitor
3. Switchables: loseable/winnable customers
Applying the MNL Model in
Segmentation Studies

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Switchability Segmentation
Current Product-Market by Switchability
(ABB Procedure)
Questions: Where should your marketing efforts be
focused?
How can you segment the market this way?
Loyal to Us Losable
Winnable
Customers
(business to gain)
Loyal to
Competitor

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Switchability Segmentation
 Loyal, if the firm is a current customer of and its
probability of choosing us is greater than 0.80.
 Competitive, if the firm is a current customer of us
and its choice probability is less than 0.80.
 Lost, if the firm is: (1) not a current customer, and (2)
its probability of buying from one of the competitors
is greater than 0.80, or its probability of buying from
ABB is less than 0.15.
 Switchable, if none of the above applies.

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Cus.
ID
Purchase
Volume
($)
District Choice
A
(US)
B C D Type
1 761 1 B 15.30% 82.27% 2.42% 0.01%
Loyal to
B
2 627 1 D 0.00% 0.00% 2.61% 97.39%
Loyal to
D
3 643 2 A 74.70% 25.29% 0.01% 0.00% Comp
4 562 3 D 48.79% 39.73% 0.00% 11.48% Losable
5 469 3 C 1.97% 0.01% 98.02% 0.00%
Loyal to
C
6 233 1 B 0.01% 96.85% 3.09% 0.04%
Loyal to
B
7 664 3 D 40.47% 7.69% 0.08% 51.76% Winnable

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Cus.
ID
Purchase
Volume
($)
District Choice
A
(US)
B C D Type
1 761 1 B 15.30% 82.27% 2.42% 0.01%
2 627 1 D 0.00% 0.00% 2.61% 97.39%
3 643 2 A 74.70% 25.29% 0.01% 0.00%
4 562 3 D 48.79% 39.73% 0.00% 11.48%
5 469 3 C 1.97% 0.01% 98.02% 0.00%
6 233 1 B 0.01% 96.85% 3.09% 0.04%
7 664 3 D 40.47% 7.69% 0.08% 51.76%

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Profiling Clusters
Two Cluster Solution for PC Data: Need-Based Variables
size power office
use
LAN storage
needs
color periph.
wide
connect.
budget
–1
1
0
Business
Design
Means of
Variables

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PDA Visual profile
0
0.5
1
1.5
2
2.5
INNO
VATO
R
USE_M
ESSAG
E
USE_CELL
USE_PIM
INF_PASSIVE
INF_ACTIVE
REM
O
TE_ACC
SHARE_INF
M
O
NITO
R
EM
AIL
W
EB
M
_M
EDIA
ERG
O
N
O
M
IC
M
O
NTH
LY
PRICE
Series1
Series2
Series3
Series4

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PDA profiles
 Cluster 1. Phone users who use Personal
Information Management software, to whom
Email and Web access, as well as Multimedia
capabilities are important.
 Cluster 2. People who use messaging services and
cell phones, need remote access to information,
appreciate better monitors, but not for multi-media
usage.

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PDA profiles..
Cluster 3. Pager users who have a high need for fast
information sharing (receiving as well as sending)
and also remote access. They use neither email
extensively, nor the Web, nor Multi-media, but do
require a handy, non-bulky device.
Cluster 4. Innovators who use cell phones a lot, have
a high need for Email, Web, and Multi-media use.
They also require a sleek device.

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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.

© DecisionPro 2007
Two-Group Discriminant Analysis
Firm Profitability
Number of
Employees
XXOXOOO
XXXOXXOOOO
XXXXOOOXOOO
XXOXXOXOOOO
XXOXOOOOOOO
X-segment
O-segment
x = High need for wireless internet
o = Low need for wireless internet
Discriminant function

© DecisionPro 2007
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).

© DecisionPro 2007
Successful Segmentation Results in a
Simple-to-Use Targeting Tool
Classify a potential customer/target into segments.
Discriminant Function Approach
Compute discriminant function scores for each customer/ prospect on
each discriminant function, and assign the customer to the segment
that has its centroid closest to that customer.
Other Approaches (e.g., Tree-based) for Assignment
Classification and Regression Trees (CART)

© DecisionPro 2007
Obtaining Data for Needs-Based
Segmentation Studies
 Conduct traditional marketing research surveys for
gathering needs data from a random sample of
respondents. Often, a qualitative study (e.g., focus
group) is conducted to determine the appropriate
needs variables.
 Gather data through a “Conjoint” study.
 Obtain descriptor data through a combination of
secondary and primary data sources.

© DecisionPro 2007
A Good Segmentation Study …
 Identifies segments of customers with differentiated needs.
 How many different segments?
 How do their needs differ?
 Enables segments to be separately targeted/reached (this can be
problematic even if segments have distinctly different needs).
 Finds one or more attractive segments (i.e. a profitable and
separate marketing program can be designed for selected
segments).
 Facilitates the implementation of the segmentation scheme as an
ongoing process, not a discrete project.

© DecisionPro 2007
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).

Principles - Ch3 - Segmentation and Targeting

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