Transcript Segmentation and Targeting Basics Market Definition Segmentation Research and Methods

Segmentation and Targeting
 Basics
 Market Definition
 Segmentation Research and Methods
 Behavior-Based Segmentation
Market Segmentation
• Market segmentation is the
subdividing of a market into
distinct subsets of customers.
Segments
• Members are different between
segments but similar within.
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.
Primary Characteristics
of Segments
• 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)
Industry
Size
Location
Organizational
structure
(Consumer markets)
Age/Income
Education
Profession
Life styles
Media habits
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 macro-segment
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)
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)
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
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.
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
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
Overview of Methods for STP
• Clustering and discriminant
analysis
• Choice-based segmentation
• Perceptual mapping
- later
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. .
.. . .. .
.. . .
.
.
Water Resistance
(Importance)
Distance between
segments C and D
Targeting
Segment(s) to serve
Strength
(Importance)
.. .
. . ....
.
.
.
.. ... .
.. . .
.
.
.. ... .
.. . .
.
.
.. ... .
.. . .
Water Resistance
(Importance)
Positioning
Product Positioning
.. .
Comp 1
Comp 2
Strength
(Importance)
.
.
.. ... .
.. . .
Us
.
.
.. ... .
.. . .
.
.
.. ... .
.. . .
Water Resistance
(Importance)
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:
(“Only product/service with XXX”)
(“More than twice the [feature] vs.
[competitor]”)
3. Similarities (“Same functionality as [competitor];
lower price”)
1. Unique
2. Difference
What are you telling your targeted segments?
Behavior-Based Segmentation
• Traditional segmentation
(eg, demographic,
psychographic)
• Needs-based segmentation
• Behavior-based segmentation
(choice models)
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.
Segmentation: Methods
Overview
• Factor analysis (to reduce data
before cluster analysis).
• Cluster analysis to form segments.
• Discriminant analysis to describe
segments.
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. Recommend:
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).
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
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
•
•
Dimension 1
II
•
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
D = 10
AB =
5.0
ABE= 93.17 CDE = 25.18
ABCDE = 98.8
E = 15
Ward’s Minimum Variance
Agglomerative Clustering
Procedure
98.80
25.18
5.00
0.50
A
B
C
D
E
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.
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
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).
PDA Example
PDA – Segmentation
• Performs Wards method - Code:
proc cluster data=hold.pda method=wards standard
outtree=treedat pseudo;
var Innovator
Use_Message Use_Cell Use_PIM
Inf_Passive Inf_Active
Remote_Acc Share_Inf Monitor
Email Web M_Media Ergonomic Monthly Price;
run;
proc tree data=treedat;
run;
PDA – Segmentation (alternative)
• Performs K-means method - Code:
proc fastclus data=hold.pda maxc=4 maxiter=10 random=41 maxiter=50 out=clus;
var Innovator Use_Message Use_Cell Use_PIM Inf_Passive
Inf_ActiveRemote_Acc Share_Inf Monitor Email Web M_Media Ergonomic ;
run;
proc means data =clus;
var Innovator
Use_Message
Inf_Active
Remote_Acc
M_Media
Ergonomic Monthly
Price;
by cluster;
run;
Use_Cell Use_PIM Inf_Passive
Share_Inf Monitor Email Web
Output
•
•
•
•
•
•
•
•
The following clusters are quite close
together and can be combined with a
small loss in consumer
grouping information:
i) clusters 7 and 5 at 0.27,
ii) clusters 1 and 6 at 0.28, ii)
fused cluster 7-5 and cluster 2 (0.34).
However, when going from a four-cluster
solution to a three-cluster solution, the
distance to be bridged is much larger
(1.11);
thus, the four-cluster solution is indicated
by the ESS.
In addition, four seems a reasonable
number of segments to handle based on
managerial judgment.
Four Cluster Solution – profile code;
proc tree data = treedata nclusters=4 out=outclus no print;
run;
** create new data set;
data temp;
merge hold.pda outclus;
run;
** profile these segments;
proc means data =temp;
var Innovator Use_Message Use_Cell Use_PIM Inf_Passive Inf_Active
Remote_Acc Share_Inf Monitor Email Web M_MedErgonomic Monthly Price;
by cluster;
run;
PDA profiles
G
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PR
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_C
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_M
VA
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F_
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E
IN
NO
PDA Visual profile
2.5
2
1.5
Series1
Series2
1
Series3
Series4
0.5
0
PDA Visual profile…
PRICE
INNOVATOR
2.5
USE_MESSAGE
2
MONTHLY
USE_CELL
1.5
1
ERGONOMIC
USE_PIM
0.5
0
M_MEDIA
INF_PASSIVE
WEB
INF_ACTIVE
EMAIL
REMOTE_ACC
MONITOR
SHARE_INF
Series1
Series2
Series3
Series4
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.
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.
Profile based on Demos/behaviour
Name the segments
Cluster 1 - Sales Pros:
Cluster 1 consists mainly of sales professionals: 54% of the cluster members
indicated Sales as their occupation. They use the cell phone heavily, and many
(45%) own a PDA already; practically all have access to a PC. Their work often
takes them away from the office. They mostly read two of the selected
magazines: 30% read BW. From the needs data, we see that they are quite price
sensitive.
Cluster 2 – Service Pros:
Cluster 2 is made up primarily of service personnel (39%) and secondarily of
sales personnel (23%). They use cell phones heavily, but only about one fifth
currently use a PDA. They spend much time on the road and in remote locations.
They read PC Magazine, 29%. From the needs data, we see that they are quite
price sensitive.
Name the segments…
Cluster 3 – Hard Hats:
Cluster 3 is made up predominantly of construction (31%) and
emergency (19%)
workers. They use cell phones, but usually do not own a PDA.
By the nature of their work, they have high information relay
needs and generally work in remote locations.
They exchange information with colleagues in the field (e.g.
construction workers on the site). Many read Field & Stream
(31%) and also PC Magazine. Note also from the needs data,
that they are the least price sensitive (willing to pay highest price
plus monthly fee) and also have the lowest income.
This apparent anomaly occurs because these folks are less likely
to have to pay for the device themselves, raising the question of
whose preferences—their own or their employers’—will drive
the adoption decision
Name the segments…
Cluster 4 – Innovators:
Cluster 4 represents early adopters (see needs data),
predominantly professionals (lawyers, consultants,
etc.).
Every cluster member has access to a PC, 89
percent already own PDAs.
They read many magazines, especially BW 49%,
PCMag 32%. Most are highly paid and highly
educated.
Who to target…
• Discuss.
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 (i.e., separately evaluate segmentation and
discriminant analysis results).
Discrimination based on
demographics/behaviour
proc discrim data=temp outstat=outdisc method=normal pool=yes list
crossvalidate;
class cluster; priors prop;
vars age education etc… ;
run;
** all relevant vars. not used to create segment solutions;
Discrimination based on
demographics/behaviour
This allows us a way to
target and profile future
customers:
Discrimination based on
demographics/behaviour
Discrimination based on
demographics/behaviour
•The first discriminant function above explains 51% the variation.
According to its coefficients, i.e., the four groups are particularly
different with respect to the amount away from the office.
•In addition, the function shares high correlation with the level of
education, possession of a PDA, and income.
•The second function explains 32% of the variance and primarily
distinguishes the occupation types construction/emergency from
sales/service, and the third function separates Sales and Service
types.
Visualising relationships
Correspondence Analysis
• Provides a graphical summary of the interactions in a table
• Also known as a perceptual map
– But so are many other charts
• Can be very useful
– E.g. to provide overview of cluster results
• However the correct interpretation is less than intuitive,
and this leads many researchers astray
Four Clusters (imputed, normalised)
Usage 9
Usage 7
Usage 8
Usage 4
Cluster 3
Cluster 2
Usage 10
Reason 2
Reason 9
Reason 13
Reason 6
Usage 5
Reason 10
Usage 6
Reason 4
Usage 2
Cluster 1
Usage 1
Reason 12
Usage 3
Reason 11
Reason 7
Reason 3
Reason 5
Reason 14
Cluster 4
Reason 1
Reason 15
25.3%
53.8%
= Correlation < 0.50 2D Fit = 79.1%
Reason 8
Interpretation
• Correspondence analysis plots should be interpreted by
looking at points relative to the origin
– Points that are in similar directions are positively associated
– Points that are on opposite sides of the origin are negatively
associated
– Points that are far from the origin exhibit the strongest associations
• Also the results reflect relative associations, not just which
rows are highest or lowest overall
Software for
Correspondence Analysis
• Earlier chart was created using a specialised package called
BRANDMAP
• Can also do correspondence analysis in most major statistical packages
• For example, using PROC CORRESP in SAS:
*---Perform Simple Correspondence Analysis—Example 1 in SAS OnlineDoc;
proc corresp all data=Cars outc=Coor;
tables Marital, Origin;
run;
*---Plot the Simple Correspondence Analysis Results---;
%plotit(data=Coor, datatype=corresp)
Cars by Marital Status
Segmentations
Other details
Tandem Segmentation
• One general method is to conduct a factor
analysis, followed by a cluster analysis
• This approach has been criticised for losing
information and not yielding as much
discrimination as cluster analysis alone
• However it can make it easier to design the
distance function, and to interpret the results
Tandem k-means Example
proc factor data=datafile n=6 rotate=varimax round reorder flag=.54 scree out=scores;
var reasons1-reasons15 usage1-usage10;
run;
proc fastclus data=scores maxc=4 seed=109162319 maxiter=50;
var factor1-factor6;
run;
• Have used the default unweighted Euclidean distance
function, which is not sensible in every context
• Also note that k-means results depend on the initial cluster
centroids (determined here by the seed)
• Typically k-means is very prone to local maxima
– Run at least 20 times to ensure reasonable maximum
Cluster Analysis Options
• There are several choices of how to form clusters in
hierarchical cluster analysis
–
–
–
–
–
Single linkage
Average linkage
Density linkage
Ward’s method
Many others
• Ward’s method (like k-means) tends to form equal sized,
roundish clusters
• Average linkage generally forms roundish clusters with
equal variance
• Density linkage can identify clusters of different shapes
FASTCLUS
Density Linkage
Cluster Analysis Issues
• Distance definition
– Weighted Euclidean distance often works well, if weights are chosen
intelligently
• Cluster shape
– Shape of clusters found is determined by method, so choose method
appropriately
• Hierarchical methods usually take more computation time than kmeans
• However multiple runs are more important for k-means, since it can be
badly affected by local minima
• Adjusting for response styles can also be worthwhile
– Some people give more positive responses overall than others
– Clusters may simply reflect these response styles unless this is adjusted
for, e.g. by standardising responses across attributes for each respondent
MVA - FASTCLUS
• PROC FASTCLUS in SAS tries to minimise the root mean
square difference between the data points and their
corresponding cluster means
– Iterates until convergence is reached on this criterion
– However it often reaches a local minimum
– Can be useful to run many times with different seeds and choose
the best set of clusters based on this RMS criterion
• See http://en.wikipedia.org/wiki/K-means_clustering for
more k-means issues
Iteration History from FASTCLUS
Relative Change in Cluster Seeds
Iteration
Criterion
1
2
3
4
5
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1
0.9645
1.0436
0.7366
0.6440
0.6343
0.5666
2
0.8596
0.3549
0.1727
0.1227
0.1246
0.0731
3
0.8499
0.2091
0.1047
0.1047
0.0656
0.0584
4
0.8454
0.1534
0.0701
0.0785
0.0276
0.0439
5
0.8430
0.1153
0.0640
0.0727
0.0331
0.0276
6
0.8414
0.0878
0.0613
0.0488
0.0253
0.0327
7
0.8402
0.0840
0.0547
0.0522
0.0249
0.0340
8
0.8392
0.0657
0.0396
0.0440
0.0188
0.0286
9
0.8386
0.0429
0.0267
0.0324
0.0149
0.0223
10
0.8383
0.0197
0.0139
0.0170
0.0119
0.0173
Convergence criterion is satisfied.
Criterion Based on Final Seeds = 0.83824
Results from Different Initial Seeds
19th run of 5 segments
Cluster Means
Cluster
FACTOR1
FACTOR2
FACTOR3
FACTOR4
FACTOR5
FACTOR6
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1
-0.17151
0.86945
-0.06349
0.08168
0.14407
1.17640
2
-0.96441
-0.62497
-0.02967
0.67086
-0.44314
0.05906
3
-0.41435
0.09450
0.15077
-1.34799
-0.23659
-0.35995
4
0.39794
-0.00661
0.56672
0.37168
0.39152
-0.40369
5
0.90424
-0.28657
-1.21874
0.01393
-0.17278
-0.00972
20th run of 5 segments
Cluster Means
Cluster
FACTOR1
FACTOR2
FACTOR3
FACTOR4
FACTOR5
FACTOR6
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1
0.08281
-0.76563
0.48252
-0.51242
-0.55281
0.64635
2
0.39409
0.00337
0.54491
0.38299
0.64039
-0.26904
3
-0.12413
0.30691
-0.36373
-0.85776
-0.31476
-0.94927
4
0.63249
0.42335
-1.27301
0.18563
0.15973
0.77637
5
-1.20912
0.21018
-0.07423
0.75704
-0.26377
0.13729
Howard-Harris Approach
• Provides automatic approach to choosing seeds for kmeans clustering
• Chooses initial seeds by fixed procedure
– Takes variable with highest variance, splits the data at the mean,
and calculates centroids of the resulting two groups
– Applies k-means with these centroids as initial seeds
– This yields a 2 cluster solution
– Choose the cluster with the higher within-cluster variance
– Choose the variable with the highest variance within that cluster,
split the cluster as above, and repeat to give a 3 cluster solution
– Repeat until have reached a set number of clusters
• I believe this approach is used by the ESPRI software
package (after variables are standardised by their range)
Another “Clustering” Method
• One alternative approach to identifying clusters is to fit a
finite mixture model
– Assume the overall distribution is a mixture of several normal
distributions
– Typically this model is fit using some variant of the EM algorithm
• E.g. weka.clusterers.EM method in WEKA data mining package
• See WEKA tutorial for an example using Fisher’s iris data
• Advantages of this method include:
– Probability model allows for statistical tests
– Handles missing data within model fitting process
– Can extend this approach to define clusters based on model
parameters, e.g. regression coefficients
• Also known as latent class modeling
Segmentations via Choice Modelling
Choice Models
1. Observe choice:
(Buy/not buy =>
Brand bought =>
direct marketers
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
Choice Models vs Surveys
With standard survey methods . . .
preference/
importance
choice

weights


predict
observe/ask

perceptions

observe/ask
But with choice models . . .
importance
choice

weights


observe
infer

perceptions

observe/ask
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
Vij
eij
= Utility that customer i has for supplier j’s product.
= Deterministic component of utility that is a function of product and supplier
attributes.
= An error term that reflects the non-deterministic component of utility.
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.
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 = ––––––
k eVik
^
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.
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
Switchability Segmentation
Loyal to Us
Losable
Winnable
Customers
Loyal to
Competitor
(business to gain)
Current Product-Market by Switchability
Questions: Where should your marketing efforts be focused?
How can you segment the market this way?
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
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.
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?
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).