MANOJ FINAL PPT welingkar institution of mumbai

project report

1. SEGMENTATION AND
TARGETING
Basics
Market Definition
Segmentation Research
and Methods
Behavior-Based
Segmentation
PROJECT REPORT FINAL REPORT
Presented by:-
NAME OF STUDENT:
ROLL NO:
Batch:

2. Market Segmentation
Market segmentation is the
subdividing of a market into distinct
subsets of customers.
Segments
Members are different between
segments but similar within.

3. 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.

4. 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)(Consumer markets)
Industry Age/Income
Size Education
Location Profession
Organizational Life styles
structure Media habits

5. 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

6. 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

7. 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

8. Variables to Segment
and Describe Markets
Consumer Industrial
Segmentation Needs, wants benefits, Needs, wants benefits, solutions to
Bases solutions to problems, problems, usage situation, usage rate,
usage situation, usage rate.size*, industrial*.
Descriptors Age, income, marital status,Industry, size, location, current
Demographicsfamily type & size, supplier(s), technology utilization,
gender, social class, etc.etc.
PsychographicsLifestyle, 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 MakingIndividual or group Formalization of purchasing
(family) choice, low or highprocedures, size & characteristics
involvement purchase, of decision making group, use of
attitudes and knowledgeoutside consultants, purchasing
about product class, pricecriteria, (de) centralizing buying,
sensitivity, etc. price sensitivity, switching costs, etc.
Media PatternsLevel 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.

9. 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)]

10. 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

11. 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

12. Overview of Methods for STP
Clustering and discriminant
analysis
Choice-based segmentation
Perceptual mapping
- later

13. .
.
.
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)
....
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.
.
.
A
....
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....
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.
..
.
....
C
B
Perceptions/Ratings for one respondent:
Customer Values
.

14. ....
.
.
.
.
.
.
.
.
....
.
.
.
.
.
.
..
Water Resistance
(Importance)
Targeting
Segment(s) to serve
....
.
.
.
.
..
....
.
.
.
.
.
.
.
.
Strength
(Importance)

15. Product Positioning
.
Comp 1
Comp 2
Us
Water Resistance
(Importance)
Positioning
..
Strength
(Importance)
....
.
.
.
.
.
.
.
.
....
.
.
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..
....
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.

16. 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?

17. Behavior-Based
Segmentation
Traditional segmentation
(eg, demographic,
psychographic)
Needs-based segmentation
Behavior-based segmentation
(choice models)

18. 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.

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

20. 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).

21. 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

22. 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

23. Ward’s Minimum Variance
Agglomerative Clustering
ProcedureFirst 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 =38.0CDB =14.0CDE =20.66AB =5.0
AE =85.0 BE =50.5
Fourth Stage: ABCD =41.0ABE=93.17CDE =25.18
Fifth Stage: ABCDE =98.8

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

25. 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.

26. Two-Group Discriminant
Analysis
Need for Data Storage
Price
Sensitivity
XXOXOOO
XXXOXXOOOO
XXXXOOOXOOO
XXOXXOXOOOO
XXOXOOOOOOO
X-segment
O-segment
x = high propensity to buy
o = low propensity to buy

27. 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).

28. PDA Example

29. PDA – Segmentation
Performs Wards method - Code:
proc cluster data=hold.pda method=wards standard
outtree=treedat pseudo;
var InnovatorUse_Message Use_Cell Use_PIM
Inf_Passive Inf_ActiveRemote_Acc Share_Inf
Monitor Email Web M_Media Ergonomic Monthly
Price;
run;
proc tree data=treedat;
run;

30. 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 InnovatorUse_Message Use_Cell Use_PIM
Inf_Passive Inf_ActiveRemote_AccShare_Inf Monitor Email
Web M_Media
Ergonomic MonthlyPrice;
by cluster;
run;

31. 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.
0.27
0.28
0.34
1.11
1.45
3.13
76 54 321 Cluster
(not to scale)
Distance

32. 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_ActiveRemote_Acc
Share_Inf Monitor Email Web M_MedErgonomic MonthlyPrice;
by cluster;
run;

33. PDA profiles

34. PDA Visual profile
0
0.5
1
1.5
2
2.5
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L Y
P
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Series1
Series2
Series3
Series4

35. PDA Visual profile…
0
0.5
1
1.5
2
2.5
INNOVATOR
USE_MESSAGE
USE_CELL
USE_PIM
INF_PASSIVE
INF_ACTIVE
REMOTE_ACC
SHARE_INFMONITOR
EMAIL
WEB
M_MEDIA
ERGONOMIC
MONTHLY
PRICE
Series1
Series2
Series3
Series4

36. 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.

37. 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.

38. Profile based on
Demos/behaviour

39. 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.

40. 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

41. 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.

42. Who to target…
Discuss.

43. 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).

44. 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;

45. Discrimination based on
demographics/behaviour
This allows us a way to
target and profile
future customers:

46. Discrimination based on
demographics/behaviour

47. 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.

48. Visualising relationships

49. 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

50. Reason 1
Reason 2
Reason 3
Reason 4
Reason 5
Reason 6
Reason 7
Reason 8
Reason 9
Reason 10
Reason 11
Reason 12
Reason 13
Reason 14
Reason 15
Usage 1
Usage 2
Usage 3
Usage 4
Usage 5
Usage 6
Usage 7
Usage 8
Usage 9
Usage 10
Cluster 1
Cluster 2
Cluster 3
Cluster 4
25.3%
53.8%
2D Fit = 79.1%
Four Clusters (imputed, normalised)
= Correlation < 0.50

51. 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

52. 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)

53. Cars by Marital Status

54. Segmentation
s
OTHER DETAILS

55. 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

56. 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

57. 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

58. FASTCLUS

59. Density Linkage

60. 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 k-means
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

61. 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

62. Iteration History from
FASTCLUS
Relative Change in Cluster Seeds
Iteration Criterion 1 2 3 4 5
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
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

63. Results from Different Initial
Seeds
19th run of 5 segments
Cluster Means
Cluster FACTOR1 FACTOR2 FACTOR3 FACTOR4 FACTOR5 FACTOR6
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
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
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
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

64. Howard-Harris Approach
Provides automatic approach to choosing seeds
for k-means 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)

65. 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

66. Segmentations via Choice
Modelling

67. 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

68. Choice Models vs Surveys
With standard survey methods . . .
preference/ importance
choice  weightsperceptions
  
predict observe/ask observe/ask
But with choice models . . .
importance
choice  weights  perceptions
  
observe infer observe/ask

69. 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.
U
ij
= V
ij
+ 
ij
where:
U
ij=Utility that customer i has for supplier j’s product.
V
ij
=Deterministic component of utility that is a function of product and
supplier attributes.

ij =An error term that reflects the non-deterministic component of utility.

70. Specification of the
Deterministic Component of
Utility V
ij =  W
k b
ijk
k
where:i=an index to represent customers, j is an index to
represent suppliers, and k is an index to represent
attributes.
b
ijk=i’s perception of attribute k for supplier j.
w
k
=estimated coefficient to represent the impact of b
ijk

on the utility realized for attribute k of supplier j for
customer i.

71. 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 (
ij) has a specific form
called double exponential, then:
e
Vij
p
ij=––––––

k e
Vik

where:
p
ij=probability that customer i chooses supplier j.
V
ij=estimated value of utility (ie, based on estimates
of b
ijk) obtained from maximum likelihood estimation.
^
^
^

72. 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

73. Switchability
Segmentation
Current Product-Market by Switchability
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

74. Using Choice-Based Segmentation for
Database Marketing
A B C D
Average Customer
Purchase Purchase Profitability
CustomerProbabilityVolume 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

75. 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.

76. 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?

77. 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).