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Segmentationand Targeting
Segmentation and targeting
Outline The segmentation-targeting-positioning
(STP) framework
Segmentation
□ The concept of market segmentation
□ Managing the segmentation process
□ Deriving market segments and describing the segments
Cluster analysis
Discriminant analysis
Targeting
Segmentation and targeting
STP – Segmentation, Targeting,
Positioning
Product
Price
Communication
Distribution
All consumers
in the market
Target
market
segment(s)
Mar
ket
ing m
ix
Marketing strategies
of competitors
Target marketing
and positioning
Segmentation and targeting
How STP creates value
More focused marketing efforts can better meet
customer needs
Customers develop preferences for offerings that
deliver greater value and satisfaction
Customers become loyal to the brand and the firm if
the brand/firm provides value and satisfaction
Loyalty leads to greater market share and insulates
the firm against competition
Profitability increases
Segmentation and targeting
Motivation for market segmentation
“One size fits all” usually doesn’t work (all
potential customers are not created equal)
Segment-of-one marketing is often not feasible
(costs outweigh the benefits)
Compromise: Market segmentation
Segmentation and targeting
Market segmentation
Partitioning a market that is characterized
by heterogeneity in customers’ response
to the marketing mix into more homo-
geneous submarkets.
Segmentation and targeting
Segmentation bases
General Product-specific
Observable
Latent
Observable features
of the physical and
social environment
(esp. demographics)
Values, lifestyles and
psychographics,
personality variables
Awareness
Product attributes and
benefits
Willingness to buy
Behavioral characteris-
tics (user status, loyalty
status, usage rate)
Usage situations
Segmentation and targeting
Problems with many segmentations
Markets can be segmented on the basis of lots of
different variables, but it’s unlikely that many of these
variables capture differences in response to the
marketing mix;
Product-specific segmentation bases are usually better
indicators of differences in customer response than
general segmentation bases;
Particularly motivational variables (purchase motivations,
customer needs, benefits sought) are important for
segmentation;
However, they are not directly observable, so they have
to be supplemented with managerially useful descriptors
that characterize the segments;
Segmentation and targeting
Segmentation criteria The essence of market segmentation:
□ market response is homogeneous within segments and
heterogeneous between segments (differentiability)
□ individuals can be assigned to a segment based on a
meaningful profile of segment characteristics
(identifiability)
Additional requirements:
□ the size and purchasing power of relevant segments can
be determined (measurability)
□ the company is able to develop a marketing mix that will
appeal to the members of a given segment (actionability)
□ members of a segment can be reached with the
appropriate marketing mix (accessibility)
□ segments and segment membership do not change in the
short run (stability)
Segmentation and targeting
Differences in customer response
marketingvariable
Response
Segment B
Segment AA1
A2
B1
B2
x1x2
Who’s
this?
Who’s
this?
Segmentation and targeting
Segmentation bases (cont’d)
Use product-specific segmentation bases to derive
segments (segmentation variables): difference in
response is key
Use general segmentation bases to profile the
segments (discriminant variables): identifiability is
key
Segmentation and targeting
Managing the segmentation process
Define the segmentation problem
□ Objectives, resources, and constraints
Identify data needs
□ Primary vs. secondary data
□ Sample definition (category users, existing customers, heavy vs.
light users, loyals vs. switchers)
□ Segmentation and discriminant variables (based on available data
and/or qualitative research)
Conduct the segmentation study and analyze the data
□ Step 1: Derive the market segments (cluster analysis)
□ Step 2: Describe the market segments (discriminant
analysis)
Implement the results
Segmentation and targeting
Step 1: Deriving market segments
The idea is to group (potential) customers who are
similar in their response to some element of the
marketing mix (e.g., response to different product
features, including price; response to advertising or
promotions; response to different distribution channels)
Choose segmentation variables that capture relevant
response differences, which can eventually be used to
position the firm’s offering to the “right” customers;
Assume that we have data for a relevant sample of
customers on a set of segmentation variables of interest;
how can we do a segmentation analysis?
Segmentation and targeting
Observations /
Segmentation Variables
R1 10 10
R2 8 9
R3 5 6
R4 6 5
R5 3 3
R6 1 2
A simple segmentation example:
Preferences of 6 consumers for 2 attributes of beer
Segmentation and targeting
A simple segmentation example:
Preferences of 6 consumers for 2 attributes of beer
Segmentation and targeting
A simple segmentation example:
Preferences of 6 consumers for 2 attributes of beer
Segmentation and targeting
Actual segments in the beer market
(based on Consumer Reports)
bitterless more
Craft ales
Craft lagers
Imported lagers
N.A. beerRegular and ice beer
Light beers
Segmentation and targeting
Segmentation in the real world
In practice, we have
□ Many potential customers
□ Many segmentation variables
What to do?
Custer analysis to the rescue!
Segmentation and targeting
Cluster analysis Basic question: How can objects (customers,
brands, stores, etc.) be grouped such that objects
within the same cluster are similar and objects in
different clusters are dissimilar?
In segmentation, the objects of interest are
customers and similarity is assessed in terms of
relevant segmentation variables;
Issues in cluster analysis:
□ How is similarity measured?
□ How are clusters formed?
□ How many clusters should be distinguished?
□ How should the clusters be interpreted?
Segmentation and targeting
How is similarity measured?
Overall measures of similarity [not relevant here]
□ Direct measures of overall similarity
□ Indirect measures of overall similarity (e.g., switching
data)
Derived measures of similarity (e.g., based on
preferences for certain benefits)
□ Metric data
Correlational measures (e.g., similarity in the profile of
ratings across certain benefits)
Distance measures (Euclidean, city-block)
□ Non-metric data
Matching coefficients (i.e., extent to which customers want
the same features in a product)
Segmentation and targeting
Euclidean distance
𝑑𝑅1𝑅6 = (𝑋2 − 𝑋1)2+(𝑌2 − 𝑌1)
2
R1
R6
Segmentation and targeting
Similarity data as input to cluster analysis
R1 R2 R3 R4 R5 R6
R1 --
R2 S21 --
R3 S31 S32 --
R4 S41 S42 S43 --
R5 S51 S53 S53 S54 --
R6 12.04 S63 S63 S64 S65 --
Segmentation and targeting
How are clusters formed? Hierarchical cluster procedures: result in a tree-like (nested)
structure that can be represented in a dendrogram;
□ Agglomerative (bottom-up) methods: initially there are as many
clusters as objects and then objects are combined;
Single linkage
Complete linkage
Average linkage
Centroid method
Ward’s method
□ Divisive (top-down) methods: initially there is one large cluster
that is subsequently divided into smaller clusters;
Non-hierarchical cluster (partitioning) procedures:
□ K-means clustering: an initial partition into G groups is chosen
and objects are reassigned if the total error can be reduced;
solutions for different G are analyzed;
Segmentation and targeting
Agglomerative methods
Single linkage: similarity is based on the shortest distance
between any two points in two clusters (nearest-neighbor
approach); at each step, the most similar clusters are joined;
Complete linkage: similarity is based on the largest distance
between any two points in two clusters (farthest-neighbor
approach);
Average linkage: similarity is based on the square root of the
average of the squared distances of all objects in two clusters;
Centroid method: similarity is based on the distance between
the centroids of the clusters;
Ward’s method: clusters are formed such that the increase in
within-group variability is minimized;
Segmentation and targeting
Which two of these three clusters should be joined in the next step
based on single linkage? Complete linkage? Average linkage? The
centroid method? Ward’s method?
Hierarchical agglomerative methods
Segmentation and targeting
For this three-cluster solution, can the total error be
reduced by reassigning a respondent to a different cluster?
Non-hierarchical clustering
Segmentation and targeting
How many clusters should be formed?
No generally accepted stopping rule is available;
In a hierarchical cluster solution, inspect the
dendrogram (tree graph), which shows the distance
(dissimilarity) at which two clusters are joined;
Look for the point in the dendrogram where
combining two clusters results in a large increase in
the within-cluster heterogeneity;
Ultimately, a cluster solution should be practically
useful; try out different solutions and choose the one
that is most interpretable and yields the most
actionable insights.
Segmentation and targeting
Dendrogram
Segmentation and targeting
How should the clusters be interpreted
Compute the average score of the cluster members
on the clustering variables used to compute the
similarity measure.
Name the clusters!
If additional variables not used during clustering are
available for each of the objects, use these variables
to further profile and differentiate the clusters.
Segmentation and targeting
Cluster averages for maltiness and bitterness:
Name the clusters!
Cluster 1 Cluster 2 Cluster 3
Maltiness 2.0 5.5 9.0
Bitterness 2.5 5.5 9.5
Segmentation and targeting
Special problems in cluster analysis
Clustering variables:
□ The final cluster solution depends strongly on the variables that
were included in the cluster analysis. Clustering variables have
to be chosen carefully.
□ If clustering variables are very similar, this may exaggerate the
influence of the underlying common factor. If some variables are
highly correlated, it may be better to combine these variables
prior to clustering.
Outliers: Unusual observations can greatly distort the final solution
obtained in the analysis. Check for outliers before doing the
analysis. Outliers can also be detected in the dendrogram.
Standardizing the data: Variables with large variances have a
disproportionate influence on similarity. If the clustering variables are
measured on different scales, standardize the data (usually by
variable, but possibly by observation).
Segmentation and targeting
Office Star data
40 respondents rated the importance of 6 attributes
when choosing an office supply store: variety of choice,
(availability of) electronics, (availability of) furniture,
quality of service, low prices, and return policy;
Importance was rated on a scale from 0 (not at all
important) to 10 (extremely important);
Data on three descriptor variables are also available:
whether or not the respondent is a professional, the
respondent’s income, and the respondent’s age;
Data on these three descriptor variables are also
available for an additional 300 respondents for whom no
segmentation data were collected;
Segmentation and targeting
Segmentation and targeting
Dis
tance
Cluster ID1 7 4 8 2 9 5 3 6
16.51
18.08
18.33
21.52
25.07
36.19
348.59
537.17
Using ME for segmentation:
Office Star data with 9 clusters
a
b
c
d
Segmentation and targeting
3-cluster solution for Office Star dataCluster SizesThe following table lists the size of the population and of each segment, in both absolute and relative terms.
Size / Cluster Overall Cluster 1 Cluster 2 Cluster 3
Number of observations 40 18 14 8
Proportion 1 0.45 0.35 0.2
Segmentation VariablesMeans of each segmentation variable for each segment.
Segmentation variable / Cluster
Overall Cluster 1 Cluster 2 Cluster 3
Variety of choice 7.53 9.11 6.93 5.00Electronics 4.57 6.06 2.79 4.38Furniture 3.45 5.78 1.43 1.75Quality of service 4.00 2.39 3.50 8.50Low prices 5.05 3.67 8.29 2.50Return policy 4.50 3.17 6.29 4.38
Segmentation and targeting
0
1
2
3
4
5
6
7
8
9
10
Variety of choice Electronics Furniture Quality of service Low prices Return policy
Means of segmentation variables by cluster and overall
Overall Cluster 1 Cluster 2 Cluster 3
Segmentation and targeting
Assignment for next week
LRB Chapter 3
Segmentation and Classification Tutorial (ME)
GE Tutorial (ME)
Office Star examples
Segmentation and targeting
Step 1: (R1&R2) vs. R3 vs. R4
Step 2: (R1&R2) vs. (R3&R4)
Step 3: (R1&R2) & (R3&R4)
Step 1: (R1&R2) vs. R3 vs. R4
Step 2: (R1&R2) vs. (R3&R4)
Step 3: (R1&R2) & (R3&R4)
Recap: Cluster analysis
(1) Calculate similarities (or differences) between objects
(2) Derive clusters
Segmentation and targeting
Recap: Cluster analysis (cont’d)
(3) Choose the number of clusters based on the dendrogram
(4) Interpret the clusters
Seg 1 Seg 2
Tartar control 9.5 2.5
Whitening 1.5 10.0
Seg 1 Seg 2 Seg 3
Tartar control 9.5 9.0 1.0
Whitening 1.5 10.0 10.0
Segmentation and targeting
Step 2: Describing market segments
In order to make the segmentation actionable, the
market segments have to be profiled (particularly if
the segmentation variables are not directly
observable);
The segmentation study should include readily
observable variables that can be used to
characterize the segments;
The goal is to find actionable variables that are
useful for predicting customers’ segment
membership;
One technique for doing this is discriminant analysis;
Segmentation and targeting
Discriminant analysis
Basic question: How can we explain or predict the
group (segment) membership of an object
(customer) based on certain (metric) independent
variables (classification), and how can we determine
which variables differentiate between the groups
(profiling)?
Issues in discriminant analysis:
□ How can groups (segments) be differentiated based
on many variables?
□ How can we assess the overall quality of
discrimination?
□ Which variables are most effective in discriminating
between the groups (segments)?
Segmentation and targeting
Two-group discriminant analysis
Two equivalent approaches:
□ Find a linear combination of the independent
(discriminant) variables such that the resulting
discriminant scores ti are maximally different across
the two groups:
𝑡𝑖 = 𝑐1𝑥1𝑖+𝑐2𝑥2𝑖+ … +𝑐𝑝𝑥𝑝𝑖
□ Find the locus of points that are equidistant from the
centroid (mean) of the two groups;
Assign a customer to the group to which it’s closest;
Segmentation and targeting
Discriminant scores based on x1 only
Cluster 1
Cluster 2
Segmentation and targeting
Discriminant scores based on x1 and x2
Cluster 1
Cluster 2
Segmentation and targeting
Equidistant points
Cluster 1
Cluster 2
Segmentation and targeting
Two-group discriminant analysis (cont’d)
To assess the overall quality of discrimination we can
use a hits-and-misses table (confusion matrix):
To assess classification accuracy, we need a benchmark
for chance prediction:
The proportional chance criterion: 𝑝2 + (1 − 𝑝)2
[where p is the proportion of observations in group 1]
Predicted
Group1 Group2
ActualGroup1 Correct Incorrect
Group2 Incorrect Correct
Segmentation and targeting
Example of two-group discriminant analysis
with two classification variables
Discrimination DataData used for discrimination
Variables / Observations Clusterx1
Age group (younger to older)
x2Level of education
(low to high)
1 1 1 3
2 1 1 5
3 1 2 4
4 1 5 25 2 2 86 2 4 87 2 5 68 2 6 4
9 2 7 710 2 8 5
Segmentation and targeting
Assessing the quality of discrimination
Confusion Matrix
Comparison of cluster membership predictions based on discriminant dataand actual cluster memberships. High values in the diagonal of the confusion matrix (in bold) indicate that discriminant data is good at predicting cluster membership.
Actual / Predicted cluster Cluster 1 Cluster 2
Cluster 1 4 0
Cluster 2 0 6
Actual / Predicted cluster Cluster 1 Cluster 2
Cluster 1 100.00% 00.00%
Cluster 2 00.00% 100.00%
Hit Rate (percent of total cases correctly classified) 100.00%
Cluster SizesThe following table lists the size of the population and of each segment, in both absolute and relative terms.
Size / Cluster Overall Cluster 1 Cluster 2
Number of observations 10 4 6
Proportion 1 0.4 0.6
[ Proportional chance criterion = 52% ]
Segmentation and targeting
Two-group discriminant analysis (cont’d)
Assessing the importance of individual predictor
variables:
□ Check whether the discriminant function is significant
and if so, how strongly each independent (discriminant)
variable is correlated with the discriminant function
scores;
□ Variables with larger (absolute) correlations are more
useful for discriminating between the groups;
The means of the variables that are important for
discrimination can then be compared across groups in
order to profile the segments;
Segmentation and targeting
Assessing the importance of
predictor variables
Classification Coefficients
Coefficients are from each variable in the discrimination function. This matrix was used internally, and will be required to run further discriminant analysis (i.e., classification) on external data.
Discriminant Variables / Functions Function 1
x1 (Age) -0.242
x2 (Education) -0.345
Discriminant Function
Correlation of variables with each significant discriminant function. (Significance level < 0.05).
Discriminant variable / Function Function 1
x2 (Education) -0.779
x1 (Age) -0.689Variance explained 100
Cumulative variance explained 100
Significance level 0.001
Segmentation and targeting
Describing the segments
Discriminant Variables
Means of each discriminant variable for each segment.
Discriminant variable / Cluster Overall Cluster 1 Cluster 2
x1 (Age) 4.1 2.25 5.333
x2 (Education) 5.2 3.5 6.333
Segmentation and targeting
Discriminant analysis for
more than two groups
For G groups, (G-1) discriminant functions are estimated
(assuming we have at least G-1 independent variables);
different discriminant functions usually separate different
sets of groups based on different variables; the
discriminant functions are used for deciding which
variables discriminate effectively between groups;
For purposes of classification, observations are assigned
to the group to which they are closest;
The quality of discrimination can be assessed with a hits-
and-misses table as before, but the proportional chance
criterion becomes 𝑝𝑖2, where the pi are the prior
probabilities of group membership;
Segmentation and targeting
Office Star data
40 respondents rated the importance of 6 attributes
when choosing an office supply store: variety of choice,
(availability of) electronics, (availability of) furniture,
quality of service, low prices, and return policy;
Importance was rated on a scale from 0 (not at all
important) to 10 (extremely important);
Data on three descriptor variables are also available:
whether or not the respondent is a professional, the
respondent’s income, and the respondent’s age;
Data on these three descriptor variables are also
available for an additional 300 respondents for whom no
segmentation data were collected;
Segmentation and targeting
3-cluster solution for Office Star dataCluster SizesThe following table lists the size of the population and of each segment, in both absolute and relative terms.
Size / Cluster Overall Cluster 1 Cluster 2 Cluster 3
Number of observations 40 18 14 8
Proportion 1 0.45 0.35 0.2
Segmentation VariablesMeans of each segmentation variable for each segment.
Segmentation variable / Cluster
Overall Cluster 1 Cluster 2 Cluster 3
Variety of choice 7.53 9.11 6.93 5.00Electronics 4.57 6.06 2.79 4.38Furniture 3.45 5.78 1.43 1.75Quality of service 4.00 2.39 3.50 8.50Low prices 5.05 3.67 8.29 2.50Return policy 4.50 3.17 6.29 4.38
Segmentation and targeting
Confusion Matrix
Comparison of cluster membership predictions based on discriminant data and actual cluster memberships. High values in the diagonal of the confusion matrix (in bold) indicate that discriminant data is good at predicting cluster membership.
Actual / Predicted cluster Cluster 1 Cluster 2 Cluster 3
Cluster 1 10 3 5Cluster 2 0 13 1Cluster 3 2 2 4
Actual / Predicted cluster Cluster 1 Cluster 2 Cluster 3
Cluster 1 55.60% 16.70% 27.80%
Cluster 2 00.00% 92.90% 07.10%
Cluster 3 25.00% 25.00% 50.00%
Hits-and-misses table for Office Star data
Overall hit rate = 67.5%, proportional chance criterion = 36.5%
Segmentation and targeting
Discriminant analysis of Office Star data
Discriminant Variables
Means of each discriminant variable for each segment.
Discriminant variable / Cluster Overall Cluster 1 Cluster 2 Cluster 3
Age 40.525 44.222 30.929 49.0
Income (000's) 42.500 48.333 32.143 47.5
Professional 0.475 0.333 0.500 0.75
Discriminant Function
Correlation of variables with each significant discriminant function(significance level < 0.05).
Discriminant variable / Function Function 1 Function 2
Age 0.91 0.013
Income (000's) 0.696 0.336
Professional 0.068 -0.771Variance explained 71.36 28.64
Cumulative variance explained 71.36 100
Significance level 0 0.042
Segmentation and targeting
Classification results for
300 additional respondents
Respondents / Discriminant variables and predicted cluster
Professional Income (000's) Age Predicted Cluster
Customer 1 1 45 30 2Customer 2 0 55 50 1
Customer 3 1 20 56 3Customer 4 0 45 23 2
Customer 5 1 55 56 3Customer 6 0 20 31 2Customer 7 0 15 58 3Customer 8 0 20 44 2Customer 9 0 20 44 2
Customer 10 1 35 28 2Etc.
Row Labels (Cluster)
Count of Predicted Cluster
Average of Age
Average of Income (000's)
Average of Professional
1 86 46 53 0.19
2 132 30 32 0.55
3 82 53 45 0.73
Grand Total 300 41 42 0.50
Segmentation and targeting
Issues in discriminant analysis
Technically, the IV’s should be multivariate normal and
the covariance matrices should be equal across groups.
Larger samples are needed when many independent
variables are included in the analysis (e.g., 20
observations per IV).
The selection of relevant IV’s is crucial, and the IV’s
should not be too highly correlated. Outliers can
negatively influence the results.
When the hit rate is calculated for the sample for which
the discriminant function was estimated, it will be biased
upward.
Segmentation and targeting
Recap: Discriminant analysis
Choose discriminant variables that can be expected to
be predictive of segment membership;
Run the discriminant analysis and assess the overall
quality of the discrimination based on the confusion
matrix (hits-and-misses table) and the proportional
chance criterion;
Assess the usefulness of individual discriminant
variables based on the magnitude of their correlation
with significant discriminant functions and compare the
means of important discriminant variables across
segments;
Classify new customers into segments based on their
scores on the discriminant variables;
Segmentation and targeting
Target marketing
evaluation of the attractiveness of each market
segment and selection of target segments;
evaluation of market segments based on
□ market segment characteristics (attractiveness)
□ company objectives and resources (competitive
position)
selection of target segments can result in
□ undifferentiated (mass) marketing
□ differentiated marketing
□ concentrated marketing
Segmentation and targeting
portfolio models are tools to allocate scarce resources to different businesses (e.g., product markets) in a multi-business firm;
steps in portfolio analysis:□ identify strategic business units (or SBUs);
□ rate each SBU in terms of market attractiveness and competitive position;
□ decide whether to build, maintain, harvest, or divest a business;
the goal is to have a balanced portfolio of businesses which will ensure profitability and growth in the long run;
Portfolio analysis
Segmentation and targeting
BCG growth-share matrix
marketgrowth rate
relative market share10x 1x .1x
10%
0%
20%
maintain leadershipand build future
cash cow
harvest and managefor maximum profitability
build shareor divest
divest
?
Segmentation and targeting
Segmentation and targeting
Steps in constructing a market attractiveness/competitive position matrix
for selecting target markets
List the segments to be evaluated and estimate their size
Identify the key factors determining market attractiveness
(e.g., size, growth, margins, current competition) and
competitive position (e.g., product fit, access, brand
reputation, current penetration)
Assign weights to each factor (e.g., 1=least important,
5=most important)
Rate each segment on the factors (e.g., 1=worst, 5=best)
Calculate each segment’s market attractiveness and
competitive position score
Plot each segment in the matrix
Segmentation and targeting
Office Star data [made up – not in ME]Horizontal Axis (ratings, weights)On a scale from 1 to 5, rate Products on each factor, and weight the importance of each factor.
Competitive Position Cluster 1 Cluster 2 Cluster 3 Weights
Product Fit 4 1 3 3
Brand Reputation 4 2 3 4
Market Share 3 1 2 3
Competitive Advantage 3 1 3 2
Vertical Axis (ratings, weights)
On a scale from 1 to 5, rate Products on each factor, and weight the importance of each factor.
Market Attractiveness Cluster 1 Cluster 2 Cluster 3 Weights
Overall Market Size 5 4 2 2
Annual Market Growth Rate 2 4 2 2
Competitive Intensity 3 5 2 4
Historical Margins 3 2 4 3
Market Size
On a scale from 1 to 20, please enter market size for each item.
Cluster 1 Cluster 2 Cluster 3
Market Size 9 7 4
Segmentation and targeting
Cluster 1
Cluster 2
Cluster 3Mar
ket
Att
ract
ive
ne
ss
Competitive Position
Office Star data
Segmentation and targeting
Market Attractiveness/
Competitive Position Matrix
Maximuminvestment
Consolidateposition
Invest tochallenge
leader
Opportunitiesinvestment
Build strengthor exit
Selectiveinvestment
Build onstrengths
Protectposition
Manage for cash
generation
Cautiousinvestment
Harvest ordivest
Harvest ordivest
Harvest ordivest
Ma
rke
t a
ttra
ctive
ne
ss
low
me
diu
mh
igh
highmediumlow
Competitive position
Segmentation and targeting
Assignment for next week
Downloads the overheads (Positioning.pdf)
LRB Chapter 4
Positioning Tutorial (ME)
Office Star examples
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