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Page 1: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

Segmentationand Targeting

Page 2: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

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

Page 3: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

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

Page 4: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

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

Page 5: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

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

Page 6: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

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.

Page 7: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

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

Page 8: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

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;

Page 9: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

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)

Page 10: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

Segmentation and targeting

Differences in customer response

marketingvariable

Response

Segment B

Segment AA1

A2

B1

B2

x1x2

Who’s

this?

Who’s

this?

Page 11: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

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

Page 12: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

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

Page 13: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

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?

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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

Page 15: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

Segmentation and targeting

A simple segmentation example:

Preferences of 6 consumers for 2 attributes of beer

Page 16: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

Segmentation and targeting

A simple segmentation example:

Preferences of 6 consumers for 2 attributes of beer

Page 17: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

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

Page 18: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

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!

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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?

Page 20: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

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)

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Segmentation and targeting

Euclidean distance

𝑑𝑅1𝑅6 = (𝑋2 − 𝑋1)2+(𝑌2 − 𝑌1)

2

R1

R6

Page 22: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

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 --

Page 23: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

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;

Page 24: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

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;

Page 25: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

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

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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

Page 27: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

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.

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Segmentation and targeting

Dendrogram

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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.

Page 30: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

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

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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).

Page 32: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

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;

Page 33: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

Segmentation and targeting

Page 34: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

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

Page 35: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

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

Page 36: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

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

Page 37: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

Segmentation and targeting

Assignment for next week

LRB Chapter 3

Segmentation and Classification Tutorial (ME)

GE Tutorial (ME)

Office Star examples

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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

Page 39: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

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

Page 40: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

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;

Page 41: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

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)?

Page 42: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

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;

Page 43: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

Segmentation and targeting

Discriminant scores based on x1 only

Cluster 1

Cluster 2

Page 44: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

Segmentation and targeting

Discriminant scores based on x1 and x2

Cluster 1

Cluster 2

Page 45: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

Segmentation and targeting

Equidistant points

Cluster 1

Cluster 2

Page 46: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

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

Page 47: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

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

Page 48: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

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% ]

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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;

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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

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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

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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;

Page 53: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

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;

Page 54: Segmentation and Targeting - personal.psu.edu · Segmentation and targeting ... Markets can be segmented on the basis of lots of different variables, ... Regular and ice beer Light

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

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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%

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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

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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

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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.

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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;

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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

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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

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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

?

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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

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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

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Segmentation and targeting

Cluster 1

Cluster 2

Cluster 3Mar

ket

Att

ract

ive

ne

ss

Competitive Position

Office Star data

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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

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Segmentation and targeting

Assignment for next week

Downloads the overheads (Positioning.pdf)

LRB Chapter 4

Positioning Tutorial (ME)

Office Star examples