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Market Segmentation &

Targeting

Cluster Analysis & Discriminant

Analysis

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Segmentation – Many Uses

Segmenting the market

benefit segmentation -- forming segments of consumers that are relatively homogeneous in terms of benefits sought

Selecting test markets

By grouping cities into homogeneous groups, it is possible to select comparable cities to test various marketing strategies

Identifying new product opportunities

“competitive sets” -- clustering brands competing more fiercely with each otherEmerging needs (Opportunity-focused segmentation)

Salesforce allocation/call planning

Emerging needs (Opportunity-focused segmentation)

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Segmentation, Targeting, &

Positioning

To identify and select groups of potential buyers (organizations, buying centres, individuals)

Whose needs within-groups are similar and between-groups are different

Who can be reached profitably

With a focused marketing program

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Segmentation

Target segments may not be clearly defined and reachable

In practice, segments may be hard to define, fuzzy, and overlapping

Buyers can be classified into one or more segments

Segmentation is not a static classification but a process to support business decisions

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Find Value-Based Segments

• Evaluate competencies vs. attractiveness

F D

A

B

Low Average High

Low

Average

High

Segment Attractiveness

“The Market”

Vs. “Segments” C

G

I

E

J

Com

pe

ten

cy in S

egm

en

t

Segments

Bases Characteristics that tell us why segments differ

(needs, preferences, decision processes…)

Descriptors Characteristics that tell us how to find and reach Business Consumer Industry Age/Income Size Education Location Profession Organizational Structure Lifestyles Media habits

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What is Cluster Analysis ?

Objective of Cluster Analysis is – to separate objects (usually consumers) into

groups such that – each object is more alike other objects in its

groups than objects outside the group

Cluster Analysis assumes that – the underlying structure of the data involves an

unordered set of discrete classes; – these classes can be hierarchical in nature,

where some classes are divided into subclasses; – we do NOT use prior information to partition the

objects into groups; – we only assume that the data are “partially”

heterogeneous i.e. that “clusters” exist

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Doing Cluster Analysis

Dimension

2

Dimension 1

•

• • • •

• •

• •

• • •

Perceptions or ratings data

from one respondent III

a I II

b

a = distance from member

to cluster center

b = distance from I to III

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Market Structure Analysis using

Hierarchical Clustering

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Maruti Hyundai Maruti Honda Hyundai

Swift Santro SX4 City Verna

1 cluster

2 clusters

3 clusters

5 clusters

Procedure - Cluster Analysis

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Problem Formulation Step 1

Select a Distance Measure Step 2

Select a Clustering Procedure Step 3

Decide on the Number of Clusters Step 4

Interpret and Profile Clusters Step 5

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Distance or Similarity Measure

Euclidean Distance

City Block Distance

Correlation

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Clustering Procedures

Hierarchical Clustering: A clustering procedure characterized by the development of a hierarchy or treelike structure

– Agglomerative Clustering -- each object starts out in a separate cluster; clusters are formed by grouping objects into bigger and bigger clusters

– Divisive Clustering -- all objects start out in one group; clusters are formed by dividing this cluster into smaller and smaller clusters

Non Hierarchical Clustering: Number of clusters are prespecified; clusters built around cluster centres

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Agglomerative Clustering Methods:

– Linkage Methods -- Clusters objects based on computation of the distance between them

– Variance Methods -- Clusters are generated to minimize within-cluster variance

– Centroid Methods -- A method of hierarchical clustering in which the distance between two clusters is the distance between their centroids

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Ward’s Minimum Variance

Agglomerative Clustering Procedure

First Stage: A = 2 B = 5 C = 9 D = 10 E = 15

Second Stage: AB = 4.5 BD = 12.5

AC = 24.5 BE = 50.0

AD = 32.0 CD = 0.5

AE = 84.5 CE = 18.0

BC = 8.0 DE = 12.5

Third Stage: CDA = 38.0 CDB = 14.0 CDE = 20.66

AB = 4.5 AE = 84.5 BE = 50.0

Fourth Stage: ABCD = 41.0 ABE= 93.17 CDE = 20.66

Fifth Stage:

ABCDE = 98.8

Blackberry Pearl - Preferences

Respondents / Brands

RIM BlackBerry

Pearl

Palm Treo 700p

Motorola Q

Nokia 9300

Sidekick3 Sony

Ericsson M600i

Segment

1 9 9 8 7 1 4 I

2 5 6 4 8 4 4 II

3 8 7 9 5 3 5 I

4 6 5 3 7 4 4 II

5 6 4 3 8 3 4 II

6 8 7 5 5 7 5 III

7 9 7 8 6 4 6 I

8 8 5 9 6 4 5 I

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Blackberry Pearl - 9 Cluster Solution

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Dis

tance

Cluster ID 1 9 4 8 5 2 6 3 7

38.02

40.95

41.86

56.04

61.75

116.86

335.86

929.86

Blackberry Pearl – Cluster Profiles

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Segmentation variable / Cluster Overall Cluster 1 Cluster 2 Cluster 3

RIM BlackBerry Pearl 6.77 8.42 5.47 5.6

Palm Treo 700p 5.5 7 4.41 4.32

Motorola Q 5.5 7.79 3.06 4.68

Nokia 9300 6.06 6.21 7.19 4.36

Sidekick3 4.12 2.91 3.47 7.04

Sony Ericsson M600i 4.54 5.33 3.62 4.36

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Segmenting the PDA Market

Cluster Analysis (Benefit Segmentation)

– Identifying customers who differ in terms of their usage of the various features of ConneCtor -- data and voice inter-connectivity

– How many segments and how do they differ?

– Which segments should be targeted? – Pricing? – Product Line?

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How do we reach these segments?

DISCRIMINANT ANALYSIS

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Using Discriminant Analysis :

Typical Marketing Problems

• Investigation of group differences

– Whether groups differ from one another

– Nature of these differences

• Characteristics that differentiate between

– Purchasers of our brand and those of competing brands

– Brand loyal and non-loyal consumers

– Light and heavy users of the product

– Good, mediocre, and poor sales representative

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Using Discriminant Analysis

• Example: How do Brand-loyal and Switchers differ in terms of their socio-economic profiles?

– Simplistic Approach: Calculate the mean income, age, education level, and so on for the brand-loyals and switchers and compare and contrast the 2 groups on these dimensions

Potential Problems

• Variables may be correlated e.g. income and education level

• Which of these variables are more important?

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Discriminant Analysis Vs.

Cluster Analysis

How does Discriminant Analysis differ from Cluster

Analysis?

– In Discriminant Analysis, we form a priori groups

(e.g. loyals vs. switchers) and then ascertain

variables which “explain” these differences.

– In Cluster Analysis, no a priori grouping but let

data tell the “natural” groupings

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Discriminant Analysis – Analytical tool that considers the variables simultaneously

so as to take into account their inter-relationship and

partially overlapping information

– Construct a linear combination of the variables i.e. a

weighted sum

– So that the linear combination best discriminates among

the groups

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Mathematical Model

D = b0 + b1X1 + b2X2 + … + bMXM + e1

D = discriminant score b = discriminant coefficients or weights X = predictor or independent variables

The coefficients, b, are estimated so that the groups differ as much as possible on the value of the discriminant function, D

Occurs when the ratio of between-group sum of squares to within-group sum of squares for the discriminant scores is at a maximum

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ME Segmentation and

Targeting 2006 - 23

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

Procedure - Discriminant Analysis

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Problem Formulation Step 1

Estimate the Discriminant Function

Coefficients Step 2

Determine the Significance of the

Discriminant Function Step 3

Interpret the Discriminant Function Step 4

Assess Validity of Discriminant

Analysis Step 5

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Interpreting Discriminant Analysis

• 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).

Discriminant Analysis: Basic

Concepts

Key Words

– Canonical Correlation: Measures the extent of

association between the discriminant scores

and the groups. It is a measure of association

between the single discriminant function and

the set of dummy variables that define the

group membership

– Centroid: Mean values for the discriminant

scores for a particular group

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Discriminant Analysis: Basic

Concepts

Key Words

– Confusion Matrix: Contains the number of correctly classified and misclassified cases. The correctly classified cases appear on the diagonal, because the predicted and actual groups are the same

– Discriminant Loading: Represents the simple correlation between the predictors and the discriminant function. Higher loadings mean that the descriptor variable is important in explaining segment membership

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