STP Cluster Discriminant (1)
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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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3
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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