Segmentation Analysis
Segmentation
• Assign people to groups/clusters on basis of similarities
in characteristics, attributes
• Bases of segmentation:
- Demographic
- Behavior
- Geographic
- Benefit
- Product/ Product usage
- Purchase
• Here we focus on two techniques used in segmentation
Segmentation: Factor Analysis
• Defines underlying structure of data matrix
• Summarizes a large no. of var. by identifying a few
common underlying dimensions called Factors
• Two primary uses:Summarization;Data reduction
• It is an interdependence technique
• Can be: Exploratory; Confirmatory.
• We consider Exploratory view-point.
Segmentation Method: Factor
Analysis(contd.)
• Six stage model building for F.A.
• Stage 1: Research Problem
a. Exproratory or confirmatory
b. Data summarization or data reduction
• Stage 2: Three basic decisions
a. Calculation of input data
b. Variable selection and measurement issues
c. Sample size
Segmentation: Factor Analysis(contd.)
• Stage 3: assumptions of F.A.
a. Normality
b. Linearity
c. Ensure data matrix has enough correlations
d. Homogeneity of sample
e. Conceptual linkages
Segmentation: Factor Analysis(contd.)
• Stage 4: Deriving factors and assesing fit.
a. Method of extracting factors
i. Common F.A.
ii. Component F.A.
Different types of variances
i. Common variance
ii. Specific (unique) variance
iii. Error
Segmentation: Factor Analysis(contd.)
• Stage 4: Deriving factors and assesing fit (contd.).
b. Number of factors to extract
i. Latent root/eigenvalue
ii. %-age of variation
iii. Scree test
iv. A priori criterion
Segmentation: Factor Analysis(contd.)
• Stage 5: Interpreting the factors
a. Initial unrotated factors are obtained
b. Rotation of factors for better interpretation
i. Orthogonal methods
- Varimax
ii. Oblique methods
c. Assess need to respecify factor model
Segmentation: Factor Analysis(contd.)
• Criteria for significance of factor loadings
- Practical significance
- Statistical significance
• Interpreting the factor matrix
- Examine matrix of loadings
- Identify highest loading for each variable
- Assess communalities for acceptable levels
- Label the factors
Segmentation: Factor Analysis(contd.)
• Stage 6: Validation of F.A.
a. Move to confirmatory perspective and
assess replicability with split-sample
or a new sample.
Example: Factor Analysis
• A direct mail company sent mailings for an
upscale good, based on a commercially available
cluster analysis package
• Contrary to expectation, response came from
middle/lower socioeconomic clusters, not from the
top
• To verify clustering, the company used census
data at the zip code level
• 40 census variables were chosen and each of the
2000 New York state zip codes was rated on these
• 10 factors (those with eigenvalue>1)were used for
further analysis
Segmentation:Cluster Analysis
• Groups objects into clusters
• Only multi variate technique that does not
estimate the variate empirically but uses a
researcher-specified variate
• Focuses on comparison of objects based on
variate not on estimation of variate itself
• Groups objects whereas Factor Analysis
groups variables
Segmentation:Cluster Analysis(contd.)
• Six stage model building process
• Stage 1: Research problem
a. Select objectives
i. Taxonomy description
ii. Data simplification
iii. Relationship identification
b. Select clustering var. Include only var.that
i. Characterize objects being clustered
ii. Relate to objects of C.A.
Segmentation:Cluster Analysis(contd.)
• Stage 2: Research design
a. Can outliers be detected
b. Similarity measures
i. Metric data (correlational;distance)
ii. Non-metric data (association)
c. Standardize data?
• Stage 3: Assumptions of C.A.
a. Ensure representativeness in sample
b. Examine is variables are highly correlated
Segmentation:Cluster Analysis(contd.)
• Stage 4: Deriving clusters and assessing fit
a. Hierarchical algorithms
i. Agglomerative(single linkage, complete
linkage, avg. linkage,Ward’s method etc)
ii. Divisive(agglomerative in reverse order)
b. Non-hierarchical
c. Combination
Segmentation:Cluster Analysis(contd.)
• Algorithm for K-means clustering
1. Partition items into K clusters
2. Assign items to cluster with nearest
centroid mean
3. Recalculate centroids both for cluster
receiving and losing item
4. Repeat steps 2 and 3 till no more
reassignments
Segmentation:Cluster Analysis(contd.)
• How many clusters to form
- Stop when similarity measure exceeds certain
value
- Stop when successive values between steps
makes sudden jump
- Adopt some statistical test or rule
- Researcher’s empirical/theoretical basis
Segmentation:Cluster Analysis(contd.)
• Stage 5: Interpretation of clusters
a. Examine each cluster w.r.t cluster variate
b. Use cluster’s centroid as a measure
• Stage 6: Validation and profiling of clusters
a. Validation(generalizability, test results
on split-sample, predictive validity)
b. Profiling(Discriminant analysis with identified
clusters as dep. var. & demographics,psychographics
as indep.var.)
Example: Cluster Analysis
• Stage 1: Objectives
To segment HATCO’s customers into groups having
similar perceptions of HATCO. Perceptions are
based on X1 through X7
• Stage 2: Research Design
- No outliers
- All 7 variables are metric, so Euclidean distance
used as similarity measure
- No correlations among variables
- All variables on same scale, so no standardization
needed
Example: Cluster Analysis
• Step 3: Assumptions
All assumptions are met
• Step 4: Deriving clusters and assessing fit
a) Hierarchical method
- Both a 2-cluster and a 4-cluster solution
selected for further analysis
- Tested for distinctiveness of the clusters
b) Non Hierarchical method
• Step 5: Interpretation of clusters
From Factor analysis of X1 through X7 we know that
X1, X2, X3 , X7 constitute one dimension (with X1, X3
inversely related to X2, X7 ) and X4 , X6 another dim
Example: Cluster Analysis
From Profile Diagram, Cluster 1is higher
on X1 and X3 and lower on X2 and X7. Thus
cluster 1 is high on dim 1 (Basic value).
Also, cluster 2 is high on X4 , X6 i.e on dim
2 (Image)
• Step 6: Validation and profiling
Clusters profiled on additional variables