1. Segmentation Analysis

2. 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

3. 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.

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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.

11. 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

12. 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

13. 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.

14. 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

15. 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

16. 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

17. 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

18. 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.)

19. 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

20. 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

21. 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