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Quantitative Techniques-II

Notes where to locate new retail stores.

If clusters of customers are found based on their attitudes towards new products and interest in
different kinds of activities, an estimate of the segment size for each segment of the population
can be obtained, by looking at the number of objects in each cluster.
Names can also be given to clusters to describe each one. For example, there can be a cluster
called “neo-rich”. Segments are prioritised based on their estimated size.
Marketing strategies for each segment are fine-tuned based on the segment characteristics. For
instance, a segment of customers, like sports car, get a special promotional offer during specific
period.

Example: In cluster analysis, the following five steps to be used:
1. Selection of the sample to be clustered (buyers, products, employees)
2. Definition on which the measurement to be made (Eg: product attributes, buyer
characteristics, employees’ qualification)
3. Computing the similarities among the entities.
4. Arrange the cluster in a hierarchy.
5. Cluster comparison and validation.

14.3.1 Cluster Analysis on Three Dimensions

The example below shows Cluster Analysis based on three dimensions age, income and family
size. Cluster Analysis is used to segment the car-buying population in a Metro. For example “A”
might represent potential buyers of low end cars. Example: Maruti 800 (for common man).
These are people who are graduating from the two-wheeler market segment. Cluster “B” may
represent mid-population segment buying Zen, Santro, Alto etc. Cluster “C” represents car
buyers, who belong to upper strata of society. Buyers of Lancer, Honda city etc. Cluster “D”
represents the super-rich cluster, i.e. Buyers of Benz, BMW etc.

Figure 14.1: Matching Measure

Example: Suppose there are five attributes, 1 to 5, on which we are judging two objects
A and B. The existence of an attribute may be indicated by 1 and its absence by 0. In this way, two
objects are viewed as similar if they share common attributes.

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