Page 300 - DMGT404 RESEARCH_METHODOLOGY

Research Methodology

Notes 13.5 Cluster Analysis

Cluster Analysis is used:
1. To classify persons or objects into small number of clusters or group.
2. To identify specific customer segment for the company’s brand.
Cluster Analysis is a technique used for classifying objects into groups. This can be used to sort
data (a number of people, companies, cities, brands or any other objects) into homogeneous
groups based on their characteristics.
The result of Cluster Analysis is a grouping of the data into groups called clusters. The researcher
can analyse the clusters for their characteristics and give the cluster, names based on these.
Where can Cluster Analysis be applied?
The marketing application of cluster analysis is in customer segmentation and estimation of
segment sizes. Industries, where this technique is useful include automobiles, retail stores,
insurance, B-to-B, durables and packaged goods. Some of the well-known frameworks in consumer
behaviour (like VALS) are based on value cluster analysis.
Cluster Analysis is applicable when:
1. An FMCG company wants to map the profile of its target audience in terms of life-style,
attitude and perceptions.

2. A consumer durable company wants to know the features and services a consumer takes
into account, when purchasing through catalogues.
3. A housing finance corporation wants to identify and cluster the basic characteristics, life-
styles and mindset of persons who would be availing housing loans. Clustering can be
done based on parameters such as interest rates, documentation, processing fee, number
of installments etc.

Process

There are two ways in which Cluster Analysis can be carried out:
1. First, objects/respondents are segmented into a pre-decided number of clusters. In this
case, a method called non-hierarchical method can be used, which partitions data into the
specified number of clusters
2. The second method is called the hierarchical method.
The above two are basic approaches used in cluster analysis. This can be used to segment
customer groups for a brand or product category, or to segment retail stores into similar groups
based on selected variables.
Interpretation of Results

Ideally, the variables should be measured on an interval or ratio scale. This is because the
clustering techniques use the distance measure to find the closest objects to group into a cluster.
An example of its use can be clustering of towns similar to each other which will help decide
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.

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