Cluster analysis in prespective to Marketing Research

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Information about Cluster analysis in prespective to Marketing Research

Published on March 20, 2014

Author: sahilkpr84



Overview of cluster analysis.
Types of analysis and examples

Cluster Analysis By : MBA- IIT-D (2nd Year) Sahil Kapoor- 2012SMN6697 Bhushan Kumar – 2012SMN6688

What is Cluster & Cluster Analysis? • Cluster is a group of similar objects (cases, points, observations, members, customers, patients, locations, etc). • Cluster Analysis is a set of data-driven partitioning techniques designed to group a collection of objects into clusters, such that :- in the number of groups (clusters) the degree of association or similarity is strong between members of the same cluster or weak between members of different clusters. What Cluster analysis means in terms of “Marketing Research” Grouping similar customers and products is a fundamental marketing concept. It is used, for example, in market segmentation . As companies cannot connect with all their customers, they have to divide markets into groups of consumers, customers, or clients (called segments) with similar needs and wants. Each of these segments can then be targeted by firms who can position themselves in a unique segment. Ex: Ferrari in high end sport car market and Alto in middle/affordable class car market.

Uses of Cluster analysis in marketing • Data Reduction: Reduction of information from entire population by reducing characteristics of representative groups with minimal loss of information. It is important to select types of variables for clustering and effect of such variables on research. • Development of potential new opportunities for products: The company can determined the extent to which a potential new product or is uniquely positioned within the completive sets of other products. Ex: Apple iMac and Iphones • Understanding the consumer behavior in market: To identify homogenous group of customers, so called market segmentation. Segmentation in terms of demographic, age, financial and other characteristics. Ex: Identifying attributes in choosing banks and segmenting banks. Ref: Tuma, M.N., Scholz, S.W., Decker, R. (2009.). The Application of Cluster Analysis in Marketing Research: A Literature Analysis.B>Quest. University of West Georgia

Conducting a cluster analysis.. Formulate the problem Select a distance measure Select a clustering procedure Decide on no. of clusters Interpret and profile clusters Access the reliability & validity

1. Formulate a problem Perhaps the most important part of formulating the clustering problem is selecting the variables on which the clustering is based. Inclusion of even one or two irrelevant variables may distort an otherwise useful clustering solution. Set of variables selected should describe the similarity between objects in terms that are relevant to the marketing research problem. Ex: Clustering of consumers based on attitudes towards drinking & shopping. Scale (1 = disagree, 7 = agree): V1 Drinking if fun V2 Drinking is bad for your health. V3 I combine drinking with eating out. V4 I prefer drinking in parties. V5 I prefer drinking high end brands . V5 I don’t care about drinking. V1 Shopping is fun. V2 Shopping is bad for your budget. V3 I combine shopping with eating out. V4 I try to get the best buys while shopping. V5 I don’t care about shopping. V6 You can save a lot of money by comparing prices.

1. Formulate a problem V1 Shopping is fun. V2 Shopping is bad for your budget. V3 I combine shopping with eating out. V4 I try to get the best buys while shopping. V5 I don’t care about shopping. V6 You can save a lot of money by comparing prices. Case No. V1 V2 V3 V4 V5 V6 1 6 4 7 3 2 3 2 2 3 1 4 5 4 3 7 2 6 4 1 3 4 4 6 4 5 3 6 5 1 2 2 2 6 4 6 6 4 6 3 3 4 7 5 3 6 3 3 4

2. Select a distance measure Objective of clustering is to group similar objects together, some measure is needed to assess how similar or different the objects are. The most common approach is to measure similarity in terms of distance between pairs of objects. Objects with smaller distances between them are more similar to each other than are those at larger distances. Single variable, similarity is straightforward •Example: income – two individuals are similar if their income level is similar and the level of dissimilarity increases as the income gap increases Multiple variables require an aggregate distance measure •Many characteristics (e.g. income, age, consumption habits, family composition, owning a car, education level), it becomes more difficult to define similarity with a single value The most known measure of distance is the Euclidean distance. The Euclidean distance is the square root of the sum of the squared differences in values for each variable   n i piqiDij 1 )( 2

3. Select a clustering procedure Clustering procedures Hierarchical Non- Hierarchical Agglomerative Divisive Sequential threshold Parallel threshold Optimizin g threshold Linkage methods Variance methods Centroid methods Ward’ method Single linkage Complete linkage Average linkage

Agglomerative Clustering

Divisive Clustering

Single Linkage Minimum Distance Complete Linkage Maximum Distance Average Linkage Average Distance Cluster 1 Cluster 2 Cluster 1 Cluster 2 Cluster 1 Cluster 2

Ward’s Procedure Centroid Method

4. Decide on no. of clusters A major issue in cluster analysis is deciding on the number of clusters. Although there are no hard and fast rules, some guidelines are available. 1 Theoretical, practical considerations may suggest a certain number of clusters. For example, if the purpose of clustering is to identify market segments, management may want a particular number of clusters. 2 In hierarchical clustering, the distances at which clusters are combined can be used as criteria. Using dendogram.

5. Interpret and profile clusters Means of variables Cluster No. V1 V2 V3 V4 V5 V6 1 5.750 3.625 6.000 3.125 1.750 3.875 2 1.667 3.000 1.833 3.500 5.500 3.333 3 3.500 5.833 3.333 6.000 3.500 6.000 V1 Shopping is fun. V2 Shopping is bad for your budget. V3 I combine shopping with eating out. V4 I try to get the best buys while shopping. V5 I don’t care about shopping. V6 You can save a lot of money by comparing prices.

Application of clustering in real world • Marketing: Help marketers discover distinct groups in their customer bases, and then use this knowledge to develop targeted marketing programs • Land use: Identification of areas of similar land use in an earth observation database • Insurance: Identifying groups of motor insurance policy holders with a high average claim cost • City-planning: Identifying groups of houses according to their house type, value, and geographical location • Earth-quake studies: Observed earth quake epicenters should be clustered along continent faults


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