Cluster Analysis

Topics: Cluster analysis, Distance, Euclidean distance Pages: 62 (16624 words) Published: March 21, 2013
Chapter 9

Cluster Analysis

Learning Objectives
After reading this chapter you should understand: – The basic concepts of cluster analysis. – How basic cluster algorithms work. – How to compute simple clustering results manually. – The different types of clustering procedures. – The SPSS clustering outputs.

Keywords Agglomerative and divisive clustering Á Chebychev distance Á City-block distance Á Clustering variables Á Dendrogram Á Distance matrix Á Euclidean distance Á Hierarchical and partitioning methods Á Icicle diagram Á k-means Á Matching coefficients Á Profiling clusters Á Two-step clustering Are there any market segments where Web-enabled mobile telephony is taking off in different ways? To answer this question, Okazaki (2006) applies a twostep cluster analysis by identifying segments of Internet adopters in Japan. The findings suggest that there are four clusters exhibiting distinct attitudes towards Web-enabled mobile telephony adoption. Interestingly, freelance, and highly educated professionals had the most negative perception of mobile Internet adoption, whereas clerical office workers had the most positive perception. Furthermore, housewives and company executives also exhibited a positive attitude toward mobile Internet usage. Marketing managers can now use these results to better target specific customer segments via mobile Internet services.

Grouping similar customers and products is a fundamental marketing activity. It is used, prominently, 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. Firms can then target each of these segments by positioning themselves in a unique segment (such as Ferrari in the high-end sports car market). While market researchers often form E. Mooi and M. Sarstedt, A Concise Guide to Market Research, DOI 10.1007/978-3-642-12541-6_9, # Springer-Verlag Berlin Heidelberg 2011 237


9 Cluster Analysis

market segments based on practical grounds, industry practice and wisdom, cluster analysis allows segments to be formed that are based on data that are less dependent on subjectivity. The segmentation of customers is a standard application of cluster analysis, but it can also be used in different, sometimes rather exotic, contexts such as evaluating typical supermarket shopping paths (Larson et al. 2005) or deriving employers’ branding strategies (Moroko and Uncles 2009).

Understanding Cluster Analysis
Cluster analysis is a convenient method for identifying homogenous groups of objects called clusters. Objects (or cases, observations) in a specific cluster share many characteristics, but are very dissimilar to objects not belonging to that cluster. Let’s try to gain a basic understanding of the cluster analysis procedure by looking at a simple example. Imagine that you are interested in segmenting your customer base in order to better target them through, for example, pricing strategies. The first step is to decide on the characteristics that you will use to segment your customers. In other words, you have to decide which clustering variables will be included in the analysis. For example, you may want to segment a market based on customers’ price consciousness (x) and brand loyalty (y). These two variables can be measured on a 7-point scale with higher values denoting a higher degree of price consciousness and brand loyalty. The values of seven respondents are shown in Table 9.1 and the scatter plot in Fig. 9.1. The objective of cluster analysis is to identify groups of objects (in this case, customers) that are very similar with regard to their price consciousness and brand loyalty and assign them into clusters. After having decided on the clustering variables (brand loyalty and price consciousness), we need to decide on the clustering procedure to form our groups of objects. This step is crucial for...
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