Cluster Analysis
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.
Introduction
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
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.
Introduction
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
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