Cluster Analysis 9

Cluster Analysis ₉

Keywords

Agglomerative clustering • Average linkage • Canberra distance • Centroid linkage • Chaining effect • Chebychev distance • City-block distance • Clusters • Clustering variables • Complete linkage • Dendrogram • Distance matrix • Divisive clustering • Duda-Hart index • Euclidean distance • Factor- cluster segmentation • Gower’s dissimilarity coefficient • Hierarchical clustering methods • Partitioning methods • k-means • k-medians • k-means++ • k-medoids • Label switching • Linkage algorithm • Local optimum • Mahalanobis distance • Manhattan metric • Market segmentation • Matching coefficients • Non-hierarchical clustering methods • Profiling • Russel and Rao coefficient • Single linkage • Simple matching coefficient • Straight line distance • Ties • Variance ration criterion • Ward’s linkage • Weighted average linkage

Learning Objectives

After reading this chapter, you should understand:

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  The basic concepts of cluster analysis.
  
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  How basic cluster algorithms work.
  
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  How to compute simple clustering results manually.
  
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  The different types of clustering procedures.
  
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  The Stata clustering outputs.
  

^Ⓒ Springer Nature Singapore Pte Ltd. 2018
E. Mooi et al., Market Research, Springer Texts in Business and Economics, DOI 10.1007/978-981-10-5218-7_9
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1. Introduction

Market segmentation is one of the most fundamental marketing activities. Since consumers, customers, and clients have different needs, companies have to divide markets into groups (segments) of consumers, customers, and clients with similar needs and wants. Firms can then target each of these segments by positioning themselves in a unique segment (e.g., Ferrari in the high-end sports car market). Market segmentation “is essential for marketing success: the most successful firms drive their businesses based on segmentation” (Lilien and Rangaswamy 2004, p. 61) and “tools such as segmentation [.. .] have the largest impact on marketing decisions” (John et al. 2014, p. 127). While market researchers often form market segments based on practical grounds, industry practice and wisdom, cluster analysis uses data to form segments, making segmentation less dependent on subjectivity.

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