Haagen-Dazs Shoppe Co. with more than 850 retail ice cream shops in over 50 countries in 2009, was interested in expanding its customer base. The objective was to identify potential consumer segments that could generate additional sales. Geodemography, a method of clustering consumers based on geographic, demographic, and lifestyle characteristics, was employed for this purpose. Primary research was conducted to develop demographic and psychographic profiles of HaagenDazs Shoppe users, including frequency of purchase, time of the day they came in, day of the week, and other product use variables. The addresses and zip codes of the respondents were also obtained. The respondents were then assigned to 40 geodemographic clusters based on the clustering procedure developed by Nielsen Claritas (www.themarketingresearch.com). For each geodemographic cluster, the profile of Haagen-Dazs customers was compared to the cluster profile to determine the degree of penetration. Using this information, Haagen-Dazs was also able to identify several potential customer groups from which to attract traffic. In addition to expanding Haagen-Dazs’ customer base, product advertising was established to target new customers accordingly. New products were introduced. As of 2009, the Haagen-Dazs brand was owned by General Mills. However, in the United States and Canada, Haagen-Dazs products were produced by Nestle under a preexisting license
The Haagen-Dazs example illustrates the use of clustering to arrive at homogeneous segments for the purpose of formulating specific marketing strategies.
Cluster analysis is a class of techniques used to classify objects or cases into relatively homogeneous groups called clusters. Objects in each cluster tend to be similar to each other and dissimilar to objects in the other clusters. Cluster analysis is also called classification analysis, or numerical taxonomy.2 We will be cencerned with clustering procedures that assign each object to one and only one cluster.’ Figure 20.1 shows an ideal clustering situation, in which the clusters are distinctly separated on two variables: quality consciousness (variable I) and price sensitivity (variable 2). Note that each consumer falls into one cluster and there are no overlapping areas. , Figure 20.2, on the other hand, presents a clustering situation that is more likely to be encountered in practice. In Figure 20.2, the boundaries for some of the clusters are not clear-cut, and the classification of some consumers is not obvious, because many of them could be grouped into one cluster or another.
Both cluster analysis and discriminant analysis are concerned with classification. However, discriminant analysis requires prior knowledge of the cluster or group membership for each object or case included to develop the classification rule. In contrast, in cluster analysis there is no a priori information about the group or cluster membership for any of the objects. Groups or clusters are suggested by the data, not defined a priori.
Cluster analysis has been used in marketing for a variety of purposes, including the following.
• Segmenting the market:
For example, consumers may be clustered on the basis of benefits sought from the purchase of a product. Each cluster would consist of consumers who are relatively homogeneous in terms of the benefits they seek.6 This approach is called benefit segmentation.