An application of model-based clustering in market segmentation

Introduction

Time series of counts data in marketing

Time series of counts (TSC) surface whenever countable, time-dependent observations can be made about a phenomenon. For example, daily catches of a fisherman, weekly swine flu cases admitted into a hospital, and monthly used cars sold by a salesman are all instances of TSC. Count data are abundant in the study of consumer behavior. Observations such as a consumer's product purchase patterns, online click streams, store visits, and rate of product consumption are examples of count data and can be analyzed for marketing purposes. In fact, in the creation of a company's marketing mix or marketing strategy, managers rely heavily on relevant, accurate and timely (RAT) information about the consumer behavior [link] . In an increasingly technology and information dependent society, companies scramble to acquire RAT information in order to understand the new, emerging demands of consumers, and to gain and keep that competitive edge.

Perhaps the most important use for consumer behavior data is market segmentation where consumers are identified and grouped based on their distinctive traits and characteristics. Market segmentation begins with the realization that there exists a heterogeneity in consumer demand of goods and services and that the heterogeneous market is made up of “a number of smaller homogeneous markets, in response to differing preferences, attributable to the desires of consumers for more precise satisfaction of their varying wants [link] ”. Market segmentation, if conducted correctly and effectively, is an extremely powerful and informative tool for managers since segmentation analysis can reveal both the size and the traits of the consumer groups, for whom appropriate group-specific marketing strategies can be devised. Correct and relevant market segmentation is critical for businesses since incorrect evaluations of consumer groups and their characteristics leads to unproductive marketing mix and waste of resources. Numerous publications and books have been written on effective methods of consumer segmentation. For a survey and review of market segmentation methods, see [link] .

Current market segmentation methods

Through a review of relevant literature on the subject, it is evident that the statistical process of cluster analysis lies at the core of market segmentation. Though classification of consumers into preset number of groups with already identified traits is also common in segmentation, clustering offers the advantages of flexibility and accuracy because the number of groups, their size and their characteristics are data-driven.