Cluster Analysis for Segmentation

Abstract

This note is designed for use in an MBA marketing research course. It provides an overview of segmentation using K-means clustering. A simple algorithm for K-means clustering and the process of profiling clusters are provided. The note discusses the need for segmentation in marketing and emphasizes the role of managerial judgment in choosing a segmentation policy. Examples from the insurance industry are used in the note. Excerpt UVA-M-0748 Rev. Mar. 28, 2018 Cluster Analysis for Segmentation Introduction We all understand that consumers are not all alike. This provides a challenge for the development and marketing of profitable products and services. Not every offering will be right for every customer, nor will every customer be equally responsive to your marketing efforts. Segmentation is a way of organizing customers into groups with similar traits, product preferences, or expectations. Once segments are identified, marketing messages and in many cases even products can be customized for each segment. The better the segment(s) chosen for targeting by a particular organization, the more successful the organization is assumed to be in the marketplace. Since its introduction in the late 1950s, market segmentation has become a central concept of marketing practice. Segments are constructed on the basis of customers' (1) demographic characteristics, (2)psychographics, (3) desired benefits from products/services, and (4) past-purchase and product-use behaviors. These days, most firms possess rich information about customers' actual purchase behavior, geodemographic, and psychographic characteristics. In cases where firms do not have access to detailed information about each customer, information from surveys of a representative sample of the customers can be used as the basis for segmentation. . . .