Using finite mixture models for market segmentation

Abstract

Market segmentation seeks to identify targeted groups of consumers to tailor products and branding in a way that is attractive to the group. Moreover, market segmentation assists companies minimize risk by figuring out which products are the most likely to earn a share of a target market and the best ways to market and deliver those products to the market. With risk minimized, a company can then focus its resources on efforts likely to be the most profitable. Market segmentation can also increase a company's accessibility by targeting products or services to the appropriate customers. Finite mixture models have been used extensively in market research. In this approach, cluster membership and parameter estimates for each cluster are estimated simultaneously. An appropriate approach for accommodating the rating responses is to assume a proportional odds model. The application focuses on customer preferences for investment bonds described by four attributes; currency, coupon rate, redemption term and price. A number of demographic variables are used to generate clusters that are accessible and actionable.