Clustering algorithms partition a set of n objects into p groups (called clusters), such that objects assigned to the same groups are homogeneous according to some criteria. To derive these clusters, the data input required is often a single n × n dissimilarity matrix. Yet for many...

The Segmentation-Targeting-Positioning (STP) process is the foundation of all marketing strategy. This chapter presents a new constrained clusterwise multidimensional unfolding procedure for performing STP that simultaneously identifies consumer segments, derives a joint space of brand...

Substitution decisions have been examined from a variety of perspectives. The economics literature measures cross-price elasticity, operations research models optimal assortments, the psychology literature studies goals in conflict, and marketing research has examined substitution-in-use, brand...

Purpose– The paper aims to examine the nature of competition within an industry by proposing and examining three separate sources of competitive heterogeneity: the strategies that industry members use, the performance that they obtain, and how effectively the strategies are utilized to obtain...

The p-median offers an alternative to centroid-based clustering algorithms for identifying unobserved categories. However, existing p-median formulations typically require data aggregation into a single proximity matrix, resulting in masked respondent heterogeneity. A proposed three-way...

Kim, Fong, and DeSarbo (2012) recently introduced a finite mixture Bayesian regression model to simultaneously identify market segments of consumers (heterogeneity) and determine how such segments differ with respect to active regression coefficients (variable selection). The current manuscript...