Clustering at the Movies

Weekly box office revenues for approximately 100 successful motion pictures are analyzed by use of a finite mixture regression technique to determine if regular sales patterns emerge. Based on an exponential decay model applied to market share data, four clusters of movies, varying in opening strength and decay rate, are found. Characteristics of the clusters and implications for future research are discussed.     

A new clustering methodology for the analysis of sorted or categorized stimuli

This paper introduces a new stochastic clustering methodology devised for the analysis of categorized or sorted data. The methodology reveals consumers' common category knowledge as well as individual differences in using this knowledge for classifying brands in a designated product class. A small study involving the categorization of 28 brands of U.S. automobiles is presented where the results of the proposed methodology are compared with those obtained from KMEANS clustering. Finally, directions for future research are discussed. Wayne S. DeSarbo is the S. S. Kresge Distinguished Professor of Marketing and Statistics, and Michael D. Johnson is Associate Professor of Marketing, both at the University of Michigan's School of Business Administration. Kamel Jedidi is Assistant Professor of Marketing at Columbia University's Graduate School of Business. The authors gratefully acknowledge DuPont Incorporated for providing financial support for this research.     

Simultaneous multidimensional unfolding and cluster analysis: An investigation of strategic groups

This paper develops a maximum likelihood based methodology for simultaneously performing multidimensional unfolding and cluster analysis on two-way dominance or profile data. This new procedure utilizes mixtures of multivariate conditional normal distributions to estimate a joint space of stimulus coordinates and K ideal points, one for each cluster or group, in a T-dimensional space. The conditional mixture, maximum likelihood methodology is introduced together with an E-M algorithm utilized for parameter estimation. A marketing strategy application is provided with an analysis of PIMS data for a set of firms drawn from the same competitive industry to determine strategic groups, while simultaneously depicting strategy-performance relationships.INSEAD     

Representing Heterogeneity in Consumer Response Models 1996 Choice Conference Participants

We define sources of heterogeneity in consumer utility functions relatedto individual differences in response tendencies, drivers of utility, formof the consumer utility function, perceptions of attributes, statedependencies, and stochasticity. A variety of alternative modelingapproaches are reviewed that accommodate subsets of these various sourcesincluding clusterwise regression, latent structure models, compounddistributions, random coefficients models, etc. We conclude by defining anumber of promising research areas in this field.     

Customer value analysis in a heterogeneous market

In recent years, customer value has become a major focus among strategy researchers and practitioners as an essential element of a firm's competitive strategy. Many firms have been interested in Customer Value Analysis (CVA) which involves a structural analysis of the antecedent factors of perceived value (i.e., perceived quality and perceived price) to assess their relative importance in the perceptions of their buyers. We develop a statistical approach for performing CVA utilizing a recursive simultaneous equation model that is formulated to accommodate buyer heterogeneity. In particular, the proposed finite‐mixture methodology allows one to estimate the relative effects and integration rules of perceived value drivers at the market segment level, as well as to simultaneously determine the (unknown) segments themselves. We demonstrate the utility of the proposed methodology via an actual commercial application involving a large electric utility company. Finally, we discuss the contributions of our research from the perspective of firm strategy and how it may be extended in the future. Copyright © 2001 John Wiley & Sons, Ltd.     

The past,present, and future of measurement and methods in marketing analysis

Marketing Letters - The field of marketing has made significant strides over the past 50 years in understanding how methodological choices affect the validity of conclusions drawn from our...     

Modeling preference evolution in discrete choice models: A Bayesian state-space approach

We develop discrete choice models that account for parameter driven preference dynamics. Choice model parameters may change over time because of shifting market conditions or due to changes in attribute levels over time or because of consumer learning. In this paper we show how such preference evolution can be modeled using hierarchial Bayesian state space models of discrete choice. The main feature of our approach is that it allows for the simultaneous incorporation of multiple sources of preference and choice dynamics. We show how the state space approach can include state dependence, unobserved heterogeneity, and more importantly, temporal variability in preferences using a correlated sequence of population distributions. The proposed model is very general and nests commonly used choice models in the literature as special cases. We use Markov chain monte carlo methods for estimating model parameters and apply our methodology to a scanner data set containing household brand choices over an eight-year period. Our analysis indicates that preferences exhibit significant variation over the time-span of the data and that incorporating time-variation in parameters is crucial for appropriate inferences regarding the magnitude and evolution of choice elasticities. We also find that models that ignore time variation in parameters can yield misleading inferences about the impact of causal variables. This paper is based on the first author's doctoral dissertation.