It has been shown in the behavioral decision making, marketing research, and psychometric literature that the structure underlying preferences can change during the administration of repeated measurements (e.g., conjoint analysis) and data collection because of effects from learning, fatigue, boredom, and so on. In this research note, we propose a new class of hierarchical dynamic Bayesian models for capturing such dynamic effects in conjoint applications, which extend the standard hierarchical Bayesian random effects and existing dynamic Bayesian models by allowing for individual-level heterogeneity around an aggregate dynamic trend. Using simulated conjoint data, we explore the performance of these new dynamic models, incorporating individual-level heterogeneity across a number of possible types of dynamic effects, and demonstrate the derived benefits versus static models. In addition, we introduce the idea of an unbiased dynamic estimate, and demonstrate that using a counterbalanced design is important from an estimation perspective when parameter dynamics are present.heterogeneity, empirical utility functions, dynamic models, Bayesian analysis, conjoint analysis, unbiased dynamic estimates
Trade-in programs are offered extensively in business-to-business (B2B) markets. The success of such programs depends on well-designed and executed trade-in policies as well as accurate prediction of return flow to support operational decisions. Motivated by a real problem facing a high-tech company, this paper develops methods to segment customers and forecast product returns based on return merchandise authorization information. Noisy, yet proven to be valuable, returned quantity signals are adjusted by taking product characteristics and customer heterogeneity into account, and the resulting forecast outperforms two benchmark strategies that represent the high-tech company's current practice and a widely adopted method in the literature, respectively. In addition, our methods can serve as tools for companies to uncover the root causes of return merchandise authorization discrepancy, monitor and analyze customer behavior, design segment-specific trade-in policies, and evaluate the effectiveness and efficiency of trade-in programs on a continuous basis.empirical research, trade-in programs, signal-based forecast, count regression models, cluster analysis, customer segmentation
The authors propose a new Bayesian latent structure regression model with variable selection to solve various commonly encountered marketing problems related to market segmentation and heterogeneity. The proposed procedure simultaneously performs segmentation and regression analysis within the derived segments, in addition to determining the optimal subset of independent variables per derived segment. The authors present comparative analyses contrasting the performance of the proposed methodology against standard latent class regression and traditional Bayesian finite mixture regression. They demonstrate that their proposed Bayesian model compares favorably with these traditional benchmark models. They then present an actual commercial customer satisfaction study performed for an electric utility company in the southeastern United States, in which they examine the heterogeneous drivers of perceived quality. Finally, they discuss limitations of the research and provide several directions for further research.
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