期刊
MARKETING SCIENCE
卷 27, 期 6, 页码 995-1011出版社
INFORMS
DOI: 10.1287/mksc.1080.0369
关键词
Bayesian decision theory; conjoint analysis; constrained optimization; cross-validation; hierarchical Bayes; loss function; market share prediction; penalized maximum likelihood; posterior risk
类别
Choice-based conjoint analysis is a popular marketing research technique to learn about consumers' preferences and to make market share forecasts under various scenarios for product offerings. Managers expect these forecasts to be realistic in terms of being able to replicate market shares at some prespecified or basecase scenario. Frequently, there is a discrepancy between the recovered and base- case market share. This paper presents a Bayesian decision theoretic approach to incorporating base- case market shares into conjoint analysis via the loss function. Because de. ning the base-case scenario typically involves a variety of management decisions, we treat the market shares as constraints on what are acceptable answers, as opposed to informative prior information. Our approach seeks to minimize the adjustment of parameters by using additive factors from a normal distribution centered at 0, with a variance as small as possible, but such that the market share constraints are satisfied. We specify an appropriate loss function, and all estimates are formally derived via minimizing the posterior expected loss. We detail algorithms that provide posterior distributions of constrained and unconstrained parameters and quantities of interest. The methods are demonstrated using discrete choice models with simulated data and data from a commercial market research study. These studies indicate that the method recovers base- case market shares without systematically distorting the preference structure from the conjoint experiment.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据