消費者異質性與聯合分析法：市場區隔化所扮演的角色

To design appropriate product, price, communication and channel strategies to satisfy consumers, marketing researchers endeavor to incorporate consumer heterogeneity into conjoint analyses to increase predictive validity of individual parameters. In estimating individual-level parameters, the most difficult problem facing researchers is the deficiency of individual-level data. In recent years many scholars have al-ready studied how to incorporate heterogeneity in marketing models as well as how to solve the problem of information deficiency. Among those models, Finite Mixture and hierarchical Bayes models are considered superior. However, both methods still have their weakness in estimating reliable individual parameters. In order to verify empirically that combining information about individuals with similar behavioral pattern will greatly increase predictive validity, we combine market segmentation and hierarchical Bayes methods to propose a new hierarchical Bayes conjoint segmentation model.