面向客户偏好异质性的车身产品离散选择模型

Discrete Choice Model of Auto-body Product for Customer Preference Heterogeneity

基于随机系数mixed logit模型,针对车身产品层次结构,建立面向客户偏好异质性的离散选择模型.根据SP(stated preference)调查所获样本数据和参数先验分布设定,运用马尔可夫链蒙特卡洛模拟方法,对参数进行贝叶斯估计.最后,通过McFadden的似然比指标检验,证明随机系数mixed logit模型具有更好的拟合优度,更能阐明客户偏好异质性的所在.该建模方法不仅有助于捕获个性化客户需求,还有助于厂商预测潜在客户的多种偏好,从而辅助车身产品的设计开发.

Based on the random parameter mixed logit model,a discrete choice model for customer preference heterogeneity was established for the hierarchy of auto-body product.According to the sampled data obtained from SP（stated preference）survey and parameter prior distribution setting,the Markov chain Monte Carlo simulation method was used to make the Bayesian estimation of parameters.Finally,the McFadden＇s likelihood ratio test proves that the random parameter mixed logit model is of optimal goodness-of-fit,and better than others to elucidate where the customer preference heterogeneity rooted in.This modeling approach helps to capture personalized customer needs,and helps manufacturers to anticipate mutiple preferences of potential customers and assists in the design and the development of auto-body products.