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.