دانلود رایگان مقاله مقایسه جایگزین ها برای عدم همبستگی نامتعارف در مدل های بازاریابی مستقیم

Abstract

We are dealing with mailing decisions of a direct marketing company and focus on assessing three alternative approaches to model unobserved heterogeneity, which are based on finite mixtures, continuous mixtures, and a mixture of Dirichlet processes (MDP), respectively. Models are estimated by Markov Chain Monte Carlo (MCMC) simulation. Based on Pseudo Bayes factors (PsBF), we find that a finite mixture model turns out to be superior both to models based on either a MDP or a continuous mixture. Whereas the MDP finds similar estimates compared to the finite mixture approach, estimates of the continuous mixture differ for some variables. According to the finite mixture, type of mailing has an effect on purchase behavior. In addition, some customers show supersaturation effects of mailings. Due to different coefficient estimates, managerial implications differ depending on which model they relate. In particular, a continuous mixture model would recommend more mailings than a finite mixture approach.

6. Conclusion

Our results on unobserved heterogeneity show that a finite mixture approach is preferable to a MDP or continuous mixture approach. Consistent with findings on the overall model fit, a finite mixture and a MDP are more similar to each other and different from the continuous mixture model. A MDP and a finite mixture both result in the same number of segments leading to a more or less identical clustering structure. Except for estimates on the size stock decay parameter, we do not observe any significant differences on estimates. However, the advantage of the MDP of not having to estimate several models comes at a price: the overall model fit is worse for the MDP compared to the finite mixture.