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In recent years, because database system is advancing as time goes by, we can collect a lot of customer transaction information. Consequently, in order to make effective marketing approach, how to effectively use of information is an important goal for enterprise.Among this information, interpurchase time is the indispensable variable used to judge the behavior of customer. There have been many research literatures addressing the issue of interpurchase time, however, many of them only focused on comparing the results predicted by gamma distribution and generalized gamma distribution. Therefore, the goal of this thesis is to analyze the meaning of the model established by different parameters. Based on generalized gamma distribution proposed by Stacy(1962), with the use of hazard function and conditional survival function we can analyze the meaning of customer behavior under different parameter ranges. We used two datasets come from kaggle to make some models by different methods such as likelihood function or hierarchical bayes. Finally, we found the best model’s parameter range is identical to theoretical discussion. Otherwise, bayes model is better than likehood function method for small samples and the opposite is true for large samples. |