| Summary: | 碩士 === 國立臺灣大學 === 國際企業學研究所 === 100 === With the development of database marketing, firms owning customer transaction database are able to analyze customer purchasing behaviors that are helpful in the formulation of marketing strategy. Such purchasing behaviors as purchase quantity, inter-purchase time, purchase frequency, purchase amount and purchase incidence are comprised in customer transaction database; wherein, purchase quantity and inter-purchase time have significant managerial implications to manufacturers and retailers. Prediction of purchase quantity is helpful for manufacturers in production planning while it provides an estimation of customer demands to improve the display of shelf space and inventory control that avoids the risk of excess inventory and shortage in inventory. Moreover, the forecast of inter-purchase time is favorable for manufacturers in scheduling marketing activities whereas predicting purchase timing is advantageous for retailers to select target customers of a given marketing campaign, leading to effective cost reduction.
Latent hierarchical Bayes model developed by Chen (2005) was employed to analyze the scanner data from a noted supermarket in Taiwan in this research. In past decades, researches related to the modeling of purchase quantity and inter-purchase time assume that these two variables are independent. However, the latent hierarchical Bayes model integrates purchase quantity and inter-purchase time through inventory consumption behaviors. The empirical research in this study verified the excellence of the model along with a comparison with the performance of OLS estimators. The results of empirical research showed that demographics did not significantly explain the behaviors of customers purchasing rice and the possible explanations are presented in the last chapter.
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