In retail businesses, it is important to grow customers into good customers in order to increase and maintain sales. Therefore, many researches have clarified the differences of the purchasing tendency between good and other types of customers; however, in fact, it is hard to say that many retailers can take useful measures for acquiring good customers effectively by taking into account the purchase tendency. On the other hand, a latent class model has been studied as an analysis method to grasp customers' preferences and purchasing trends from purchasing history data. In this study, we assume latent classes behind customers' purchasing data, and apply the Latent Dirichlet Allocation model to represent the differences in customers' preferences. This model enables grasping the tendency of purchasing items in each latent class considering diversity and heterogeneity of the features of customers and items. Moreover, we propose a method to extract important items for each customer in terms of turning him/her into a good customer based on the k-nearest neighbor algorithm by using the analysis result of purchasing trends in each latent class. Furthermore, we apply the proposed model to actual purchasing history data and show the possibility of its application in practice.