Analytical Model of Customer Purchasing Behavior Considering Event Characteristics on Flower Delivery Business

抄録

Currently, a large amount of data accumulated in electronic commerce (EC) sites, and it is possible to customize a marketing strategy for each customer. This study focuses on an analytical model of purchasing data cumulated on a fresh flowers EC site for their marketing analysis. On an EC site providing fresh flower products, the purchase frequency per customer is much lower than that on EC sites selling commodity products or books. Therefore, the accumulated data for each customer on an EC site providing fresh flowers are limited and it is difficult to analyze the individual preferences of each user based on their purchasing history. On the other hand, fresh flower products are characterized by their purchase for several events in an individual’s life such as birthday and funeral. Moreover, the selected colors and prices of flowers tend to be different depending on the event. In this research, we focus on the relationship between the attributes of customers and purchasing actions, making it unnecessary to analyze the purchasing behavior of individual customers. We propose the analytical model considering the relationship between the event, the color and price of flower items, and other factors. Because customers’ purchase behaviors are very diverse, the variation in the relationship may be difficult to explain using conventional methods with hard clustering, wherein each datapoint belongs strictly to one class. Therefore, we propose a new latent class model to analyze purchasing behavior data. The proposed model is expected to reveal customers’ purchase behaviors and, thus, help to develop efficient marketing strategies. Moreover, we validate the effectiveness of our proposed method by applying it to the real purchase history data on the EC site.