A Study on Customer Purchase Behavior Analysis Based on Hidden Topic Markov Models

Abstract

Along with recent developments of Internet society, purchasing actions on E-commerce (hereinafter called “EC”) sites have become common for many consumers. On the other hand, it is known that the conversion rate (hereinafter called “CVR”) on EC sites is usually several percent at most. Therefore, many EC sites desire effective measures to improve CVR. In general, a user browses several pages on an EC site before he/she decide to purchase an item and it is considered that users’ intentions are reflected in their page transition tendency on an EC site. If a model analyzing the page transition data can extract users’ purchasing intentions, it enables to utilize the information for making a good promotion measure. Here, it is sometimes better to assume latent classes behind the users’ page transitions to understand their purchase intentions, because there are usually not only several user groups with different preferences but also plural states of purchasing intentions. However, previous models either assume the same latent topic on each page in the same session or assume a latent topic for each page every time. These models cannot handle situations where users’ intentions may change during browsing but not change frequently from page to page. In this study, we propose a purchasing behavior analysis model based on Hidden Topic Markov Models (HTMM). The proposed method can divide users’ browsing sequence into multiple subsequences with the same statistical characteristics according to latent topics estimated from page transitions. Then, the purchase probability of each latent topic can be obtained by using the purchase results obtained from the actual browsing history data together. By the proposed model, the purchase probabilities become possible to estimate the purchase intention of the users in real time and the information is effective for considering marketing measures. In this study, an experiment using real browsing history data is carried out and the effectiveness of the proposed method is demonstrated.