A new latent class model for analysis of purchasing and browsing histories on EC sites

Masayuki Goto*, Kenta Mikawa, Shigeichi Hirasawa, Manabu Kobayashi, Tota Suko, Shunsuke Horii

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


The electronic commerce site (EC site) has become an important marketing channel where consumers can purchase many kinds of products; their access logs, including purchase records and browsing histories, are saved in the EC sites' databases. These log data can be utilized for the purpose of web marketing. The customers who purchase many product items are good customers, whereas the other customers, who do not purchase many items, must not be good customers even if they browse many items. If the attributes of good customers and those of other customers are clarified, such information is valuable as input for making a new marketing strategy. Regarding the product items, the characteristics of good items that are bought by many users are valuable information. It is necessary to construct a method to efficiently analyze such characteristics. This paper proposes a new latent class model to analyze both purchasing and browsing histories to make latent item and user clusters. By applying the proposal, an example of data analysis on an EC site is demonstrated. Through the clusters obtained by the proposed latent class model and the classification rule by the decision tree model, new findings are extracted from the data of purchasing and browsing histories.

Original languageEnglish
Pages (from-to)335-346
Number of pages12
JournalIndustrial Engineering and Management Systems
Issue number4
Publication statusPublished - 2015 Dec 1


  • Aspect model
  • Big data
  • Business analytics
  • Customer segmentation
  • Latent class model
  • Web marketing

ASJC Scopus subject areas

  • Social Sciences(all)
  • Economics, Econometrics and Finance(all)


Dive into the research topics of 'A new latent class model for analysis of purchasing and browsing histories on EC sites'. Together they form a unique fingerprint.

Cite this