Customer clustering based on a latent class model representing preferences for item seasonality

Masato Ninohira, Haruka Yamashita, Masayuki Goto

Research output: Contribution to journalArticlepeer-review


It has recently become easier for retail stores to obtain mass customer purchase history data. Analyzing these data, it is possible to understand the preferences of each customer and to use the results for marketing strategies. At the same time, it is important to take into account item seasonality in supermarkets planing marketing policies. It is, therefore, necessary to understand whether each customer purchases items based on seasonality throughout the year. In this study, we propose a new latent class model for analyzing customers’ purchasing behavior focusing on the seasonality of items, and demonstrate an analysis using our model. Moreover, we show that analysis of customers’ purchase behavior using both conventional latent class models and our latent class model provides more useful results than using only one model.

Original languageEnglish
Pages (from-to)195-206
Number of pages12
JournalJournal of Japan Industrial Management Association
Issue number4 E
Publication statusPublished - 2019


  • Aspect model
  • Customer segmentation
  • Item seasonality
  • Latent class model

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering
  • Applied Mathematics


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