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Discovering heterogeneous consumer groups from sales transaction data

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  • Lee, Haengju
  • Eun, Yongsoon

Abstract

We propose a demand estimation method to discover heterogeneous consumer groups. The estimation requires only historical sales data and product availability. Consumers belonging to different segments possess heterogeneous preferences and, in turn, heterogeneous substitution behaviors. For such consumers, the latent class consumer choice model can better represent their heterogeneous purchasing behaviors. In the latent class choice model, there are multiple consumer segments, and the segment types are not observable to the retailer. The expectation-maximization (EM) method is developed to jointly estimate the arrival rate and the parameters of the choice model. The developed method enables a simple estimation procedure by treating the observed data as incomplete observations of the consumer type along with consumer’s first choice. The first choice is the choice before the substitution effects occur. We test the procedure on simulated data sets. The results show that the procedure effectively detects heterogeneous consumer segments that have significant market presence.

Suggested Citation

  • Lee, Haengju & Eun, Yongsoon, 2020. "Discovering heterogeneous consumer groups from sales transaction data," European Journal of Operational Research, Elsevier, vol. 280(1), pages 338-350.
  • Handle: RePEc:eee:ejores:v:280:y:2020:i:1:p:338-350
    DOI: 10.1016/j.ejor.2019.05.043
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    Citations

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    Cited by:

    1. Wang, Xiaolin & Zhao, Xiujie & Liu, Bin, 2020. "Design and pricing of extended warranty menus based on the multinomial logit choice model," European Journal of Operational Research, Elsevier, vol. 287(1), pages 237-250.
    2. Hamed Sherafat Moula & S. Hadi Yaghoubyan & Razieh Malekhosseini & Karamollah Bagherifard, 2024. "Customer type discovery in hotel revenue management: a data mining approach," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 23(3), pages 238-248, June.
    3. Milad HajMirzaei & Koorush Ziarati & Alireza Nikseresht, 0. "Discovering customer types using sales transactions and product availability data of 5 hotel datasets with genetic algorithm," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 0, pages 1-15.
    4. Milad HajMirzaei & Koorush Ziarati & Alireza Nikseresht, 2020. "Discovering customer types using sales transactions and product availability data of 5 hotel datasets with genetic algorithm," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 19(6), pages 386-400, December.
    5. Milad HajMirzaei & Koorush Ziarati & Alireza Nikseresht, 2022. "A customer type discovery algorithm in hotel revenue management systems," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(2), pages 200-211, April.
    6. Hamed Sherafat Moula & S. Hadi Yaghoubyan & Razieh Malekhosseini & Karamollah Bagherifard, 2023. "Customer type discovery in hotel revenue management by Memetic algorithm," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 22(6), pages 470-481, December.
    7. Berbeglia, Franco & Berbeglia, Gerardo & Van Hentenryck, Pascal, 2021. "Market segmentation in online platforms," European Journal of Operational Research, Elsevier, vol. 295(3), pages 1025-1041.

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