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Discovering customer types using sales transactions and product availability data of 5 hotel datasets with genetic algorithm

Author

Listed:
  • Milad HajMirzaei

    (Shiraz University)

  • Koorush Ziarati

    (Shiraz University)

  • Alireza Nikseresht

    (Shiraz University)

Abstract

Demand forecasting is an integral part of every revenue management system. Demand raises from customers; therefore, knowing customers and their behavior is essential in this regard. Similar customers are grouped into a customer type. Discovering customer types from sales transactions and product availability data is a challenging topic. The basic idea of this paper is to use metaheuristic’s capability in exploring the search space instead of mathematical demand models in the research field of market discovery. In this work, a genetic algorithm is proposed to find efficient customer types. The main challenge of using a genetic algorithm in this field is to choose the proper fitness function. We use a two-phase fitness function for this problem to evaluate feasible and infeasible solutions. To evaluate the proposed method, a real publicly available dataset of five hotels is used. The results indicate that the genetic algorithm improves approximately 10% of the log-likelihood value of other proposed approaches with equal or lower number of customer types.

Suggested Citation

  • 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.
    • Handle: RePEc:pal:jorapm:v:19:y:2020:i:6:d:10.1057_s41272-020-00245-3
    DOI: 10.1057/s41272-020-00245-3
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. 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.
    3. 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.

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