Customer type discovery in hotel revenue management: a data mining approach

Hamed Sherafat Moula (), S. Hadi Yaghoubyan (), Razieh Malekhosseini () and Karamollah Bagherifard ()
Additional contact information
Hamed Sherafat Moula: Islamic Azad University
S. Hadi Yaghoubyan: Islamic Azad University
Razieh Malekhosseini: Islamic Azad University
Karamollah Bagherifard: Islamic Azad University

Journal of Revenue and Pricing Management, 2024, vol. 23, issue 3, No 5, 238-248

Abstract: Abstract Demand estimation is a fundamental component of revenue management systems. The demand for a product can be ascertained from the customers who purchase it. Identifying customer types in this context is a challenging endeavor, recently resolved using meta-heuristic and mathematical techniques. Meta-heuristics leverage the scarcity of data in the search space, commencing with random samples and employing the fitness function as a guide during operations. Our proposed approach generates the search space by incorporating supplementary data to identify valuable customer types. We employ a new period table with additional data to achieve this objective. Subsequently, we reduce the search space through data mining's clustering method and ultimately employ a greedy algorithm and fitness function to identify valuable customer types and construct our solution. To validate our approach, we compare our solution and the most recent research in this field, including genetic, memetic, and mathematical approaches. Compared to memetic methods, our results indicate that our solution has a smaller length, with a maximum reduction of 34%, and exhibits improvement in log value, with a maximum of 7%.

Keywords: Revenue management; Data mining; Clustering; Customer type (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1057/s41272-024-00474-w Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pal:jorapm:v:23:y:2024:i:3:d:10.1057_s41272-024-00474-w

Ordering information: This journal article can be ordered from
https://www.palgrave.com/gp/journal/41272

DOI: 10.1057/s41272-024-00474-w

Access Statistics for this article

Journal of Revenue and Pricing Management is currently edited by Ian Yeoman

More articles in Journal of Revenue and Pricing Management from Palgrave Macmillan
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().