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

Milad HajMirzaei (), Koorush Ziarati () and Alireza Nikseresht ()
Additional contact information
Milad HajMirzaei: Shiraz University
Koorush Ziarati: Shiraz University
Alireza Nikseresht: Shiraz University

Journal of Revenue and Pricing Management, 2020, vol. 19, issue 6, No 3, 386-400

Abstract: 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.

Keywords: Revenue Management; Evolutionary algorithms; Genetic algorithm; Optimization (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://link.springer.com/10.1057/s41272-020-00245-3 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:19:y:2020:i:6:d:10.1057_s41272-020-00245-3

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

DOI: 10.1057/s41272-020-00245-3

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 ().