Detailed Information on Publication Record
2019
Behavioral Segmentation of Hotel Customers: An Empirical Study
CHALUPA, Štěpán, Jan CHROMÝ and Petr ČECHBasic information
Original name
Behavioral Segmentation of Hotel Customers: An Empirical Study
Authors
CHALUPA, Štěpán (203 Czech Republic, guarantor, belonging to the institution), Jan CHROMÝ (203 Czech Republic, belonging to the institution) and Petr ČECH (203 Czech Republic, belonging to the institution)
Edition
Granada, Proceedings of the 33rd International Business Information Management Association Conference, p. 2113-2119, 7 pp. 2019
Publisher
International Business Information Management Association
Other information
Language
English
Type of outcome
Stať ve sborníku
Field of Study
50204 Business and management
Country of publisher
United States of America
Confidentiality degree
není předmětem státního či obchodního tajemství
Publication form
storage medium (CD, DVD, flash disk)
Organization unit
University College Prague – University of International Relations and Institute of Hospitality Management and Economics, Ltd.
ISBN
978-0-9998551-2-6
UT WoS
000503988803057
Keywords in English
Customer segmentation; Cluster Analysis; Marketing Analysis
Tags
International impact, Reviewed
Změněno: 5/3/2020 07:52, Ing. Bc. Jan Chromý, Ph.D.
Abstract
V originále
The paper focuses on the use of cluster analysis for hotel customers’ segmentation and its possible application within the hotel marketing mix. Study works with Two-Step Cluster analysis that allows clustering of quantitative and nominal data. Main results show that using booking window (the period between reservation creation and date of arrival), distribution channel, length of stay (in days) and net room rate to cluster hotel customers into homogenous segments can be beneficial during the process of customer segmentation. Six basic customer segments were identified and lately described mainly by their behaviour in time, and money spends for a single reservation. The paper directly describes the whole methodology of Two-Step Clustering and possible outputs that can be used in revenue management research.