2024
Understanding customer's online booking intentions using hotel big data analysis
CHALUPA, Štěpán a Martin PETŘÍČEKZákladní údaje
Originální název
Understanding customer's online booking intentions using hotel big data analysis
Autoři
CHALUPA, Štěpán a Martin PETŘÍČEK
Vydání
Journal of Vacation Marketing, Sage, 2024, 1356-7667
Další údaje
Jazyk
angličtina
Typ výsledku
Článek v odborném periodiku
Utajení
není předmětem státního či obchodního tajemství
Organizační jednotka
University College Prague – Vysoká škola mezinárodních vztahů a Vysoká škola hotelová a ekonomická s.r.o.
UT WoS
000847837600001
Klíčová slova anglicky
Price elasticity of demand, hotel market segmentation, cluster analysis, hospitality marketing, hospitality e-commerce
Příznaky
Mezinárodní význam, Recenzováno
Změněno: 4. 6. 2024 09:20, Ing. Štěpán Chalupa, Ph.D.
Anotace
V originále
The presented article focuses on the issue of customer segmentation in the hospitality industry and its use for price optimisation. To identify the market segments article uses the Two-Step cluster analysis. The analysis was based on the seven variables (length of stay, average room rate, distribution channel, reservation day, day of arrival, lead time and payment conditions). Six clusters were identified as following segments: Corporates, Early Bird Bookers, Last Minute Bookers, Product Seekers, Values Seekers and Last Minute Bookers. To optimise the price for these segments, article works with the coefficient of price elasticity of demand for the presented market segments. The price elasticity of demand is measured by the log-log regression analysis. Data were colected from the four-star hotel in Prague, Czech Republic and analysis is based on more than 9000 transactions. Last minute bookers segment was the only one with the positive coefficient of price elasticity and has the lowest value of lead time (9,27 in average). Product seekers have the highest coefficient of price elasticity (−3,413) and highest average room rate (3573 CZK in average). Early bird bookers, Long time stayers, Corporates and Value seekers was identified as pricely inelastic.