Understanding customer's online booking intentions using hotel big data analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F25619161%3A_____%2F22%3AN0000004" target="_blank" >RIV/25619161:_____/22:N0000004 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/25619161:_____/22:N0000063

Výsledek na webu

<a href="https://journals.sagepub.com/doi/10.1177/13567667221122107" target="_blank" >https://journals.sagepub.com/doi/10.1177/13567667221122107</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1177/13567667221122107." target="_blank" >10.1177/13567667221122107.</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Understanding customer's online booking intentions using hotel big data analysis

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Understanding customer's online booking intentions using hotel big data analysis

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50204 - Business and management

Projekt

—

Návaznosti

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Vacation Marketing

ISSN

1356-7667

e-ISSN

—

Svazek periodika

13

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

4

Strana od-do

135-138

Kód UT WoS článku

000847837600001

EID výsledku v databázi Scopus

2-s2.0-85138314586