Information Technology & Tourism

, Volume 15, Issue 4, pp 365–394 | Cite as

Evidence of hotels’ dynamic pricing patterns on an Internet distribution channel: the case study of the Basque Country’s hotels in 2013–2014

  • Noelia Oses
  • Jon Kepa Gerrikagoitia
  • Aurkene Alzua
Original Research


The price is the single, most efficient tool that hoteliers have to adjust the demand and the offer in the short term. Dynamic pricing is the practice of changing the price charged for a product based on time. Using hotel room price data collected from an Internet distribution channel, this paper presents the research carried out to investigate the dynamic pricing practices of the hotels in the Basque Country in 2013 and 2014. The analysis shows that these hotels favour two price-changing patterns. The first pattern refers to the practice of changing a number of prices for contiguous, future target dates on the same date. The second pattern refers to the practice of changing the price a set number of days in advance of the target date. This paper and the research presented in it are an extension of the research published in the ENTER 2015 conference proceedings. The findings reinforce the previous conclusions by arriving to the same patterns after analysing data for a much wider geographical area, the Basque Country as opposed to Bilbao, and two years instead of one, 2014 as well as 2013 as previously.


Dynamic pricing Hotel room prices Internet distribution channel Revenue management Hotel competitiveness 


  1. Abrate G, Fraquelli G, Viglia G (2012) Dynamic pricing strategies: evidence from European hotels. Int J Hosp Manag 31(1):160–168CrossRefGoogle Scholar
  2. Bitran G, Caldentey R (2003) Commissioned paper: an overview of pricing models for revenue management. Manuf Service Oper Manag 5(3):203–229CrossRefGoogle Scholar
  3. Etzioni O, Tuchinda R, Knoblock CA, Yates A (2003) To buy or not to buy: mining airfare data to minimize ticket purchase price. In: 9th ACM SIGKDD international conference on knowledge discovery and data mining, pp 119–128Google Scholar
  4. El Haddad R, Roper A, Jones P (2008) The impact of revenue management decisions on customers attitudes and behaviours: a case study of a leading UK Budget Hotel Chain. In: EuroCHRIE conference 2008Google Scholar
  5. Gerrikagoitia JK, Alzua A, Ibarguren I, Roman I, Espinosa N (2011) Competitive intelligence applied to tourism destination management: hotel market monitor. In: Proceedings of the conference UNWTO Algarve forum on tourism and science: bridging theory and practiceGoogle Scholar
  6. Heerschap N, Ortega S, Priem A, Offermans M (2014) Innovation of tourism statistics through the use of new big data sources. Global Forum of Tourism Statistics, PragueGoogle Scholar
  7. Ivanov S, Zhechev V (2012) Hotel revenue management—a critical literature review. Tourism 60(2):175–198Google Scholar
  8. Kim WG, Kim DJ (2004) Factors affecting online hotel reservation intention between online and non-online customers. Int J Hosp Manag 23(4):381–395CrossRefGoogle Scholar
  9. Kimes SE, Wirtz J (2003) Has revenue management become acceptable? J Service Res 6(2):125–135CrossRefGoogle Scholar
  10. Lockyer T (2005) The perceived importance of price as one hotel selection dimension. Tour Manag 26(4):529–537CrossRefGoogle Scholar
  11. Magnini VP, Honeycutt ED, Hodge SK (2007) Data mining for hotel firms: use and limitations. In: Rutherford D, O’Fallon M (eds) Hotel management and operations, 4th edn. Van Nostrand Rheinhold, New York, pp 399–414Google Scholar
  12. Magnini VP, Karande K (2011) Understanding consumer services buyers based upon their purchase channel. J Bus Res 64(6):543–550CrossRefGoogle Scholar
  13. Möller M, Watanabe M (2010) Advance purchase discounts versus clearance sales. Econ J 120(547):1125–1148CrossRefGoogle Scholar
  14. Oses N, Gerrikagoitia JK, Alzua A (2015a) Dynamic pricing patterns on an Internet distribution channel: the case study of Bilbao’s hotels in 2013. In: ENTER conference on information and communication technologies in tourism, pp 735–747Google Scholar
  15. Oses N, Gerrikagoitia JK, Alzua A (2015b) Modelling and prediction of a destination’s monthly average daily rate and occupancy rate based on hotel room prices offered online. Tour Econ Fast Track. doi: 10.5367/te.2015.0491
  16. Roman I (2012) Measuring the influence of events in hotel room prices, Master’s thesis. Universidad del País Vasco-Euskal Herriko UnibertsitateaGoogle Scholar
  17. Roman I, Ibarguren I, Gerrikagoitia JK, Torres-Manzanera E (2013) Measurement of the hotel average daily rate using internet distribution systems. e-Rev Tour ResGoogle Scholar
  18. Tanford S, Raab C, Kim Y-S (2012) Determinants of customer loyalty and purchasing behavior for full-service and limited-service hotels. Int J Hosp Manag 31(2):319–328CrossRefGoogle Scholar
  19. Tso A, Law R (2005) Analysing the online pricing practices of hotels in Hong Kong. Int J Hosp Manag 24(2):301–307CrossRefGoogle Scholar
  20. Walchhofer N, Hronsky M, Froeschl K (2009) The online market observatory: a domain model approach. In: 2009 3rd international conference on knowledge science, engineering and management (KSEM), pp 229–240Google Scholar
  21. Walchhofer N, Fröschl KA, Dippelreiter B, Pöttler M, Werthner H (2009) Semamo: an approach to semantic market monitoring. J IT Tour 11(3):197–209Google Scholar
  22. Wöber KW (1998) TourMIS: an adaptive distributed marketing information system for strategic decision support in national, regional, or city tourist offices. Pac Tour Rev 2:273–286Google Scholar
  23. Wöber KW (2003) Information supply in tourism management by marketing decision support systems. Tour Manag 24(3):241–255CrossRefGoogle Scholar
  24. Wong J, Law R (2005) Analysing the intention to purchase on hotel websites: a study of travellers to Hong Kong. Int J Hosp Manag 24(3):311–329CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Centro de Investigación Cooperativa en Turismo, CICtourGUNEDonostiako Parke TeknologikoaDonostiaSpain

Personalised recommendations