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To this goal, the preferences tourists attach to price and seven other accommodation attributes rated by Internet users on Booking.com were revealed with the analytical hierarchy process\u00a0(AHP). Next, a two-stage clustering procedure based on these preferences was undertaken followed by profiling of the clusters in terms of their socio-demographics and travel characteristics. The results show that even if the ranking of the attributes is roughly the same for all the segments (with cleanliness, value for money, and location always in top four), all eight attributes effectively segment tourists into three clusters: \u201cquality-seekers\u201d (45% of the market), \u201cbargain-seekers\u201d (35%), and \u201ccleanliness-seekers\u201d (20%). The segments differ in terms of tourists\u2019 income and expenditures, type of accommodation, actual payer for accommodation, and trip purpose. In contrast, socio-demographics, and most tourists stay variables are alike across the segments. The proposed method of benefit segmentation provides a new perspective for an exploitation of eWOM data by accommodation providers in their marketing strategy.<\/jats:p>","DOI":"10.1007\/s40558-021-00200-x","type":"journal-article","created":{"date-parts":[[2021,4,19]],"date-time":"2021-04-19T20:03:50Z","timestamp":1618862630000},"page":"265-290","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Benefit segmentation in the tourist accommodation market based on eWOM attribute ratings"],"prefix":"10.1007","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8528-7391","authenticated-orcid":false,"given":"Karolina","family":"Nessel","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7705-4216","authenticated-orcid":false,"given":"Szczepan","family":"Ko\u015bci\u00f3\u0142ek","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1308-6803","authenticated-orcid":false,"given":"Ewa","family":"Wszendyby\u0142-Skulska","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4802-3231","authenticated-orcid":false,"given":"Sebastian","family":"Kopera","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,4,19]]},"reference":[{"key":"200_CR1","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1016\/j.chb.2017.05.032","volume":"75","author":"A Ahani","year":"2017","unstructured":"Ahani A, Rahim NZA, Nilashi M (2017) Forecasting social CRM adoption in SMEs: a combined SEM-neural network method. 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