Given the increasing importance of electronic word-of-mouth (eWOM) in the global tourism market, the purpose of the study was to estimate weights customers assign to main attributes of tourist accommodations embodied in easily observed eWOM numerical ratings and subsequently to determine segments of customers with homogenous preferences. 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 (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: “quality-seekers” (45% of the market), “bargain-seekers” (35%), and “cleanliness-seekers” (20%). The segments differ in terms of tourists’ 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.

鉴于电子口碑（eWOM）在全球旅游市场中的重要性日益提高，该研究的目的是评估客户在容易观察到的eWOM数值评级中赋予旅游住宿主要属性的权重，并随后确定具有相同偏好的客户。为此，通过层次分析法（AHP）揭示了游客对价格的偏好以及Internet用户在Booking.com上对其他七个住宿属性的评价。接下来，基于这些偏好进行了两阶段聚类程序，然后根据聚类的社会人口统计学和出行特征对聚类进行了分析。结果表明，即使在所有细分受众群中，属性的排名大致相同（清洁度，物有所值，并且地理位置始终排在前四位），所有这八项属性都将游客有效地划分为三个类别：“质量寻求者”（占市场的45％），“讨价还价者”（占35％）和“清洁度”（占20％） ）。这些细分在游客的收入和支出，住宿类型，实际住宿付款人和旅行目的方面有所不同。相比之下，社会人口统计学和大多数游客留下的变量在各个细分市场上都是相同的。所提出的利益分割方法为住宿提供者在其营销策略中利用eWOM数据提供了新的视角。这些细分在游客的收入和支出，住宿类型，实际住宿付款人和旅行目的方面有所不同。相比之下，社会人口统计学和大多数游客留下的变量在各个细分市场上都是相同的。所提出的利益分割方法为住宿提供者在其营销策略中利用eWOM数据提供了新的视角。这些细分在游客的收入和支出，住宿类型，实际住宿付款人和旅行目的方面有所不同。相比之下，社会人口统计学和大多数游客留下的变量在各个细分市场上都是相同的。所提出的利益分割方法为住宿提供者在其营销策略中利用eWOM数据提供了新的视角。