Over the past few years, the hotel sector has been affected by the rise of non-hotel tourist accommodation platforms, led by Airbnb, that have revolutionised the commercialisation of holiday stays.
In November 2016, an article published in the Journal of Revenue & Pricing Management, pointed out that «Airbnb and similar companies pose a significant threat to traditional hotels because they have understood, before hoteliers, how customers’ preferences have changed. And hoteliers, to react to these changes, would have to redesign their hotels and reinvent how they welcome guests to give them what they now expect: a sense of belonging».
The customer that nowadays opts to stay in a hotel has made a conscious choice: he has other options at his fingertips, and the assessment of his experience in the hotel will determine his future decisions, and also those of other potential clients. For this reason, it is particularly important that hotel managers understand their customers’ preferences and satisfaction level in an agile way. It is paramount to identify services that make customers prefer their hotels to a non-hotel accommodation, usually with no complementary services.
Traditional satisfaction surveys can help in this analysis. Mabrian’s Travel Intelligence platform goes beyond this, proposing a deep semantic analysis of the opinions expressed by clients on the different hotel review portals. A complete analysis of perceptions based on Natural Language Processing techniques and Artificial Intelligence using a sample of thousands of opinions (Big Data) expressed spontaneously by clients.
With this information, hotel managers can identify and measure their clients’ satisfaction level with their establishments in general, and with the specific services that compose it (cleaning, room, staff, food, etc.). This information is also segmented by different origin markets and hotel categories, and can be compared with other competitor establishments.
This information provides knowledge on which areas have met client expectations and which areas show a need for improvement, key for the hotels to focus their efforts and investments in the short and medium term.
Taking Rome, one of the main tourist capitals in Europe, as an example, the analysis of 784 hotel accommodations and over 45,000 reviews in 2017 shows clear conclusions.
While the overall average assessment of the hotel service is good (67.4 points out of 100), significant differences in satisfaction emerge between the US and UK markets, as well as between the Spanish and French. The latter ones are critical, leaving the hotel satisfaction index (HSi) only slightly above 50 points out of 100.
By focussing concretely on guest satisfaction with the different hotel services, illustrative differences can also be observed. For example, Spanish customers show a poor satisfaction level with aspects like the room, food and hotel reception, while the French are the ones that worst value the cleaning and location among the analysed markets. On the other hand, what must be highlighted is the high satisfaction level shown in general with the staff.
Mabrian’s platform collects, filters and analyses the opinions generated by customers through different web platforms. It offers measurable and segmented data that allows tourist business managers to make the best decisions, based on objective values.