Guest Ratings Outshine Brand In Hotel Selection, According To New Study

Price and guest ratings carry more weight than brand value as key attributes to hotel property selection, according to a new study of more than 900 consumers by Expedia Group, the world's travel platform, and Unabashed Research - showing that independent hotels can more effectively compete today with their branded counterparts.

Unsurprisingly, price was by far the most influential driver of hotel selection, as consumers prioritize value above all else when allocating their travel budgets. Promotions and discounts that represent a real value to the customer is an almost guaranteed way to get hotel shoppers' attention and have them select a property.

"While consumers want the best deal on travel bookings, their individual selections ultimately reflect their values," said Abhijit Pal, head of research, Expedia Group. "The consumer searching for a budget accommodation will look for the best value within their constraints, while someone with more disposable income may prefer a luxury option and be willing to pay more per night, but not more than they have to."

Guest ratings have a strong influence on consumer selection, with a 72 percent chance that any consumer will value guest ratings higher than hotel brand, according to the study. In fact, consumers are willing to pay more for higher guest reviews, and considerably more so than for more premium brands. Participants overall were willing to pay more for a hotel with higher guest ratings: 24 percent more for a 3.9 rated hotel versus a 3.4 rated hotel, and 35 percent more for a 4.4 rated hotel versus a 3.9 rated hotel.

"Peer, or guest, ratings have essentially leveled the playing field for independent hotels, as more potential guests seek out third party endorsements for hotel properties they are considering," said Abhijit Pal, head of research at Expedia Group. "Independent hotels today can compete on a global scale with brands because distribution and technology enable them to compete, and quality is within their control."

Hotel brand did carry a slight advantage over other attributes, including remodel callouts, room image and hotel ratings (stars), according to the study. Premium brands showed more influence on selection, with shoppers rewarding those brands with some ADR premium, but not nearly to the extent that superb guest ratings do.

A recent Cornell University study, looking at over 95,000 reviews and ratings for independent high-end properties, found that "the key drivers in customer satisfaction remain service and room. Hoteliers should therefore focus on the operational areas that speak volumes about service and room, such as appropriately friendly service throughout the property, as well as the quality of beds and ensuring a good night's sleep for the guest. The traditional lodging service that delivers a good night's sleep in a clean, well-functioning room, together with availability of an excellent breakfast, remains central to customer satisfaction."

Room image, hotel brand, star ratings, guest ratings, recently remodel tags, and price are just a few of the key attributes that shoppers use to select hotels. Remodel tags, room image, and star ratings all had lower importance as individual features on likelihood for selection but can help sway the decision when combined with strong features on other attributes. To view the full report, please visit: https://discover.expediapartnercentral.com/2019/04/01/big-decision-guest-ratings-outshine-brand-hotel-selection/.

Methodology
The study included 903 general population participants in a choice based conjoint study to select hotel properties for two destinations of their choice. The study leveraged a Van Westendorp price sensitivity meter in order to generate pricing for 12,642 randomly generated search results, a representative room image for each property, and Expedia Group's most popular hotel brand in each star rating category. To determine the relationship between individual attributes and their likelihood of influencing the selection, a hierarchical Bayesian inference model was built using a Markov Chain Monte Carlo simulation.