The concept of a contemporary, boutique motel, either as a new build or retrofit, that commands sizably higher-than-market rates started to become a trend in the 2010s but has grown since the pandemic. Spurred on by a core psychographic of drive-to-vacation culture amongst Gen X and millennials, in the United States we are seeing multi-property brands like Bluebird and Urban Cowboy achieve success by opening exquisitely decorated lodges in sought-after rural areas near major cities. Then there are a host of independents gracing the covers of travel magazines such as The Vagabond Hotel in Miami, The Dive Motel in Nashville, Skyview Los Alamos in California and Thunderbird in Marfa, Texas.
The idea of an urban hotel embedding itself into the surrounding community is nothing new, but for many properties it remains just that: an idea that’s talked about in press releases and on the website yet seldom woven into every aspect of the operations and amenities. When a hotel does in fact go through the meticulous process of creating a strong sense of community, the results are nothing short of magical, and this is reflected through a positive impact on guest satisfaction, demand and the ability to grow rate.
Using interfaces to access the multitudes of information housed within a hotel s property management system (PMS), technology vendors have on their hands a literal treasure trove of first-party training data for machine learning applications. Any primary data extraction must nevertheless be focused around specific use cases that must be tested and verified before deploying in a live environment. Thus far, we ve seen lots of discussion around what s possible in the abstract for machine learning, but outside of perhaps chatbots and algorithmic rate optimization recommendations, many of the purported use cases are not practical for the here and now.
Today there are 850,000 open positions in U.S. hospitality and leisure with NO TAKERS. Similar is the situation in Europe and APAC: a lot of unfilled hospitality positions.
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