Skip to main content

Location-Based Machine Learning Gives Hotels the Power to Meet Guest Demands

For hotels focusing on personalization, there is smart and then there is smarter. To stand out in a crowded market, many “smart” hotels are making big bold moves to get personal with their guests.  To win the loyalty game, some hotels are creating custom concierges – specialists hired to meet a unique niche of demands and services.
A recent article in the Boston Globe titled “Need a book, a crib, or running pal on vacation? There’s a concierge for that,” highlights the new roles of Book Butlers, Jogging Concierges, Baby Concierges, exotic “bird whisperers” in Aruba, and in Hawaii a “Director of Aloha.” These innovative roles, while creating a unique experience, also put a great strain on hotel staff bandwidth. Not every hotel is in a position to fund these positions, but through mobile engagement, there is a way to meet the complex expectations of guests. In this article, RoamingAround discusses some of the ways hotels could engage in mobile engagement to their benefit.
Personalization for boutiques and big brands
Through brilliant advances in mobile engagement and machine learning there is now a cost effective way to put an expert concierge in every guest's pocket. An expert that knows guest preferences, learns about their individual behavior and only serves them with what they want – when and where they want it.
Because today’s travelers think beyond the room and focus on what is going on in the surrounding neighborhood, they can use their own mobile device to  “RoamAbout” the town to find inside information, exclusive offers and receive personalized content. A personalized experience for every guest is the new reality.
Any hotel can create a personalized experience for every guest that wants one. It’s simply a matter of bridging old world hospitality with the latest advancement in mobile engagement. Standing out in a crowded market has never been more scalable, affordable and rewarding.
Get smarter
Location-based machine learning takes as its foundation an opt-in whereby the guest allows a mobile app to submit information about geographical points throughout the day. This information can be submitted anonymously or tied to a unique advertising ID. Geographical data and dwell time can then be correlated against online rosters of locations with category keywords to make inferences about a user’s preferences, buying habits, and demographics. It can supplement declared interests, spend events, and all other types of online data to grow exponentially better profiles over time. The patterns it identifies are more meaningful. It knows the difference between an inference and an assumption. It runs in the background. It’s easy on the battery.
Measuring success
To be successful, it must also be precise and it must be tactful, the secret to mobile engagement being never to annoy, embarrass, or inconvenience the customer — because that line of communication is always just a click away from being cut off permanently. Capitalizing on a guest’s interest at the right time in the right place establishes the hotel brand as valuable, personalized and trusted.
When data is aggregated from large sample sizes, it can give absolutely stellar reports. How often and in what locations do loyalty users visit the competition? What can hotel owners infer based on their movements about age, sex, household income, and spending habits? How can hotels keep the relationship with them warm while they’re off-property, even out-of-town, with deals and other kinds of communication that are relevant to their real interests?
Mobile engagement is ripe for implementation because so many hotels see that the demand for a personalized experience is now an affordable reality with huge returns.
This ad will auto-close in 10 seconds