A computer told Ana Brant that the ultra-rich care deeply about their breakfast options. This came as a surprise.
Brant is the director of guest experience and innovation for the Dorchester Collection, a hotel group that counts among its properties the Beverly Hills Hotel, the Hotel Eden in Rome, and Le Meurice in Paris, where rooms start at $780 a night and wind their way up to over $16,000 for the Belle Etoile (“beautiful star”) suite. Her job, which she describes on LinkedIn as “the science of luxury service,” is to listen to the very wealthy people who stay in her company’s hotels, so they keep staying there instead of, say, the Peninsula, the St. Regis, or the Mandarin Oriental.
Hotels of this kind throw mountains of money at celebrity chefs to build fine dining destinations. Dinner is the main event. Breakfast is often an afterthought. And yet here was big-data proof — delivered by machine learning software called Metis, which analyzes online customer reviews — that Dorchester Collection guests write way more about breakfast in their reviews than dinner.
The algorithm found that the ultra-wealthy actually do like the idea of a buffet — but only if it comes in the form of a waiter who says he can make anything.
Metis also found that guests loved to customize their breakfasts; they were, as Brant put it, “looking at breakfast menus as an inspirational list of ingredients.” So she went to her chefs. It turned out Metis was right: Dorchester kitchens reported that somewhere between 80 and 90% of breakfast orders are modified.
So today, when you sit down to breakfast at the Beverly Hills Hotel (which has 1,019 reviews on TripAdvisor, 298 on Booking.com, 235 on Yelp, and 294 on Expedia), a waiter comes up to you and asks what you want — they’ve got everything. No menu.
“Guests love it,” Brant said. “It’s a Hollywood crowd. Everyone has their own diet.”
And it’s all because of an algorithm, one that could signal a new way for customer service businesses to study their clientele: through the collection and analysis of their own words.
In the past, luxury businesses have had to rely on “secret shoppers” and customer feedback forms to improve their service. Now, Metis is taking the massive trove of consumer data on customer review sites like TripAdvisor and Booking.com and turning it into market research that will tell businesses what their elite clients want, before they know they want it. It began with a little bit of customer feedback. Around five years ago, as review sites started to flourish, David and Kyle Richey, who for nearly four decades have run the luxury consulting firm Richey International, noticed that their clients were aghast.
“Hotels that we were dealing with were starting to feel overwhelmed by the amount of data that was coming at them,” said Kyle Richey.
Hotels didn’t know how to handle the sheer volume of feedback on the sites and saw it as a headache. But the Richeys — whose clients include the Ritz Paris, Viking River Cruises, and the NFL — saw it as a potential source of value. “We realized that there is rich content within the reviews, but everyone was using them for PR value,” Kyle Richey said. “No one was using them for operational value or strategy, because it’s hard to read thousands of reviews and find the trends.” In other words, businesses were slapping positive Yelp reviews on their windows, not using the feedback to improve.
In 2013, the Richeys started meeting with text analytics firms in the Bay Area, where they’re based, to develop a way to turn reviews into advice. But all of the firms’ proposals were overly complex. So they hired their own engineers to write machine learning software that could look for words and phrases that correlate with important customer service metrics like emotional bond and loyalty. Then they turned that software over to Werner Koepf, the senior vice president of engineering at Conversica, which makes AI for marketing and sales, to build a web app that their clients could use.
Finally, after two years and several million dollars of their own money, the Richeys were ready to demo Metis. Brant, their first client, was bowled over.
“I thought, Oh my goodness,” Brant said. “This is going to be the most amazing thing ever.“
In June 2015, Brant took a Metis demo comparing six ultra-luxury hotels in New York to a meeting of Dorchester Collection general managers in LA. The managers were impressed, particularly by a finding that a “super iconic and amazing hotel had a serious issue with leadership — people were running away when a customer complained.” They approved a Metis study on the spot.
So the Richeys ran Metis on over 8,000 TripAdvisor reviews, some on Dorchester hotels and some on competitors. That’s what led Brant to the realization that the ultra-wealthy actually do like the idea of a buffet — choosing exactly what they want — but only if it comes in the form of a waiter who says he can make anything.
“If you want to continue to be a true luxury, you have to figure out a way to draw insights that no one has ever had.”
Metis’s findings went further than breakfast. The analysis found that words related to relaxation and unwinding were closely correlated with words related to emotional bond and loyalty (words like “recommend” and “return”). In reviews of the Dorchester-owned Hotel Bel-Air, the software found that guests frequently mentioned words like “relaxation,” “unwinding,” and “pampered” alongside descriptions of patios, terraces, and fireplaces. Brant realized that photos on the hotel website didn’t emphasize the rooms’ outdoor features — a situation she quickly changed. Now the Dorchester Collection places Google keyword bids on words such as “fireplace” and “terrace.” (Companies pay Google for ads to show up next to search results for certain words.)
If the changes prompted by Metis seem granular — some music in the hotel bar here, an easily selfied vantage point there (“If the customer can’t insinuate himself into the view, it doesn’t exist,” said David Richey) — that’s sort of the point. The hidden desires of ultra-high-end hotel customers, who are used to an extraordinarily high standard of service, come down to the details. Differentiation happens at the margins.
“If you want to continue to be a true luxury,” Brant said, “you have to figure out a way to draw insights that no one has ever had.”
Yes, luxury hotels now have at their disposal a computer program that can divine the small details to lure the, as Brant put it, “C-suite executives, A-list celebrities, fashion executives, politicians, and notable businessmen” away from their competition.
And who, exactly, will be drawing these insights and adjusting these details? So far, the Richeys have used Metis for about 15 clients, including Viking River Cruises and a “major sports league.” That’s not for lack of demand: Kyle Richey said Metis has received “strong interest from major brands, including a very well-known Swiss jeweler.” The Richeys stressed, though, that the tool is in its early days and that they want to proceed slowly.
That said, their goals are huge. “My hope is that it will change the nature of market research,” Kyle Richey said.
That would mean, presumably, broadening Metis’s use past luxury industries and into the larger world of customer service. Could we one day soon see major changes to the Cheesecake Factory and Foot Locker based on an algorithm’s analysis of thousands of online reviews?
Perhaps, but don’t ask Ana Brant.
“I’ve always been in luxury,” Brant said. “I’m not sure what triggers the masses.”
- Tonight at 8:30pm ET, BuzzFeed is celebrating the biggest night in Hollywood, the Oscars, with a live show and tipsy game of bingo 🏆🍹
- Philip Bilden, the businessman nominated by President Trump to be Secretary of the Navy, has withdrawn himself from consideration.
- Actor Bill Paxton has died at 61. He starred in classic films including "Twister," "Titanic," "Big Love," and "Aliens."
- The Nokia brick phone is making a comeback — reimagined with a colored screen, but the game Snake hasn't gone anywhere 🐍📲
Report an Issue
Drag to highlight one or more parts of the screen.
We got your feedback, and we'll follow up with you at
Sadly, an error occured while sending your feedback. Please contact firstname.lastname@example.org to let us know.