Guest facing hotel technologies are undergoing a fundamental shift. Over the next few decades guest-facing technology is going to take a central and interactive role in hospitality. We already know it’s going to put a lot of people out of jobs, but still, it’s hard to imagine that interactive technology could ever actually ever take the place of the hospitality industry’s most valuable resource: helpful, friendly people. It is certain, though, that interactive smart technologies will add a new dimension to guest service that we’ve never seen before.
This article is about how interactive self-learning guest technology is going to fundamentally change the nature of hospitality, how that technology works, and why making the thoughtful technology investments today could give you a competitive advantage tomorrow.
The technologies you invest in today are key to ensuring you’ll have access to the data that you’ll need tomorrow to efficiently and effectively train your in-house guest-facing smart technologies. The best technologies to invest in are not just the ones that accomplish the tasks they’re built for, but, those that, like a good team member, also contribute to the optimal performance of others around them.
Data is the new oil and smart technologies aren’t just a fun way to use it. They’re a way create it. In fact, they’re the best way to create it, and the effects are compounding. Those who get started early collecting data for training smart technology are creating an opportunity to get ahead of the competition, and, because of the compounding nature of the technology, those who get ahead early can quickly be propelled surprisingly into strong positions of industry leadership.
Just look at Google
How Predictive Technologies Work
I dislike the term AI. For too many people it conjures up a misleading ideas of talking robots, super intelligence, and space station software gone mad. The vast majority of the AI-type technology people are working on now are algorithms designed to perform specific functions at which they can improve over time, like Google Maps and Netflix. In the next few years we can just expect to see these kinds of task-specific applications get more refined and complex.
These task-specific applications employ a branch of AI-technology called machine learning. Machine learning applications are designed to handle a specific task (figuring out what you’d like to watch on Netflix, for example). When they make predictions they receive feedback based on your reaction, which they then consider in future recommendations. Your personal Netflix also has the ability to examine the likes and dislikes of thousands of other users who have a taste profile similar to yours, find the movies you haven’t seen that are most popular among, and recommend them to you. Then, if it receives a positive feedback signal (you watch the whole movie) it has a little more data on what you like. If you watch 5 minutes and turn it off, it better understands what you dislike. That’s why, ideally, your Netflix recommendations should get better and better the more you watch it.
The problem with machine learning applications is getting them started. In the beginning, however, no matter how well programed an application is, it can’t know what movies you like. It can’t know what anybody likes. It only knows how to interpret signals about what people like (or dislike).
Machine learning applications need massive amounts of real-life behavior to examine and learn from before they can come anywhere close to predicting human tastes. Even good applications can have a hard time. I think we’ve all heard about a few hilarious products Amazon has recommended. Companies like Netflix and Amazon have enough data to provide basic training for your personal recommendation app bot before it starts recommending things to you. Although it may not know your taste, it can make some assumptions based on your age and gender and the things that other people of your age and gender like.
The same will be true of guest services technologies in your hotel. Predictive guest technologies will probably come from the supplier pre-trained with general data, but that will have about as much insight into your guests’ needs as a your new Amazon account has into your shopping habits. Having a robust system in place for generating real, deep guest preference insights will be a great advantage for training guest service apps (among all the other competitive advantages it provides). Building that system is not complicated. Is simply requires prioritizing guest preference insight collection when investing in new technologies.
We’re not quite at the point yet where people are chatting with the speaker in their hotel room about the restaurants in the neighborhood (or where the speaker has been trained to nudge them toward the hotel restaurants), but we’re not that far off either. People have been typing these questions into Google for years and they’re asking Siri for answers more and more.
Soon, each hotel will have its own smart technologies that understands its facilities, the guests, the neighborhood, and the city, better than Google ever could. An application will learn from guest room monitoring the woman in 715 got up at 4:00am on her first day, bought a coffee to go at 4:45, and then left the hotel in taxi. So, when the application senses her lights and shower on at the same time the following day, at 4:30 it asks her through the room speaker if she’d like a coffee and taxi waiting in the lobby, and sends the elevator to the 5th floor. The system will learn after how many days in the hotel a business guest will usually request laundry service and learn to offer it preemptively, creating a convenience to the man who wanted it and a subtle upsell to the one who was planning to put it off.
In the most ideal situation, a hotelier will have a robust set of insights about guest preferences and habits offsite. A deep understanding of how guests behave in-destination creates the potential for not only high quality recommendations, but generates potential new ancillary revenues streams through development of partnerships and even new service packages.
Although the future of hospitality has some major changes in store, the competition among hotels will remain remarkably similar to how it has always been.
At the end of the day, success in hospitality has always relied on knowing the customer better than the competition, and using that knowledge to create new revenue streams and deliver unbeatable service.
That hasn’t changed at all. Now we simply have a better tool kit.
Keep an eye out for my next blog post in which I’ll review and compare the tools in that kit.
SAS: Machine Learning: What It Is & Why It Matters
Forbes: What Is Machine Learning?
Skift: Travel Megatrends 2017: Artificial Intelligence In Travel Is Finally Becoming A Reality
Skift: Microsoft Bets On Artificial Intelligence To Help It Succeed Again In Travel
Event Manager Blog: 3 Hotels Using Artificial Intelligence To Improve Guest Experience
Fuse: How Personalization & Artificial Intelligence Are Taking Branded Content To The Next Level
Smart Investor: Data-rich Hotels Wow Guests
Eye For Travel: Bringing Predictive Analytics To The Hotel Industry
Hospitality Tech: Iot And Hospitality: The Evolution Of Customer Contact
Network World: How The Hospitality Industry Will Profit From The Iot
Hotel Business: At Wynn Las Vegas, Amazon Echo Checks In
Cytexone: 4 Ways Hotels Are Integrating The Internet Of Things
Sabre: Meet The Connected Traveler
Technology Will Replace 80% Of What Doctors Do
Aloft Hotels Unveils Voice-Activated Hotel Rooms