In the competitive race for guests, Airbnb fares better every year. As of April 2019, Airbnb had demonstrated a 45% increase in bookings year over year and a 62.5% increase in arrivals (iPropertyManagement). This success is due mainly to the company’s use of machine learning (ML) technology to optimize their search and booking process. Airbnb has invested heavily in crafting a personalized and highly streamlined guest pathway, and as Inc notes in an article aptly titled, Airbnb’s Biggest Weapon Against Hotels: Machine Learning, “Airbnb’s investment is paying off.” The article points out the company’s impressive increase in conversion rates — and the platform’s ability to get more people to make bookings, more quickly.
Hotels that embrace ML have a leg-up on Airbnb because they can implement the technology in far more areas of the guest experience. In addition to better search experiences and personalized upselling, hotels can utilize ML for revenue management, chatbots and digital concierges, in-room virtual assistants, communications throughout the guest stay, energy management, and behind-the-scenes efficiencies in housekeeping as well as food and beverage. However, many properties are still holding back due to common misconceptions about the technology.
The first step for most hotels toward taking advantage of ML to improve the guest experience and increase revenue is to understand the myths and let go of the overblown or even inaccurate ideas about ML and its risks – looking instead at the reality and the rewards.
Here are two of the biggest myths holding hotels back from the benefits of ML. (For more myths plus a book complete with strategies and use cases, see our new eBook: The Hospitality Executive’s Guide to Machine Learning: Will You Be a Leader, Follower, or Dinosaur?)
#1 – Reservations agents, revenue managers, and
front desk staff will become obsolete.
Many managers believe they will work themselves and their staff out of their jobs if they adopt ML technology. A survey by the Brookings Institute found that 52% of adults believe robots will be able to perform most of the activities humans do within the next thirty years (Brookings). The truth is that we are far from the level of deep learning (DL) that would be required for robots to do everything we do—and that there are things humans can do that machines simply can’t. Machines don’t strategize well. What they do well is to create probabilities. They can enhance our human efforts by doing what the human brain doesn’t do well, but the human component—especially in hospitality—is essential. What machines do well is tactical; what humans do well is strategic.
Companies like Nor1 are solving for the human component from the start. A core question we ask is how do reservation agents, revenue managers, and agents intersect with the technology? How will the technology help these team members do more of what they do well, including serving the guest? ML offers data in real-time that makes agents’ jobs more efficient and effective. In the long term, ML doesn’t mean that humans will be replaced; it means that our jobs will evolve, and in many ways, they will evolve to suit us better, as some of the work we find hard or impossible rolls over to ML. As Forbes Contributor Shep Hyken notes, “Can you imagine an accountant trying to do complicated tax work without the aid of a calculator? That’s how companies need to think of AI. It is an essential tool that will be, if it is not already, not simply a ‘nice to have,’ but a ‘must have’ technology” (Forbes).
Myth #2 – We’ve nailed personalization and don’t need ML for this.
This myth speaks specifically to the ways hotels tend to perceive ML and the necessity or lack thereof. Many properties have been gathering data for years now, though the data still tends to be siloed and fragmented. With this data, hotels have been quick to believe that if an email has the guest name and some preferential detail (i.e., she played golf with us once, so let’s send her a golf offer) that this qualifies as personalization. But this is something that happens late in the guest life cycle, once the guest has already stayed, and it’s not always correct (i.e., maybe she tried golf and hated it).
about the 50% of guests whom you have never had any correspondence with? How do
you personalize for those guests? It requires a different level of
understanding of the “unknown” guest to do this, and ML is the closest a
property can get to personalizing for the guest who has never made a
reservation. ML offers the ability to use vast amounts of data to microsegment
and create probabilities in merchandising and rates for guests you’ve never
interacted with. It’s as close to individual personalization as you can get.
Further, pricing and offer selection must go hand-in-hand when it comes to personalization. Offers presented with the ideal pricing for the particular guest are where ML does its most important work toward increasing revenue. Personalization is a bridge between RM and CRM. What to offer and at what price are equally crucial in personalized offers.
Airbnb knows that true personalization requires ML technology to aggregate data points about each guest in real-time to present the ideal set of listings. They have focused their ML technology on creating the savviest level of personalization to reduce abandonment, speed up guest decision making, and increase conversions. For instance, “search results aren’t generic, but rather based on your exact profile, your past behavior on the platform, and the behavior of users similar to you… data about how you react, including post-trip ratings and reviews, all goes back into the system.”
Hotels that want to compete not just with Airbnb but with their neighboring properties must elevate the guest experience by serving guests more of precisely what they want from the beginning. Because of its unique ability to process millions of data points in real-time, ML is among the only ways to achieve the level of personalization that guests seek. It’s a win-win; it also increases revenue, results in a happier guest, and, we believe, will make for more satisfied staff, who get to do more of what they do well in the long run.
For more myths and strategies, get the full eBook here: The Hospitality Executive’s Guide to Machine Learning: Will You Be a Leader, Follower, or Dinosaur?
About Jason G. Bryant, Nor1 Founder and CEO
Jason G. Bryant, Nor1 Founder and CEO, oversees day-to-day operations, provides visionary leadership and strategic direction for the upsell technology company.
With Jason at the helm, Nor1 has matured into the technology leader in upsell solutions. Headquartered in Silicon Valley, Nor1 provides innovative revenue enhancement solutions to the hospitality industry that focus on the intersection of machine learning, guest engagement and operational efficiency.
A seasoned entrepreneur, Jason has over 25 years experience building and leading international software development and operations organizations. Prior to Nor1, he founded and grew DRCI into a world-class offshore software development organization which maintained facilities in India and Mexico. DRCI focused on providing services to the travel industry, specialized in both high-volume transaction systems and internet technologies.
Jason is a frequent speaker on technology and entrepreneurship. He earned a Bachelor of Science degree from the University of Michigan. Jason also volunteers his time to a number of non-profit organizations. He currently sits on the boards of CYC – Giving Foster Youth A Voice and Impact As One, a Silicon Valley based technology non-profit.
About Nor1, Inc.
Nor1 is founded on a rich heritage of hospitality technology innovation. Using our wealth of industry knowledge, we are committed in the task of helping hotels upsell their rooms and attributes using our proprietary decision engine.
With over 3500 installs worldwide, Nor1 has developed new and innovative products and consultative services that empower today’s hotels to take control of their guest engagement and revenue destiny and grow their business more effectively.
Nor1′s real-time pricing and merchandising intelligence engine, PRiME®, powers eStandby Upgrade®, eXpress Upgrade™, CheckIn Merchandising™, eReach™, and eDirect™ to recommend the most relevant upgrade to the right guest at the right time for the most optimal price.
Investors include Concur Technologies, Goldman Sachs, and Accel Partners.
For more information, please visit www.nor1.com.
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