Unlike many tech companies, Drift, a conversational marketing platform, and Wistia, a video marketing company, are best known for their outstanding customer service. While many people still imagine tech companies as silent rooms full of introverted engineers (except those of us in tech, who know better), tech companies that rely on the ideal combination of the human touch and technology are the companies that shine.
Take Wistia. It's a small company, and at some point, their three phone lines were ringing so much it became unsustainable. To find a solution, they looked at concrete data around the calls they received, such as support times, new client calls, and so forth. Ultimately, they made the controversial decision to remove their phone number from the website and rely on a chat widget—a dance between the people in the company and technology. Drift, on the other hand, ensures that all teams work directly with clients each week, engineers included. The idea came from CEO David Cancel's discovery that when customers spoke directly with engineers, the engineers were compelled to create a solution, sometimes on the spot. And clients were supremely happy.
So what does stellar tech customer service have to do with hotels? 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. Further, there are things humans can do that machines simply can't. Strategic revenue management, for instance. But when we talk about machine learning technology, hotel executives often think the prevailing idea is to replace humans instead of seeing it as a technology that supports service.
Just as with tech companies that excel at customer service, relationships are paramount. Similarly, technology that supports these relationships makes them better and more productive. For instance, nearly 80% of customers say they prefer live chat, which requires this fine balance of human engagement and technology. Customers cite the immediacy of chat compared to other channels (SuperOffice). The same goes for many other technologies, but for our purposes here, we will specifically address upselling technology powered by ML.
Consider what we ask the front desk agents to do—respond in a professional but warm and empathetic way to a variety of guests with vastly different wants and needs and process their credit card and room assignments as efficiently and quickly as possible. In the midst of this, we want them to intuit what the guests might upgrade to without necessarily having all the data (or possibly too much data to process in a short amount of time). We like to think that if we give the front desk guidelines and incentives that they'll upsell more effectively, but there are too many variables.
Upselling technology succeeds not because it replaces the front desk, but because it enhances their capabilities. Machines don't strategize well. What they do well is to create probabilities. They can strengthen our efforts by doing what our brains aren't as good at, but the human component—especially at the front desk—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 front desk agents interact with the technology? How will the technology empower 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. Over time, ML will allow our roles to 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.
Agents benefit from the tactics of ML upselling technology by being served the right offer for the right guest (at the right time). The right offer means the technology takes into account information about the reason for the guest travel and the history of how guests like this one have reacted to offers in the past, as well as considering details such as season and rate information. The right guest means that the technology knows that a business traveler doesn't need the Friday night family offer. A family doesn't need to see offers intended for couples, and so forth. When travelers see too many options, decision fatigue sets in, but ML can pare the offer set down to only what this guest is most likely to buy, which boosts the chances they will accept an upgrade. Finally, the right time. ML technology can serve up offers throughout the guest lifecycle—from pre-booking through the stay. However, once a guest has NOT accepted an offer, that one likely shouldn't be shown again, which means agents aren't continuing to sell a guest on an offer they don't want. As Sean O'Neill of Skift so aptly put it, "Treating a traveler as nothing more than a walking, talking, grab bag of preferences, attributes, and emotional buttons to be exploited for upselling will backfire on hoteliers." Instead of throwing darts at a board, or treating the guest as "grab bag of preferences," hotels can employ ML technology to accurately isolate what matters to the guest and offer a genuinely better experience based on those preferences. It creates value.
Agents benefit, as well. Having the offers for the specific guest on hand at check-in boosts an agent's confidence in offering it. Unfortunately, many agents skip the upgrade offer because they see it as an imposition rather than a value-add. Further, the agent isn't doing the mental arithmetic required to guess at what the guest wants; this increases the speed of service at the front desk and creates more time to build a relationship with the guest. The upselling technology supports the front desk in the same way other fundamental technologies do. As Forbes Contributor Shep Hyken noted, "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).
As we move through this particular technological cycle, the hospitality industry can and should look toward the cultivation of ML skillsets on property and look toward how technology can enhance service, not replace it. Team members should be educated on the skills they need to master to achieve a productive collaboration with the ML systems. Not only will this yield the most positive results, but it also assures your team evolves at the same pace as the technology, creating a potent combination.
For more about Machine Learning and the impact on the hotel industry, download NOR1's ebook The Hospitality Executive's Guide to Machine Learning: Will You Be a Leader, Follower, or Dinosaur?
About Nor1, Inc.
On November 18, 2020, Oracle announced that it has entered into an agreement to acquire Nor1. The acquisition extends Oracle Hospitality's OPERA Cloud Suite by adding Nor1's Merchandising platform that enables hotels to provide personalized offers throughout the guest journey using AI & machine learning, thereby improving guest engagement, and driving incremental revenue and improved loyalty for hotels.
Nor1 is the leader in hospitality upgrade, up-sell, and merchandising technology. Headquartered in Silicon Valley with offices across the world, Nor1 provides data-driven pricing and merchandising products that maximize incremental revenues for Hilton, IHG, Radisson Hotel Group, Accor, Wyndham, and other global hotels and resorts. Nor1′s real-time pricing and merchandising intelligence engine, PRiME®, powers eStandby Upgrade®, eXpress Upgrade™, and CheckIn Merchandising™,to recommend the most relevant upgrade to the right guest at the right time for the most optimal price. For more information, please visit www.nor1.com, or contact us at [email protected].