Many hotels have unknowingly followed the myths for facts about ML not being as resourceful or it being a bad investment. It has hindered the business and has caused a loss of customers. Myths need to be broken, and reality should be understood, especially in hospitality.

FREMONT, CA: With competition for obtaining more guests, the online booking systems have been enhancing their features with each year. The success of online booking is mainly due to the usage of machine learning (ML) technology to optimize the search and booking process.

If the hotels embrace the use of ML as well, they can implement the technology in much better ways to enhance the guest experience. Additionally, to optimize search experiences and personalized upselling from ML can also be utilized for upping revenues. With that being established, revenue management, chatbots, and digital concierges, in-room virtual assistants, selective communications throughout the guest stay, energy management, and behind-the-scenes efficiencies in housekeeping, as well as food and beverage, can be monitored with ML applications. However, many hotels and properties are still holding back due to common misconceptions about the technology.

The initiation step for a hotel to leverage ML in the process is by improving the guest experience and for increasing the revenue. These will assist the understanding of myths and recognize how overblown and inaccurate the ideas are about ML and the risks it possesses. 

Myth 1 – Reservations Agents, Revenue Managers, and Front Desk Staff will Become Obsolete:

Many managers and staff workers believe that by leveraging technologies, their jobs might be lost. A survey conducted by Brookings institute has found that 52 percent of the adults believe that robots will be able to overtake humans within the next 30 years.

The advancements occurring in the technologies have been substantial over the past few years. The level of deep learning (DL) is still at a foundational level, and for robots to do a human’s job, the system needs to be highly sophisticated.

Machines create probabilities, but humans are good at strategizing. The machines can only enhance the actions carried out by the human brain. Especially in the field of hospitality, where the human element is necessary, jobs will not be lost.

Myth 2 – Hotels Already Know Personalization and Don’t Need ML:

The hotels perceive ML to be an un-necessary spend without any ROI. The necessity or the lack of it is mistaken, and for years now, many properties have avoided utilizing the technology. Many hotels have been gathering data, even though the data is siloed and fragmented. With this data, hotels have been misguided to believe that if an email has the guest name and some preferential detail that it qualifies as personalization. It is something that occurs late in the guest life cycle.  It does not mean the guests who have stayed in the accommodation before would like to do the same things, or they might not have liked the activities from experience.

It requires a deeper level of understanding of an unknown guest to apply a personalization. ML is the most accurate solution; a property can get to personalizing for the guest who has never made a reservation. ML offers the capacities to access and utilize vast amounts of data to micro-segment, create possibilities in merchandising, and rates for guests that have never before been interacted with. It’s as close to individual personalization a hotel manager can get.

Further, the pricing and offer selection must go hand-in-hand in the case of personalization. Offers presented with optimal pricing for the particular guests are where ML does its best work—increasing revenue. Personalization is a bridge to fill the gap between RM and Customer Relation Management. What can the hotel offer and at what price is equally crucial in personalized offers?

True personalization requires ML technology to congregate data points about every guest in real-time to provide the ideal set of listings based on priorities. The hotels can focus their ML technology on formulating the savviest level of personalization to reduce rejections, speed up guest decision making, and increase conversions. When considered, search results aren’t generic but based precisely on the profile, past behavior reviews on the platform. These reviews contain the experience of users with data about how the staffs have handled it, and post-trip ratings all going back to the database utilized by the hotels’ ML.

Hotels that desire to compete with its neighboring properties must elevate and out-perform the guest experience by serving guests their preferences. Because of ML technology’s ability to process millions of data points in real-time, it is one among the only ways to achieve such a deep level of personalization that guests seek. With leveraging the technology, the revenue is increase; guests are happier, which in turn increases the satisfaction of the staff and creates motivation to perform much better the next time. It’s a win-win scenario.