By Laura Calin
There is much debate in the hospitality industry about the future of ancillary services and upselling. Some will argue that the ancillary revolution is not forthcoming, but the data begs to differ. A recent Oracle study found that 81% of hoteliers surveyed expect a big service model shift between now and 2025, and 49% strongly agreed that special amenities and upgrades are critical to their revenue strategy.
Moreover, customers seem game for this a la carte pricing. Over half of consumers, 54%, said they are willing to pay more to choose their view; 38% to choose their room; and 32% to choose their room floor; etc.
Look no further than the airlines to see how this strategy is working in the travel industry. We have the option to choose a seat and pay upsell fees based on its location, leg room, etc. We can often check one bag but have the option to pay for additional. Southwest offers customer the option to upgrade to early boarding for a small fee – one I am more than happy to pay to get a better choice of seat. These companies, and others, make it possible for hoteliers to position upselling as a means of creating a better experience for their guests, not just hawking products and services.
So, how can hoteliers emulate this strategy? Machine learning. This form of artificial intelligence enables hotels to capture and apply data to present relevant offers to guests in real time and maximizing total revenue per guest. This type of data isn’t the usual sort that hoteliers tend to fall back on. Historical data, especially in post-COVID times, is less valuable than it once was. What’s more valuable is data that illuminates why the guest is taking this trip right now, so a deep understanding of reservation data is critical – market segment, source of business, rate code, day-of-week check-in, etc.
For example, Mohegan Sun is a four-star diamond resort and casino in Uncasville, Connecticut. It is one of the largest casinos in the world with nearly 1,700 hotel rooms, more than 250,000 square feet of meeting space, 45 bars and restaurants, and a 10,000-seat arena for concerts and sporting events. To boost non-gaming revenues, Mohegan Sun is using Oracle Nor1 to upsell services and accommodations such as suites, early check-in, and late check-out. Since implementing Nor1, the property has had a 500% increase in upsells.
As guests move through the booking and pre-arrival phase, every additional guest interaction is valuable data. Did they click on the loyalty offer on the booking engine confirmation web page? Did they ignore an upsell offer for a suite on the confirmation email? Did they click on an early check-in offer on the pre-arrival email? Did they pre-register on their mobile device?
As a system begins to receive data during the reservation life cycle, personalization – even for guests who have never booked with your hotel before – becomes possible. Personalization at this level identifies different things about a guest all at the same time to understand what combination of attributes that guest values, at what price, and at what point in their journey the guest will be most likely to upgrade or request services or products.
When a guest checks in, the system can take into consideration what was already offered to the guest earlier in the reservation life cycle and how the guest reacted. The closest way to get to true personalization is using these buying signals specific to an individual guest. With that knowledge, the system can present recommendations to the front desk/reception agent in real time to inform their upsell offers for each guest.
Every guest wants a different experience. Some are willing to spend on a suite, some on food and beverage, and some on other offerings.
This has certainly been the experience Great Wolf Resorts has seen from its guests. From themed rooms and extra space to a ‘Wolf Pass’ offering access to waterparks and more, the resort merchandises a broad catalog of room options and attractions to cater to every segment and every guest. Rather than manually tracking upgrades against availability and spending staff hours on data entry, Great Wolf leans on Nor1 to automate the upsell process and connect with OPERA PMS for a clear view of inventory.
“We’re always trying to think of new ways to give people the experience they want,” explained Dave Van Saun, director, ancillary revenue at Great Wolf Resorts. “The way this manifests in revenue management is in offering different fare types, bundled offers, passes and more.”
One of the greatest values of machine learning is the feedback loop; that is, writing the guest interaction with the offer back into the data so the machine can learn what guests accept – and just as importantly, what they reject – to make better offer decisions in the future. The more buying signals the system can receive, the more targeted merchandising offers can become.
“We’re unique compared to most other hotels or even big Vegas resorts because we have more offerings under our roof, lots of attractions and extra items to entice guests,” added Van Saun. “We’ve always had a focus on ancillary revenue and have seen it as a good growth opportunity, but the pandemic allowed us to double down. Getting a guest who has already paid for a room to spend more to have a better experience is a win-win and delivers high margin revenue.”
Ancillary vs. upselling vs. cross-selling – it’s all the same: revenue generated by offering guests products and services relevant to their stay. The only way to generate sustained and forecastable incremental revenue is to automate it using machine learning.
