One of the best ways for hoteliers to engage guests is to offer them products and services that they’ll covet – and willingly pay for.
NB: This is an article from Oracle
That’s why upsells are vitally important, especially now as operators try to recover lost revenue. But the question remains: How do you pursue them without taxing already-stretched staff and resources?
Artificial intelligence, or more specifically, machine learning (ML) makes it possible.
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ML technology is critical to upselling because it takes the guesswork out of what to offer when to offer it, and how to price it. Hoteliers can focus instead on creating the ideal guest service experience, and Oracle’s Nor1 guest engagement suite provides machine-learning-based solutions to increase incremental revenue for hotels.
Upselling with ML uses millions of data points to generate offers that each guest is most likely to find attractive and thus accept. These decisions are generated in real-time, across the reservation lifecycle – from booking through post-stay, including digital and in-person touchpoints.
ML-generated offers can enhance stays by providing guests a variety of opportunities based on their reasons for travel and personal preferences. Such an upselling approach generates predictable, forecastable revenue, reaping the maximum possible amount while optimally balancing the house.
The precision and dynamism offered by ML cross three key areas: selection, presentation, and pricing.
Offer selection
Hotels have so much more to offer than an upgrade to the next room category. Room location, views, balconies—all these things are attributes of value to guests. Furthermore, we know that guests will pay for early check-in and late checkout, along with other non-room inventory products and services.
But not every guest needs to see every offer available at the property. Machine learning chooses what to offer the individual guest based on hundreds of data points, including:
- Relevance to the guest based on the reason for travel or guest preferences
- Product and service availability at the hotel at a given point in the reservation lifecycle
- Past performance of similar offers
- Guest’s past behavior (his/her specific engagement with offers at this property earlier in the reservation lifecycle)
With ML-generated offers, hotels move from static suggestions, like a suite upgrade, to a dynamic set of opportunities tailored to the guest. They rely not just on what has been booked, but also on what this guest – or guests just like them – have not booked. Factoring negative and positive information makes recommendations far more accurate, which increases conversion rate.
Priority presentation
Algorithms determine in what order the offers should be presented and delivered. Offer assortment (which products appear together and in what order) is a significant driver of conversion, leveraging buyer behavior dynamics of anchor and reference pricing.
The optimal number of offers will be presented at any time to avoid the paradox of choice. In addition, offers to guests will change as they move along the purchase pathway. For example, if a hotel guest has received an offer at booking and declines it, the offer likely won’t be presented again at check-in. Rather, a new offer will replace it.
Dynamic pricing
Historically, hotels have offered a suite upgrade for whatever price the revenue manager determined made sense for a given day, week, or month. The front desk, given instructions on the rate, gave each guest who walked through the door the same upgrade opportunity with the same static price. Typically, offers might also be distributed via email – again with no real consideration for guest preferences or pricing strategy.
By contrast, ML generated hotel upselling correlates and weights multiple data points to determine optimal pricing. Such factors include:
- Booked rate
- Room type delta or supplement
- Current rates
- Guest demand
- Room availability
As the system receives feedback from guests during the reservation lifecycle, it accrues more data points to enhance future decision-making. An ML platform uses this intelligence during every relevant interaction with the guest.
As recovery gains momentum, hoteliers will need to position themselves to capitalize on increasing demand. Engaging guests with relevant, timely, and appropriate hotel offers is an essential step in that effort.
