Three Reasons Why Machine Learning Is Critical to Upselling
As lockdowns lift and pent-up globetrotters make plans to travel the world, hotels will need to compete for guests. Certainly, every hotel operator will strive to make their guest experience as attractive as possible to bring their revenue streams back to pre-pandemic levels.
One revenue stream that has been traditionally elusive for hotel operators is upselling. For decades, hotels relied on the front desk to play a guessing game, where staff would try to identify upselling opportunities for callers and walk-ins. Yet, this method of upselling rarely allows hotels to fully capitalize on the opportunities that new bookings bring.
Machine learning (ML) can easily solve this issue by taking the guesswork out of the process. While front desk workers may fall prey to irrelevant information and subconscious biases, ML uses millions of data points to identify the right offer for every customer. Not only that, but artificial intelligence (AI) can also help the front desk agent choose the right time and the right price to optimize revenue.
With ML, gone are the days of flat rates. Instead of static offers, guests will be provided with recommendations that are uniquely designed for them. Using data, offers are generated in real time and customized to the guests’ personal preferences, points in the reservation lifestyle and prior activities.
Here are three ways hotel operators can differentiate themselves from competitors using ML-based upselling.
Offer more than just a standard room upgrade
Guests want more than just room upgrades. From room locations to attributes like balconies to customized check-in/check-out times, the potential upgrade offerings are truly endless. While a wide range of offers is desirable, hotels simply cannot present them all to every guest.
Using ML, hotel operators can offer true personalization, combining guest data with merchandising performance data. Aggregating data on the guest’s reasons for travel, the performance history of products, and similar guests’ interactions with offerings can lead hotels to provide the right offer to the right guest at the right price and time. Offers that are fully customized to the guest’s unique needs and desires will increase the likelihood that they’ll purchase the upgrade.
Show guests what they want
Presentation is important. Instead of displaying offerings in the same order to all guests, ML enables hotels to order products by how effectively they convert guests into consumers and display a unique offer set to each guest. It can even use consumer behaviors, anchoring and reference pricing to effectively lead guests to choose their desired offers. Moreover, instead of frustrating the guests by repeatedly providing the same offer, ML learns from previous guest behaviors in the purchase pathway and replaces old, declined offers with newer, more customized ones.
Determine the best price
The optimal price is one that both maximizes the hotel’s revenue and seems reasonable to the guest. However, what’s “reasonable” depends on the guest. Instead of settling for an arbitrary price for upgrades, ML uses dynamic pricing to make a real-time estimate of what the guest is willing to pay for the upgrade.
ML algorithms use weighted correlations including booked rate, room type data, current rates, guest demand, and room availability to predict at what rate the guest is likely to book the upgrade, while allowing the property to achieve the greatest revenue.
Effective upselling and personalization can distinguish hotels from their competitors. By going the extra mile to create a customized guest experience, hotels will win more customers. Additionally, ML enables hotels to effectively sell what’s already available instead of wasting time and energy creating new upselling opportunities. When done right, hotel operators can count on upselling revenue as a given rather than a bonus. To do this, hotels must implement precise and dynamic tools to identify what guests need and what they’re willing to pay for. Only ML can truly accomplish this.