How hoteliers can capitalise on the potential of AI
Hoteliers are telling us they are surrounded by tech-savvy guests, with many regularly turning to generative AI (GenAI) for recommendations on where to visit, stay, or eat. It can be exciting for travellers to discover hidden gems in cities they would have never otherwise found, or visit hole-in-the-wall cafes with unique menus. That heightened expectation of travel experiences is carried throughout their trips, which can add pressure on hoteliers to assess GenAI for how it can help to improve their business and customer experiences.
However, there are hidden gems within the world of AI itself that are worth exploring, too. Predictive analytics is an equally exciting yet lesser widely understood technology in comparison to GenAI. It can add just as much, if not more, value to hoteliers with the right planning and approach. GenAI and predictive analytics leverage machine learning (ML) in different ways, and knowing how to make the most of their capabilities could have a significant impact on a hotelier's bottom line.
Furthermore, Australian hotels are reporting a 15% increase in bookings for July and August in comparison to last year, demonstrating growing opportunities for customer acquisition and engagement. As hoteliers assess the best ways to capitalise on these market shifts, it will be critical to focus efforts and resources on new technologies in ways that add genuine and tangible value.
Here are three ways we're using AI to help boost our customers' bottom lines.
Predicting demand with machine learning
Many hoteliers are already in the habit of analysing data, with the goal of uncovering insights about how the business has performed. Assumptions are then made about the following weeks and months based on that historical data. In comparison to what machine learning can accomplish, these methods are more like educated guesses than predictions.
Hoteliers are often looking for data trends that could help to predict future demand, but it can be challenging even when just assessing their own data. Machine learning can not only do this more quickly and with greater analytical depth, but it can be used to cross-reference huge data sets to uncover trends or insights that hoteliers may have not even considered exploring.
For example, weather data is extremely complex. It involves extensive data points related to temperature, humidity, rainfall, wind, cyclone tracks, air pressure, and much more. Similarly, tourism data is multi-faceted. There are arrivals and departures, country or city of origin, demographic of visitors, length of stay, reasons for visiting, perk preferences, and even cancellation tendencies.
It wouldn't be within a hotelier's day-to-day capacity to be able to analyse their own data, plus the entirety of either of these data sets. This is where machine learning can create new possibilities. With tools based on machine learning simultaneously analysing a hotelier's own data, weather data, tourism data, flight data, and also data from dozens of booking channels from around the world to predict where and how demand will come to the business, hoteliers' ability to forecast, plan, budget and resource their accommodation is catapulted to a new level.
Furthermore, it's one thing to be able to predict demand a day or two in advance, which could empower a hotelier to make strategic last-minute offerings to customers looking for a booking at short notice. It's another thing entirely to empower hoteliers with demand predictions that look several weeks or even months into the future. The more time hoteliers have to prepare for peaks and troughs in demand, the more efficiently they can plan for hiring and staffing schedules, budget for advertising and marketing campaigns, and reach optimal occupancy rates.
Instead of panic-selling the day before a low occupancy period, hoteliers could avoid this period altogether with significant time to strategise in advance. With machine learning, hoteliers can go beyond analysing past trends and look ahead to understand how to most effectively engage with past customers, connect with new customers, expand into new markets, capitalise on the most meaningful platforms and distribution channels for their customer base, optimise their marketing strategies, and more.
Shifting from a sales lens to a customer lens
It's common for hoteliers to design their services alongside who they consider to be their competitors. This could include their pricing, cancellation policies, perks and benefits, communications strategies, and more. Historically, hoteliers have leaned on a sales lens to determine their competitors, most commonly looking at other accommodation providers in the same location, star-rating category, or with similar themes. This approach makes a lot of assumptions about who the customer is comparing the business to, rather than actually knowing how they are making decisions. This is where AI can again make a huge impact.
With AI, hoteliers can take the guesswork out of competitor or cohort analysis, and get a front-row seat to how customers are viewing hotels for their upcoming trip. Are they comparing hotels with a pool, B&B options that are pet-friendly, or accommodation on a train line that will take them straight to the concert they've got planned for the weekend? These are all questions that analysing historical data will provide very limited insights into, while AI can be used to answer these questions with tangible recommendations.
Over two decades, I've managed teams with billions of anonymised pieces of data at their fingertips, and seen first-hand how much value can be uncovered when that data is structured, analysed, and communicated in a comprehensive way to our colleagues, partners and customers. AI is the latest tool helping technology and data professionals help businesses run smarter, and for hoteliers, there's never been a better time to explore how these capabilities can help bring their aspirations to life.
