Throughout the history of technology adoption, a new invention doesn’t necessarily take off because of direct, apples-to-apples savings versus the old guard, but rather because of the new value that this innovation unlocks, at first on an incremental then later on an exponential basis.
The most studied example of this was the advent of electricity brushing up against the entrenched steam power-oriented factories during the turn of the 20th century. In pitching their wears to factory owners, electric motor companies saw little success by citing the one-to-one cost savings from shifting away from coal, wood and other means of localized steam generation. The nuisance of exchanging machinery was simply too great – both financially and emotionally – for the factory owners.
Instead, the electric motor salespeople realized success when they emphasized how electricity enabled a groundbreaking reconfiguration of manufacturing processes within the factory floor, moving away from cramped, multistory buildings in the heart of the city and into flexible, single-floor arrangements that could be cheaply built in the suburbs. One early adopter who saw the potential for this new design paradigm was Henry Ford with his electric-powered assembly line, and the rest is, well, history.
This same narrative played out during the late aughts with the battle of Blackberry’s email-specific mobile phone versus the newer app-driven iPhone, or with the dawn of Netflix’s streaming service that usurped Blockbuster’s video rental stores. This value-driven innovation principle is relevant today as we transition from a static internet of one-size-fits-all websites into ones that use artificial intelligence (AI) to match the website content to each guest’s needs as this guest moves through the customer journey.
To showcase how AI-enabled websites and integrated booking engines (IBEs) can unlock substantial new economic value for hotel properties, we sat down with Frank Reeves, Chief Evangelist at SHR Group, to discuss the company’s next-generation website and IBE platform called allora.ai. Reeves is also the co-founder and CEO of Avvio (the developer of allora.ai) which was acquired by SHR in late 2022.
While framing the advent of next-gen hotel websites within the context of steam giving way to electric power may seem a bit grandiose on the surface, this comparison of historic events to inform predictions about present-day outcomes for hotel tech trends is in fact similar to what the machine learning (ML) propelling allora.ai have been doing for quite some time now.
The more contextual data and interactive feedback the system has – as derived from the entirety of guests accessing websites or going through a hotel’s IBE built by allora.ai – the more the platform learns what a guest might want. The outcome here is that the ML can better predict the optimized orientation of a website’s content in order to achieve a specific goal, such as boosting reservation conversion rate.
As Reeves puts it, our current websites are ‘static digital brochures’. A customer enters, and while tracking mechanism like pixels may tell the analytics platform where this user came from (IP address, mobile versus desktop, organic versus paid ad and so on), the website doesn’t react or A/B test how the information is presented in order to better fit the context of the customer.
Now, however, with an AI engine powering a website, a hotel can present information that’s fully personalized to each guest, no matter which stage of the customer journey they are presently on.
The Paradigm Shift in New Economic Value from AI-Powered Hotel Websites — Source: Hotel Mogel Consulting Limited
How might this look like in practice within only those initial stages of interaction for now?
When you consider the website up until the point at which a guest departs the hotel, a lot can happen that can influence hotel revenues. Importantly, it’s the context of the guest that’s changing. A person who lands on a website during the initial discovery or dream phase may not have the intent to book just yet; they are simply on a fact-finding mission. When they exit a static website after this first or second interaction, traditional metrics record this as a lost customer, even though this isn’t necessarily true.
Suppose that during the first browse, this particular user seems especially interested in the spa as determined by what they click on and time spent on certain webpages. Armed with this information as well as previous learnings from past interactions with other similar customers, the allora.ai platform would then rearrange the website’s information to better personalize it for what’s deemed to be a spa seeker. For example, upon returning to the website, wellness-oriented imagery would be prioritized in the homepage slider, while rows of spa content would appear ahead of rows about, say, golf or F&B.
The more a user returns to the website, the more the AI can optimize the configuration of content based on how the prospect interacts. This also applies to the IBE, where past reservation searches are stored for the guest to pick up where they left off once they revisit the website. And, of course, the IBE can be set up to continuously A/B test various offers for even more insights about what drives bookings.
Refinement after refinement at each customer click, the flywheel of ML learning and feedback data means that a website can automatically cater the content to what each individual wants to help move them down the sales funnel and, ultimately, into a finished reservation. Managers can also set up rules to incentivize results, such as having the website display a direct booking discount or F&B credit when a user returns for, say, the fourth time onwards or after two weeks have passed since the first viewing.
Finally, it goes without saying that all these content optimizations help immensely with a channel shift away from the high-commission OTAs. No third party will ever be able to offer as personalized and as pleasurable a user experience, resulting in reduced costs per acquisition and greater net revenues.
As a digression here, it’s important to consider why many customers prefer the OTAs in the first place. Yes, there’s the convenience of aggregating all properties in a location, but it’s also because of guest frustration with a brand.com. Website hyper-personalization via an AI backbone fixes this issue, really bringing the hotel’s hospitality into the online realm.
The value that AI-powered websites will add to the prebooking phase is by itself remarkable because of how it can help brands to micro-segment customers within the sales funnel, so much so that marketers can now accurately separate the upper funnel from the lower funnel or even the ‘upper-upper funnel’ with specific insights at each partial phase on what motivates guests to move in the right direction.
But, why stop at using websites solely to drive guestroom and package sales? What can an AI-powered website do to boost ancillary spend (TRevPAR) or keep the relationship going after departure?
Take a London hotel that we work with, for example, commented Reeves.
We observed from both past guest behavior on this brand’s website and from the totality of interactions across all hotels using the allora.ai platform that travelers who had already made a direct booking and who originated from the United States have vastly different needs leading up to arrival over those travelers coming in locally from within the United Kingdom. Monetarily speaking, past US travelers highly favored F&B content, so prioritizing the display of various dining options for incoming guests from the US resulted in more prearrival F&B revenue per guest and more on-premises utilization.
To expand on this example a bit further, the true power of ML comes not only from how past users interact with your own website, but also from how all users across all websites have behaved. It’s this combination of macro (systemwide patterns) and micro (feedback on your own brand.com) that has allowed allora.ai to attain a minimum efficient scale for any new property to leverage past learnings.
Such a blend of macro and micro opens a wholly novel website functionality: reducing cancellations. Because the system knows who is more likely to cancel a reservation during the prearrival phase based on past cancellation data from all travelers at all properties on the platforms, managers can use this information to proactively send out cheerful reminders in the days leading up to arrival or even send out additional incentives to those ‘risky’ guests such as F&B vouchers.
The analog of a flywheel spinning across the entirety of the customer journey to unlock value at each stage really works here. Each website or booking engine interaction provides feedback to fuel improvements for future interactions. This can be broken down as follows:
To close, consider how a hotel can get ‘more juice from the squeeze’ at each point in this chain by leveraging the latest in artificial intelligence and machine learning. Every optimization works to increase automation so that teams can be more productive per unit time while also providing those teams with more accurate insights to inform other operational improvements.
At discovery and prebooking, we’re talking about increasing the conversion rate versus the competition and driving more traffic away from the OTAs, enhancing both gross revenues and net revenues that flow through to net operating income (NOI). At the prearrival stage, we’re talking about TRevPAR and fewer cancellations, both of which also augment NOI. Then for onsite and beyond, we’re talking about how the website can lead to better satisfaction scores which in turn enhance word of mouth and return visits to further lower future customer acquisition costs.
Taken together, all this acts to boost the overall economics of a property and its long-term asset valuation. If the static brochure websites of yesteryear are like steam power, then ML deployments truly are the electricity upgrade your hotel needs for the decade ahead.