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Unlocking New Guest Value with AI-Powered Websites Part I: Customer Journey Context

With an AI engine powering the hotel's 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.
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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 Reeves is also the co-founder and CEO of Avvio (the developer of which was acquired by SHR in late 2022.

Static Versus Contextual Websites

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 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 – 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. 

Consider the following diagram of the customer journey:

customer journey diagram
customer journey diagram

Machine Learning at Each Interaction

What might this look like in practice (within only those initial stages of interaction for now)?

  • Interaction #1 (discovery): the AI can recognize the country where a customer is searching from and, if it’s overseas, rewrite the content to state, “Enjoy our spa after a long-haul flight.”
  • Interaction #2 (early prebooking): with the initial clicks acting as a profiling baseline of interests, the AI can showcase a review of the hotel’s dining outlet from someone in the user’s country
  • Interaction #3 (early prebooking): now with an expression of intent around dining and spa based on user clicks and country of origin, the AI can bring to the top the “Food and Spa LOS” package
  • Interaction #4 (late prebooking): now with the intended travel dates plugged into the IBE, the AI can highlight guestroom and suite reviews from past guests in the user’s source country
  • Interaction #5 (late prebooking): bringing together every interaction and learning about the prospect, the AI can reorient the rates and packages to create a hyper-personalized experience

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 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. 

Significantly, and as Reeves stresses, travelers take their time when selecting their host hotels. Thus, a user who drops off after Interaction #1 or Interaction #2 isn’t necessarily lost forever, even though traditional metrics may record them as such. In fact, just the opposite; they are likely to return and reevaluate a hotel, but entering the website with a different mindset. It’s then up to the hotel, as armed with new tools like’s reconfigurable website and IBE tools, to reorient the content and present the hotel in the most contextually appropriate manner possible.

It’s this context that can exponentially increase customer convenience, satisfaction and personalization; truly a new milestone in the utility of digital interfaces that will be achieved over the next few years. Stay tuned for Part II coming out shortly where we further explain how AI-powered websites gain in functionality over time to deliver revenues to a hotel across the entire customer journey.

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Together, Adam and Larry Mogelonsky represent one of the world’s most published writing teams in hospitality, with over a decade’s worth of material online. As the partners of Hotel Mogel Consulting Ltd., a Toronto-based consulting practice, Larry focuses on asset management, sales and operations while Adam specializes in hotel technology and marketing. Their experience encompasses properties around the world, both branded and independent, ranging from luxury and boutique to select service. Their work includes seven books: “In Vino Veritas: A Guide for Hoteliers and Restaurateurs to Sell More Wine” (2022), “More Hotel Mogel” (2020), “The Hotel Mogel” (2018), “The Llama is Inn” (2017), “Hotel Llama” (2015), “Llamas Rule” (2013) and “Are You an Ostrich or a Llama?” (2012). You can reach them at [email protected] to discuss hotel business challenges or to book speaking engagements.


This article may not be reproduced without the expressed permission of the authors.


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