Technology

Robotic cleaning of lobby and corridor areas make sense, plus robotic grass cutting. Less relevant for room cleaning given the corners and obstacles, although Neom is exploring this. Car parking via flat bots that rise up and lift the car off its wheels also work well and can optimise space in limited car parks.

This year labor costs in hospitality consume 1/3 of hotel revenue (STR) and robotization and automation are becoming increasingly appealing to hotel owners and operators. Ex. Robots like Rosie by Tailos, thousands of which have already been deployed at various hotels, clean guest rooms 20 percent faster and public areas up to 80 percent faster than human housekeepers.

Of course! We've seen robots in progressive hotels and resorts for years. You're right that cleaning and innovative food delivery are the easier pickings. But we should discuss that bright red line that starts and ends with the front desk.

My take remains the same: there is no such thing as "total" revenue management. Revenue is revenue, period. When you buy a pair of Nikes at a sneaker shop, they come with shoelaces; shoelaces are not considered "ancillary" revenue.

The evolution of the Revenue Management discipline in our industry, since its inception 40 years ago, has been driven by large franchised organizations and management companies that care about RevPAR because that's how their business models are structured and that is how they are incentivized.

One of the big megatrends for hospitality is the increasing value of mixed-use branded properties where every revenue stream (rooms, ancillaries and stay-independent) acts to synergize each other. All the greatest hoteliers have tacitly appreciated this quality, especially in luxury, but they haven’t been able quantify the relationships between spending habits within different verticals because of the disconnected nature of software databases.

The question needs to be reworded to ask how Generative AI (Gen-AI) will transform the industry, as Predictive AI (P-AI) has been core to revenue management / pricing systems for decades. P-AI has also been fundamental to RPA - Robotic Process Automation - which links disparate systems that may not have APIs.

We will in a world of abundance: destinations, flight routes, accomodations, restaurants. This has created a paradox of choice wherein the discovery process of deciding where to go (for leisure), what hotel to book and where to dine is almost a chore in that it can take up a ton of time.

Let’s start with the second question. First, you need an AI strategy, and an AI strategy holds or falls apart with your data strategy. To use AI, you need to work with good, clean data. For example, suppose your systems are littered with duplicate profiles.

I believe there will be a large shift over a long period of time. However there's a visual aspect of information that doesn't exist in a query type conversational level. Visual and sound create the emotional reaction. So generative on an "answer my question" level I think yes, but not on an inpirational level.

I agree that we're witnessing the rise of a new, AI-driven interface to the internet, which will expand but not entirely replace today's web interface. GAI chatbots are the first step, worrying Google about the future of its profitable search engine.

In a word, no. Conversational interfaces have their place, but they would add friction to airline, hotel, and restaurant searches. Amazon some years back tried to promote and monetise voice search via its Alexa devices, but no one was interested.

Any hotel group which values the direct channel (brand.com) already knows that data science has been at the core of digital marketing in hospitality for many years. It's fundamental to customer segmentation, targeting and results measurement.

The data fragmentation in hospitality is a huge challenge the industry faces today. Guest and operational data lives in multiple "data islands" that do not talk to each other: PMS, CRS, WBE, CRM, ORM, CMS, DMS, Social Media and BI.

Data science is an area of expertise that needs specialists, similar to other areas like design and creativity. Typically, most companies have outsourced these non-core functions; in the case of creativity, to ad agencies, for example.

Behaviors of guests & sorting of all offerings (room, packages, meals, drinks, activities) based on previous behaviors. Much of this if done on a per person can be done using general purpose AI along with a just in time promp injection model.

The best data that a PMS stores is its guest history - travel purchase, rooms stayed in, ancillary purchases, number of occupants, special requests and so on. While we can discover some patterns to help grow the business, a machine can sift through this multitude far more efficiently to find patterns and do so in an unbiased manner.

PMS data is only one source of first-party data, and it's actually quite limited because it focuses only on past guests. A richer set of data is built when combining PMS with CRM data and direct website behavior data. Once powered by that dataset, some key practical applications are: Benchmarking: create automated competitive sets for each hotel and find opportunities for improvement.