I hate chatbots. This one I would hire.
Updated: 1 day ago
I hate chatbots that make me do their job. I’m trying to book a hotel and tell the bot who is travelling,, what matters to us and what I’m unsure about.
The bot replies: “You can explore our accommodation here.”
A link. Back to browsing the website, or straight into the hotel’s booking engine, where I still have to do the work myself.
Now I’m still opening tabs, comparing options and trying to work out which accommodation is the right one for my travel context. The chatbot has sent me somewhere else. It hasn’t helped me choose.
If that’s the experience on your website, you have automated the wrong thing: sending people to information instead of helping them make a decision.
As a guest, I’m now picking up the phone or booking somewhere else.
At Schladming Appartements,
guests get more than just a link.
On Schladming Appartements’ website, the conversation takes the guest from “Which apartment is right for us?” to a booking ready for payment. The bot recommends suitable apartments, explains the differences and guides the guest through choosing an apartment and rate—all in the chat. The chosen booking then carries straight through to the payment page, without another search or having to select everything again.
For example: “Which unit do you recommend for 2 adults and 3 children, and is the extra price worth it?”
A link from an ordinary chatbot leaves that question with the guest. Here, the answer puts the apartments next to each other and explains the trade-offs.The guest sees which features the apartments share, where they differ and what the price difference buys. And, I can even book it directly without leaving the chat.
The bot connects the price difference to concrete benefits: sauna access, a gym and proximity to the Reiteralm valley station. The guest can see what paying more buys.
The guest still decides whether those differences are worth paying for. But now there is something concrete to decide on.
The conversation follows the decision
1. “Give me three options and show me the trade-offs.”
Say a family likes several apartments but cannot decide where to start. If they ask for three suggestions, the bot narrows the options and explains the reasons for each:
Basecamp Reiteralm: sauna access and a location at the valley station.
Weisse Wand Alm: an alpine setting and a fireplace.
Haus Martina Top 5: a quieter, practical stay.
Now the family has a useful choice to make: easier access to skiing, evenings by a fireplace or a quieter base. They can follow up on whichever matters most.
For the reservations team, this is a familiar task: turn “we’re coming skiing with the family” into a useful shortlist. Here, the differences between Basecamp Reiteralm, Weisse Wand Alm and Haus Martina are explained within the booking conversation. The guest does not have to call simply to understand those options.
2. “Compare these two. Is paying more worth it?”
Then two apartments catch their attention. What is actually different—and is the extra cost worth it? The comparison shown above puts Basecamp Reiteralm 3.02 and Haus Martina Top 5 side by side.
The comparison places prices alongside location, accommodation type, bathroom features, outdoor space and amenities. Shared amenities are identified too, so guests can focus on the differences that matter.
The bot recommends an option and explains the value behind its price: sauna access, the gym, outdoor space and the Reiteralm location. The guest no longer has to piece the answer together from two listings. The differences are in front of them, with a reason for the recommendation.
For the hotelier, the important detail is what makes the higher-priced option relevant: sauna access, a gym and the Reiteralm location. These are concrete reasons a guest can assess. The conversation makes visible the differences a reservations colleague would otherwise have to explain.
Categories sell the hotel's definition of premium. Dynamic Inventory sells the guest's.
3. “I’ll take that one. What are my options?”
Once the guest settles on an apartment, the next question is about the rate. The conversation moves with them: optional services, non-refundable or flexible offers, and the cancellation and payment conditions for each.
The apartment is already chosen. Now the guest can weigh up price and flexibility without starting over. The booking is arranged in the chat: the guest chooses the apartment and rate, then reviews the dates, party and total in the booking summary.
From there, “Yes, book this” takes the guest straight to the payment page to complete the booking. No new search. No finding the apartment again. No choosing the rate a second time.
Operationally, the selected apartment and rate carry through to payment. The guest does not repeat the search or try to reconstruct the offer in a separate booking engine.
The strategic difference sits underneath the chat
Schladming Appartements manages more than 300 units across the village and surrounding valley. Different buildings, locations, layouts and reasons to book. The conversation is useful because those differences can become relevant offers.
If the inventory supplied to a chatbot consists only of room categories, those categories remain the basis of what it can sell. Better conversation alone does not turn a category into a bookable offer built around a guest’s priorities.
This is where a hotelier needs to look beyond the chat window.
A category-based PMS manages the inventory in its existing structure. Adding a chatbot does not change that structure.
An IBE selling that category catalogue gives guests a way to book it. It does not, simply by being online, create different sellable products around individual needs.
A chatbot using the same catalogue can explain and navigate it. It cannot offer a feature-specific product with its own availability, price and conditions unless the connected systems support it.
GauVendi’s Dynamic Inventory provides that structure. It connects individual accommodation features and locations to availability, prices and conditions, and makes different presentations of the same physical unit sellable as dynamically created, virtual products.
A family holiday, a stay for two and a workation can draw on the same underlying availability. Each offer brings forward the features and conditions relevant to that guest.
Location belongs in that structure too: proximity to the ski lift, the village centre or another apartment. For a family, easy access to the slopes may decide the stay. For friends booking separately, staying close to one another may matter more. That is what AI-ready inventory means here: information that AI can use to match a guest’s needs to an offer they can actually book.
The implication reaches beyond this chat experience. AI-based search, recommendations and booking applications all need access to what can genuinely be sold. Dynamic Inventory provides a foundation for those applications while the PMS remains in place.
Dynamic Inventory in numbers
Schladming Appartements reports the following results with GauVendi’s Dynamic Inventory:
Twice as many sellable products from the same physical units.
73% of bookings were for dynamically created (virtual) products.
42% of those guests chose to pay above the category price.
Accommodation type and location accounted for 75% of actively selected preferences.
The same apartments give guests more ways to find a relevant offer and more specific reasons to pay for the features and location they value.
More possibilities in the background.
Easier decisions for the guest.
The guest needs a manageable selection that fits their plans, with clear reasons to choose.
Dynamic Inventory makes more offers possible in the background. The conversation helps guests find the ones that fit their plans.
We call this dynamic matching of supply and demand.
Think of Spotify: the point is to put the right song in the right playlist for the moment. The same song can belong in a workout playlist, a road-trip mix or an evening at home. Its relevance changes with the context.
The same apartment can suit a family ski holiday, a couple’s getaway or a workation. Each context calls for a different presentation:
Family ski holiday: lead with proximity to the lift.
Couple’s getaway: lead with atmosphere and wellness.
Workation: lead with workspace and connectivity.
The unit stays the same. The reasons to choose it change. GauVendi’s Dynamic Inventory makes those presentations sellable as dynamically created, virtual products, connected to the same underlying availability. The relevant offer can then be matched to the guest’s needs and context.
Just as the right playlist makes it easy to find the right song, the right presentation makes it easier to choose an apartment.
One apartment. More reasons to book.
Our four-minute video, “Apartments & Vacation Rentals: One Apartment, Many Products”, explains how one apartment serves different guest needs while the PMS stays in place and booking portals keep receiving categories.
What this means for your hotel and your team
Your reservations team already knows why a guest should choose one room over another. Dynamic Inventory makes that knowledge usable in the booking journey: features, location, price and conditions, connected to the same underlying availability.
Schladming Appartements shows the result: relevant options, a clear comparison and the chosen stay carried through to payment. More of the routine decision-making happens in the conversation, leaving your team room for requests that need a person.
The competitive edge is inventory that AI can turn into relevant, bookable offers. More possibilities behind the scenes. Easier decisions for the guest.
Next time someone demonstrates a hotel chatbot, ask:
“Which one should I book and why?”
Then follow the answer through to payment. Does it help you book or hand the work back to you?
Sell the room, not the box.
Markus Mueller · CEO & Co-Founder, GauVendi
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