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DataNexx

Data category

Hospitality & Travel Data

Bookings and cancellations, pricing and occupancy history, guest reviews with responses and service recovery.

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Hospitality data for AI, also called travel and hospitality data, links price, demand and guest experience over many seasons: bookings and cancellations, rate and occupancy history, reviews with the staff response and what happened next. AI teams use it for demand, pricing and guest-service models. Service recovery records are rare and useful. DataNexx helps hotels, travel firms and restaurant groups license these records without guest identities.

Why it matters for AI

Recorded, judged, resolved.

Hospitality records link price, demand and guest experience over many seasons. Reviews paired with the staff response and what happened next show how service problems are actually resolved. Guest identities are removed before licensing.

Typical data

  • Bookings & cancellations
  • Pricing & occupancy history
  • Guest reviews & responses
  • Complaints & service recovery
  • Menu items & sales
  • Supplier & inventory records

Representative workflow

Illustrative

  1. 01Booking
  2. 02Price & availability
  3. 03Stay or visit
  4. 04Feedback or complaint
  5. 05Staff response
  6. 06Service recovery
  7. 07Repeat visit or review

Who typically holds it

  • Hotels and hotel groups
  • Tour operators and travel agencies
  • Restaurants and food-service groups
  • Short-term rental managers

Potential AI applications

  • Demand and pricing models
  • Review and sentiment analysis
  • Guest-service assistants
  • Cancellation prediction

Which systems hold hospitality data?

The property management system (PMS) holds reservations, stays and folios. The channel manager and booking engine hold rates, availability and cancellations. Revenue tools hold pricing decisions. Reputation and guest-messaging tools hold reviews, complaints and replies.

The link from complaint to outcome is valuable. A complaint, the staff response and the guest's next visit or review form one example.

  • Bookings, cancellations and lead times
  • Rate and occupancy history by season
  • Reviews paired with staff responses
  • Complaints with the service recovery taken

What do AI teams build with hospitality data?

Teams train demand and pricing models on years of rates and occupancy. They predict cancellations from booking patterns. They train guest-service assistants on complaints and the replies that worked.

Restaurant groups add menu and sales data. That helps forecast demand and reduce waste.

Tour operators and travel agencies add a trip view. Itineraries, changes and disruption handling show how travel plans hold up in practice.

How is guest data protected in hospitality datasets?

Hotel systems hold sensitive guest data: names, contact details, passport numbers, payment cards and stay histories. All of this is removed before licensing. Card data falls under the PCI Data Security Standard and is never included.

Reviews and messages can name staff and guests or describe health needs. Only scoped, de-identified excerpts are considered, never bulk exports of guest messaging. Reviews posted on third-party sites may be governed by that site's terms, so their use is checked first.

How does a hospitality business prepare a first description?

A short description is enough to start. No guest data is shared.

  • Property types and number of sites, roughly
  • Years of booking and rate history
  • Which systems hold reviews and complaints
  • Whether service recovery steps are recorded

Example record (illustrative)

Illustrative record: a fictional, de-identified service recovery case. Not a real guest.
FieldWhat it holds
Stay typeTwo-night weekend stay, city hotel
ComplaintRoom not ready at check-in; two-hour wait
Staff responseApology, lounge access and a late checkout offered
Recovery costSmall food and drink credit, banded
OutcomePositive review mentioning the response; later repeat booking
RedactionsGuest and staff names, contact and payment data removed

Frequently asked questions

What hospitality data is most useful for AI?

Long histories of rates, demand and cancellations, plus complaints linked to the response and the outcome.

Are guest details shared?

No. Names, contact details, passport numbers and payment data are removed before anything is licensed.

Can we license reviews from public sites?

Only where the platform's terms allow it. Reviews collected through your own channels are usually simpler.

Can AI companies buy hotel datasets through DataNexx?

There is no ready-made catalogue. We arrange licences between hotels, travel firms or restaurant groups and AI companies, starting from a buyer's dataset request.

How soon will we hear back?

We reply within 24 hours. The first step is a short description of what you hold.

Is there a fee for the hotel or group?

No. There is no fee to the data owner. Compensation comes from the licence with the AI company.

Sources

Trust & compliance

Data licensing without losing control.

Your data stays yours until you decide otherwise. We assess before anything is shared, and we treat rights and privacy as conditions of a transaction — not afterthoughts.

No raw data for an initial evaluation

Nothing moves without authorization

Ownership is not the same as licensing rights

Specialist review where it is needed

Scoped records, never bulk exports

How we handle rights, privacy and confidentiality

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