Platform · trivago

Trivago Data Scraping

Q: Is a trivago rate a bookable rate?

No, and rate_is_referred is a constant true so nothing downstream assumes otherwise. The displayed rate is supplied by or taken from the seller and can differ on arrival, for reasons including timing, currency handling, tax basis and fee inclusion. It is evidence of what was advertised, which is useful. It is not evidence of what was bookable, which is what parity work usually needs.

Metasearch does not sell rooms. It points at sellers — and the price it points at is not always the price you land on.

Trivago data scraping collects hotel metasearch results: which sellers are surfaced for a property and stay date, at what displayed rate, in what position. The distinction that shapes everything: metasearch routes rather than sells, so the record is a referred offer, and the rate displayed does not always match what the destination site shows on arrival.

Treating metasearch as a price source is the common error. It is a distribution and visibility source that happens to display prices.

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trivago.jsonl LIVE FEED

{"property_id":"tv-44120","city":"Lisbon", "stay_date":"2026-10-09","market_observed_from":"GB", "device":"desktop", "seller_name":"seller-a","seller_type":"ota", "rate_displayed":98.00,"currency_displayed":"GBP", "rate_is_referred":true, "position":1,"placement_type":"paid"} {"property_id":"tv-44120", "seller_name":"hotel-direct","seller_type":"direct", "rate_displayed":94.00,"position":4, "note":"direct is cheaper and fourth — a commercial question with a budget attached"} {"seller_name":"seller-c","seller_absent":true, "absence_cause":"not_attributed", "caution":"absent may mean no bid, no availability, or not surfaced. we do not guess"}

3 of 2,204,880 referred-offer rows rate_is_referred constant true · NOT bookable rates · schema v1.0

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to trivago or its owners. trivago and related marks belong to their respective owners, used here only to name the publicly accessible source this service collects from.

Our Data Powers

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trivago at a glance

How we handle trivago specifically

Platform-specific handling, not a generic retail template pointed at a different domain.

Model: Metasearch. It routes, it does not sell
The record: A referred offer, not a bookable rate
The trap: Displayed rate may differ from the destination
What we flag: rate_is_referred, constant true
Sellers: Which OTAs and direct sites surface, and where
Position: Meaningless without the query — property, dates and market
Paid placement: Recorded where distinguishable, undetermined where not
Refresh: Daily per stay date; the seller mix changes more than the rate does

Platform specifics

Why metasearch is a visibility source, not a price source

These are the reasons a trivago dataset needs its own handling rather than a shared retail schema.

A referred rate is not a bookable rate

Metasearch aggregates offers from OTAs and hotel direct sites and displays them for comparison. The shopper clicks through to the seller to book.

The rate displayed on the metasearch page is supplied by, or scraped from, the seller — and it can differ from what the seller shows on arrival, for reasons including timing, currency handling, tax display basis and fee inclusion.

A metasearch rate is evidence of what was advertised, which is genuinely useful.
It is not evidence of what was bookable, which is what a parity analysis usually needs.

rate_is_referred is a constant true on every record. Nothing downstream should treat these as bookable rates without knowing that.

Which does not make it less useful

It makes it useful for a different question. Where a hotel wants to know which sellers are competing for its traffic, and at what advertised rates, metasearch is the clearest single view available — clearer than checking each OTA individually, because it shows them side by side as the shopper sees them.

The seller mix is the finding

On an OTA the interesting variable is the rate. On metasearch it is who appears.

Which OTAs surface for a given property and stay date, and which do not.
Whether the hotel's own direct site appears, and in what position relative to OTAs.
How the mix changes by lead time, market and season.

For a hotel or group, direct-site visibility on metasearch is a commercial question with a budget attached, and it is directly observable here. We record seller_name, seller_type (ota or direct) and position with the full query context.

Position needs its query

Position on metasearch depends on the property, the stay dates, the market observed from and the device. We record all four alongside the position, because position without them is not reproducible — the same discipline our Google Shopping page applies.

Paid placement, and what we will not infer

Metasearch monetises through seller bidding. Prominence is therefore partly bought.

Where paid placement is distinguishable from organic ordering, we record it.
Where it is not, placement_type is undetermined — a real value, used rather than guessed.

We do not infer a bid level, a spend figure or a share of voice from position. Those would be conclusions drawn from an observation that does not contain them.

And one thing we will not claim

That a seller's absence means it does not have the property. A seller may be absent because it did not bid, because it has no availability, or because the metasearch did not surface it in that context. We record the absence and do not attribute a cause.

Scope

What we collect on trivago, and what we do not

The right column matters more than the left. Anyone can list fields; the limits are what tell you whether the dataset will hold up.

✅ What we collect

Referred offers with seller name and type, per property and stay date
rate_is_referred constant true on every record
Position with the full query context — property, dates, market, device
placement_type as paid, organic or undetermined
Seller mix over time, by lead time and market
Direct-site presence and position relative to OTAs
Stay date and observation date, with derived lead time
Displayed currency and rate basis where shown
Seller absence recorded, without a cause attributed

❌ What we do not, and why

A referred rate presented as a bookable rate
A bid level, spend figure or share of voice inferred from position
A cause attributed to a seller's absence
placement_type guessed where it is not distinguishable
Occupancy, bookings or guest data

Core trivago fields

The full dictionary is agreed during scoping. These are the fields specific to this platform.

Field	What it is on this platform
property_id / property_name / city	The hotel being searched
stay_date / observed_at / lead_time_days	Both dates, and the derived axis
market_observed_from / device	Part of the query. Position is not reproducible without them
seller_name / seller_type	Which OTA or direct site, and which kind
rate_displayed / currency_displayed / rate_basis	As shown on the metasearch page
rate_is_referred	Constant true. Not a bookable rate
position	With the full query context recorded alongside
placement_type	paid, organic or undetermined — never guessed
seller_absent	Recorded, with no cause attributed
seller_count	How many sellers surfaced for this property and date
star_rating	As published

Use cases

What teams do with trivago data

Direct-site visibility on metasearch

Whether a hotel's own site appears and in what position relative to OTAs, which is a commercial question with a budget attached and is directly observable here.

Seller mix analysis

Which OTAs compete for a property's traffic and how that changes by lead time, market and season — clearer than checking each OTA separately because it shows them as the shopper sees them.

Advertised-rate comparison

What sellers advertise side by side, which is genuine evidence of advertised pricing even though it is not evidence of bookable pricing.

Paid versus organic prominence

placement_type recorded where distinguishable, so bought prominence is not mistaken for competitive advantage.

The 24-hour sample — run on your sources, not ours

Send us a trivago item or category list. We run real collection against it and return the output within 24 hours, with the platform-specific fields populated so you can check them yourself rather than take our word for it.

Real extraction from your actual sources
Returned within 24 hours
Coverage and QA note included
You keep the data either way
No card, no trial clock
Named engineer on the call

Get my free sample Book a 20-min scoping call Reply within one business day. Reference calls available under NDA.

How we engage

Three ways to engage us

Same collection pipeline and QA underneath. The difference is who holds the schedule and how the data reaches you.

Managed service (most common)

We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.

Dedicated engineer assigned to your account
Site changes fixed by us, not reported to you
Scheduled delivery to your warehouse or S3
Named contact on Slack or email

Best fit: Teams who need the data, not the infrastructure.

API access

The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.

On-demand and scheduled endpoints
Rate limits agreed to your load profile
Sandbox keys for integration testing
Versioned schema with deprecation notice

Best fit: Product and engineering teams building on live data.

One-time or project extraction

A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.

Fixed scope agreed in writing upfront
Single delivery with full QA report
Methodology documented for your records
Converts to managed if you want continuity

Best fit: Research, strategy and diligence work with a deadline.

Pricing

Every engagement is quoted individually, because the honest answer depends on your scope: how many sources, how many records, how often, and how the data reaches you. We scope it with you, run a free pilot on your own sources, and then quote a fixed monthly figure — no per-request metering and no overage billing when volumes move. Request a quote and you will have a number after one call.

trivago is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. trivago data becomes useful when it sits next to the competitor set on one schema, refreshed on one schedule, so a price index or availability comparison is genuinely like-for-like.

That is what travel & hospitality data covers, and a trivago-only engagement can be expanded into it without rebuilding. If you already know you need several platforms, start there instead — it is the same pipeline and usually the better scoping conversation.

FAQ

trivago data scraping: frequently asked questions

Platform-specific questions, including what cannot be collected here.

No, and rate_is_referred is a constant true so nothing downstream assumes otherwise. The displayed rate is supplied by or taken from the seller and can differ on arrival, for reasons including timing, currency handling, tax basis and fee inclusion.

It is evidence of what was advertised, which is useful. It is not evidence of what was bookable, which is what parity work usually needs.

Visibility rather than price. Which sellers compete for a property's traffic, whether the hotel's own site appears and where, and how that mix changes by lead time and market.

For a hotel, direct-site visibility on metasearch is a commercial question with a budget attached, and this is the clearest single view of it.

No. We record position and, where distinguishable, whether placement was paid. We do not infer a bid level, spend figure or share of voice from position — those are conclusions the observation does not contain.

Not necessarily, and we do not attribute a cause. A seller may be absent because it did not bid, because it has no availability, or because the metasearch did not surface it in that context.

We record the absence as an observation and leave the interpretation to you.

Because position depends on the property, the stay dates, the market observed from and the device. Without all four it is not reproducible.

Same discipline as on Google Shopping, where a result exists because a search happened.

We quote individually on properties times stay dates times markets times observations. Market count matters because results differ by where the search came from.

One scoping call, a free pilot within 24 hours, then a fixed monthly quote. Request a quote.

See real trivago data before you commit to anything

Send us an item or category list. We return the output within 24 hours with the platform-specific fields populated.

Get a free sample Talk to an engineer

Free pilot, no card, no obligation. If we cannot collect a field you need on this platform, the sample shows you that too.

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