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Platform · Cruise lines

Cruise Pricing Data Scraping

Not a product with a price. A sailing, a cabin category and an occupancy assumption — and the last one is where most comparisons break.

Cruise pricing data scraping collects fares by sailing date and cabin category across cruise lines. The record is sailing plus cabin category plus occupancy, not a product: the same ship in the same week carries a dozen different prices, they are quoted per person on a double occupancy assumption, and a solo traveller pays a supplement that is a separate field.

Travel data covers flights, hotels and car rental. Cruise is structurally none of those, which is why a hotel rate schema applied to it produces numbers that look fine and compare nothing.

Free pilot on your own Cruise lines list, returned in 24 hours. No card, no trial clock — and you keep the sample data either way.

cruise_fares.jsonl LIVE FEED
{"line":"line-a","ship":"Example Voyager", "sailing_id":"EV-2027-03-14-07N", "sail_date":"2027-03-14","nights":7, "cabin_category_code":"8B", "cabin_category_name":"Balcony — Deck 8 aft", "cabin_type":"balcony","categories_in_type":12, "price_per_person":1149.00,"currency":"USD", "occupancy_basis":"double", "solo_supplement":862.00, "taxes_included":false,"gratuities_included":false, "availability_state":"specific_available", "days_to_sail":201} {"cabin_category_code":"8A","cabin_type":"balcony", "price_per_person":1449.00, "note":"same type, different category — 300 apart. A four-type average hides this"} {"cabin_category_code":"6C", "availability_state":"guarantee_only", "caution":"category bookable, no specific cabin assigned — a distinct state"}
3 of 2,204,880 sailing-category rows per person, occupancy basis stated · ship occupancy NOT produced · schema v1.0

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

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Cruise lines at a glance

How we handle Cruise lines specifically

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

Record
Sailing + cabin category + occupancy
Price basis
Per person, on a stated occupancy assumption
Solo
A supplement, recorded separately — not a multiplier
Cabin category
The line's own codes. Twelve balcony grades is normal
Inclusions
Port taxes and gratuities sometimes included, sometimes not. Flagged
Guarantee cabins
A distinct availability state, not a specific room
Itinerary
Ports and sea days recorded, since they drive comparability
Refresh
Daily standard; sub-daily during promotional windows
Platform specifics

Why a hotel schema does not fit a cruise

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

Cabin category is not cabin type, and rolling it up loses the analysis

Buyers usually ask for four types: interior, ocean view, balcony, suite. Cruise lines do not sell four types. They sell categories — often a dozen balcony grades alone, differing by deck, position, obstruction and forward or aft placement, at materially different prices.

  • Roll up to four types and you get an average that no cabin actually costs.
  • Keep the line's own codes and you can compare like with like, but the codes differ per line.

We deliver the line's own cabin_category_code and cabin_category_name, plus a normalised cabin_type for rollup, and we record categories_in_type so the spread inside a type is visible. Both views are delivered; neither substitutes for the other.

This is the same discipline as delivering both boutique-level and platform-level size availability on luxury multi-brand platforms — the aggregate is what a shopper experiences, the detail is what an operator needs.

Per person, double occupancy, and the solo supplement

A cruise fare is quoted per person and assumes two people share the cabin. That assumption is invisible in the number and it changes what the number means.

  • A solo traveller pays a supplement, frequently a large one, which is a separate commercial decision rather than a fixed multiplier.
  • Third and fourth berth pricing is often much lower and quoted differently again.
  • Comparing a per-person fare to a hotel room rate is a category error that produces a cruise looking half its actual cost.

Every record carries occupancy_basis, price_per_person, and where quoted, solo_supplement and third_berth_price as their own fields. We compute a cabin total only on a stated basis, with the basis recorded, and never fold the supplement into the headline fare.

Guarantee cabins are an availability state, not a price

Lines sell "guarantee" bookings — you buy a category and the specific cabin is assigned later, sometimes days before sailing. It is a real product with its own price, and it is not the same as a specific cabin being available.

  • Guarantee available, specific cabins sold out is a common and meaningful state.
  • Guarantee pricing can be below the same category's assigned-cabin price.
  • Treating it as normal availability overstates how much choice a booker actually has.

We record availability_state as one of specific_available, guarantee_only, waitlist or sold_out. Collapsing these to available or not loses the distinction that matters most as a sailing fills.

What we do not report

How full a ship is. Occupancy is not published, and inferring it from category sell-out is the same error as inferring hotel occupancy from rate availability. We report availability states and let the inference stay yours.

Scope

What we collect on Cruise lines, 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

  • Fare by sailing date and cabin category, per person
  • The line's own category code and name, plus a normalised type for rollup
  • Occupancy basis stated on every record
  • Solo supplement and third-berth pricing as separate fields where quoted
  • Port taxes and gratuities, with an included-or-not flag
  • Availability state — specific, guarantee only, waitlist or sold out
  • Itinerary ports, sea days and embarkation port
  • Ship, line and sailing duration
  • Promotional inclusions as published, such as drinks or wifi packages

❌ What we do not, and why

  • Ship occupancy or how full a sailing is, which is not published
  • Passenger data, bookings or manifests
  • A single blended price across cabin categories
  • Fares behind a travel-agent login or a loyalty tier
  • Solo pricing inferred by multiplying a per-person fare

Core Cruise lines fields

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

Field What it is on this platform
line / ship / sailing_id Cruise line, vessel and the sailing
sail_date / nights / embark_port When it departs, for how long, from where
cabin_category_code / cabin_category_name The line's own codes
cabin_type Normalised interior, ocean view, balcony, suite — for rollup only
categories_in_type How many categories the rollup is averaging
price_per_person / currency The fare as quoted
occupancy_basis Usually double. Stated, never assumed
solo_supplement / third_berth_price Separate fields where quoted
taxes_included / gratuities_included Flags, because inclusion varies by line and market
availability_state specific_available, guarantee_only, waitlist or sold_out
itinerary_ports / sea_days Comparability depends on these
observed_at / days_to_sail Observation time and lead time
Use cases

What teams do with Cruise lines data

Category-level competitive fare benchmarking

Fares by the line's own cabin categories across competing sailings on comparable itineraries, rather than a four-type average that no cabin actually costs.

Lead-time and pricing curve analysis

Days-to-sail on every observation, so the shape of the pricing curve as a sailing approaches is measurable rather than assumed.

Guarantee inventory as a fill signal

Availability states tracked as a sailing approaches, showing where specific cabins have gone and only guarantee remains — labelled as an availability state, not as occupancy.

True cost comparison including inclusions

Taxes, gratuities and promotional inclusions captured with flags, so a headline fare that excludes them is comparable to one that does not.

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

Send us a Cruise lines 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
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  • 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.
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The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.

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

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Cruise lines is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Cruise lines 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 Cruise lines-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

Cruise lines data scraping: frequently asked questions

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

We deliver that rollup, and we deliver the line's own categories underneath it, because a dozen balcony grades at different prices average to a number no cabin actually costs.

categories_in_type tells you how many categories the rollup is averaging, so you can see when the rollup is safe and when it is hiding a wide spread.

Because a cruise fare is per person and assumes two people share the cabin, and that assumption is invisible in the number.

A solo traveller pays a supplement that is a commercial decision rather than a fixed multiplier, so it cannot be derived. We capture it as its own field where it is quoted, and never fold it into the headline fare.

You buy a category and the specific cabin is assigned later. It is a real product with its own price, often below the assigned-cabin price for the same category.

Guarantee available while specific cabins are sold out is a common state and a meaningful one. Collapsing it into 'available' overstates the choice a booker actually has.

No. Ship occupancy is not published. Inferring it from category sell-out is the same error as inferring hotel occupancy from rate availability — the relationship exists but it is not stable enough to sell as a number.

We report availability states with timestamps and days-to-sail. The inference stays yours, where you can put a confidence on it.

They do, and it is the most common reason two fares that look comparable are not. Inclusion varies by line and by source market.

We flag taxes_included and gratuities_included on every record rather than adjusting the fare, because adjusting would require assuming an amount we may not have observed.

We quote individually. The unusual driver is the grid: sailings times cabin categories times observation dates. A single ship season carries hundreds of category-sailing combinations before you add competitors.

We usually scope a defined set of ships and itineraries rather than a line's full deployment. One scoping call, a free pilot within 24 hours, then a fixed monthly quote. Request a quote.

See real Cruise lines 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.

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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