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CSV import via SFTP: What are the file specifications for PMS exports?

Felix Dreizehnter
Updated

File specification and export guidelines for hotel IT teams and PMS vendors
Version 2.0 · October 2026

About this document

If your hotel's PMS is not connected to the TrustYou Customer Data Platform (CDP) through a direct API, your guest, reservation and revenue data can be delivered as CSV files via SFTP. This document describes exactly how these files must be structured so that they are imported completely and without errors.

Who should read it: your IT team and your PMS vendor, who will build and schedule the export.

How to use it:

  • Share this document with your PMS vendor and agree on which PMS fields fill each column (section 3 onwards).
  • Pay special attention to the mandatory fields (highlighted in blue in every table) and the accepted values (section 2.2). These are the most common causes of failed imports.
  • Produce a sample export and provide these with your TrustYou onboarding contact.

Why precision matters

The importer reads values exactly as delivered. A single unknown value in a field with a fixed list (for example a reservation status of Confirmed instead of Booked) or a date in the wrong format stops the import of that file. In reservations.csv this stops the entire daily import, so no guests, reservations or revenues are updated until the file is corrected.

1. File overview

Seven CSV files are delivered. They are loaded together and then ingested in the order below, because later files refer to records in earlier ones.

# File One row = Unique key Refers to
1 locations.csv one hotel source_id -
2 rooms.csv one physical room / unit source_id location
3 guests.csv one guest or company profile source_id location (optional)
4 reservations.csv one room booked for one stay source_id location, room
5 guest_reservation_mappings.csv one guest's role on one reservation source_reservation_id + source_guest_id reservation, guest
6 guest_reservation_infos.csv one guest's stay dates on one reservation source_reservation_id + source_guest_id reservation, guest
7 revenues.csv one posted revenue line (e.g. one room night, one breakfast) source_id reservation, debtor (guest)

1.1 Mandatory fields per file

These fields must be present and filled with a valid value in every row. Rows that miss them are skipped or stop the import.

File Mandatory fields
locations.csv source_id, secondary_source_id, name, room_count (integer)
rooms.csv source_id, source_location_id
guests.csv source_id, type, full_name
reservations.csv source_id, source_location_id, arrival, departure
guest_reservation_mappings.csv source_reservation_id, source_guest_id, is_main_guest, type
guest_reservation_infos.csv source_reservation_id, source_guest_id, arrival, departure
revenues.csv source_id, source_created_at, type, count (integer), amount, source_reservation_id, source_debtor_id
The columns name, source_category_id and source_category must exist in the header (values may be empty).

2. General file requirements

These rules apply to all files. The right-hand column shows what happens if a rule is not followed.

Topic Rule If not followed
File names Exactly as listed in section 1, lower case (e.g. reservations.csv). File is not picked up.
Encoding UTF-8 (a BOM is tolerated). Check that names such as Müller, Dvořák or Øresund display correctly. Non-UTF-8 files (e.g. Windows-1252 or UTF-16 saved from Excel) are dropped entirely, with 0 rows imported.
Delimiter Semicolon ; between fields. With a comma, every row fails and 0 rows are imported.
Header row First line contains the column names exactly as in this document: lower case, with underscores, case-sensitive (including the spelling is_accomodation). Column order does not matter. Additional columns are ignored; optional columns may be omitted. A misspelled column is treated as missing.
Row length Every row has the same number of fields as the header. A row with more fields than the header is skipped.
Quoting If a value contains ;, a line break or ", wrap the value in double quotes and double any quote inside it, e.g. "Smith; Jones". Fields shift and the row is skipped or misread.
Date / time format ISO 8601: YYYY-MM-DD HH:mm:ss.fff (milliseconds optional), or YYYY-MM-DD for dates. No time zone offset. Non-ISO formats such as 03.11.2026 or 11/03/2026 stop the file import (for reservations: the whole daily import).
Time zone All timestamps in UTC. Exception: arrival and departure dates are sent as the stay date at 00:00:00.000. Check-in/out times are shifted by hours.
Booleans Use TRUE / FALSE. TRUE, 1 and YES (any case) are read as true. Any other value is read as false, so Y or X silently becomes false.
Numbers Dot as decimal separator, no thousands separator, no currency symbol, no % sign: 1234.50, 19. Revenue amounts that cannot be read stop the revenue import. Reservation amounts that cannot be read are stored as empty.
IDs Plain text, kept exactly as in the PMS (leading zeros, case). IDs must be unique across all hotels you deliver, not just within one hotel. IDs that repeat across hotels overwrite each other. Excel can turn 00123 into 123 or 1.23E+10.
Duplicates Each key appears only once per file. If a key appears twice, the last row in the file wins.
Empty values Leave optional fields empty. Do not use placeholders such as N/A, 0000, 1900-01-01 or noemail@hotel.com. Placeholders are imported as real data and can merge unrelated guests.

2.1 References between files

Every reference must point to a record that exists in the referenced file. Rows with a broken reference are skipped individually; the rest of the file continues.

File Column Must exist as If not found
rooms source_location_id locations.source_id Room is skipped
guests source_location_id (optional) locations.source_id -
reservations source_location_id locations.source_id or locations.secondary_source_id Reservation is skipped
reservations source_room_id (optional) rooms.source_id Reservation is imported without a room
guest_reservation_mappings source_reservation_id
source_guest_id 		reservations.source_id
guests.source_id 		Mapping is skipped
guest_reservation_infos source_reservation_id
source_guest_id 		reservations.source_id
guests.source_id 		Row is skipped
revenues source_reservation_id
source_debtor_id 		reservations.source_id
guests.source_id 		Revenue is skipped

2.2 Accepted values at a glance

The following fields only accept the values listed. Values are case-sensitive and must be spelled exactly as shown. Map your PMS codes to these values in the export.

File and column Accepted values Notes
reservations.status Booked, CheckedIn, CheckedOut, NoShow, Cancelled (or empty) Any other value (e.g. Confirmed, In-House, Canceled) stops the whole daily import.
revenues.type Room, Item Room = room/accommodation postings, Item = everything else.
guests.type Person, Company Mandatory.
guests.gender Male, Female, Diverse, Unknown (empty = Unknown) Any other value (e.g. M, F, Other) stops the guest import.
guest_reservation_mappings.type MainGuest, Booker, AdditionalGuest, Debtor Any other value (e.g. CompanyContact, Guest) stops the mapping import.
All Boolean columns TRUE, FALSE See Booleans in section 2.

2.3 File delivery via SFTP

  • TrustYou provides the SFTP credentials and the target folder. Files are placed in that folder with the fixed names from section 1.
  • Files are picked up once per day. They are not moved or deleted after pickup, so each new export simply overwrites the previous file.
  • Full and incremental exports both work. Records are created or updated by their key. Records that are missing from a later file stay unchanged in the CDP; nothing is deleted.
  • Because nothing is deleted, a cancellation or change must be sent as an updated row with the same source_id (e.g. status Cancelled), never by removing the row.
  • Recommended initial load: all reservations of the agreed history period (e.g. the last 2 years) plus all future reservations. Afterwards, a daily export of everything created or modified since the last export (based on source_modified_at).

3. locations.csv

Hotel / property master data. Usually one row per hotel; a group with 10 hotels sends 10 rows. All other files refer to these records.

Key: source_id · Ingested: 1st

Column Type Required Description and accepted values
source_id Text Yes Unique hotel / property code as used in the PMS (e.g. AMSTY, 1042).
secondary_source_id Text Yes Alternative code for the hotel (e.g. CRS or channel manager code). If there is none, repeat source_id.
source_created_at DateTime No When the record was created in the PMS (UTC).
source_modified_at DateTime No When the record was last modified in the PMS (UTC).
name Text Yes Public hotel name, e.g. Hotel Amsterdam Centre.
room_count Integer Yes Total number of rooms, whole number greater than 0.

Guidelines

  • Write source_id identically wherever it is used (source_location_id in all other files): same case, no leading or trailing spaces.
  • Other files may refer to a hotel by either source_id or secondary_source_id. Choose one and use it consistently; source_id is recommended.
  • Use the proper public hotel name, not an internal abbreviation. It is visible to users in the CDP.
  • room_count should match the number of rows in rooms.csv with is_accomodation = TRUE. A large difference usually means rooms are missing from the export.

4. rooms.csv

Room / unit inventory of each hotel.

Key: source_id · Refers to: locations · Ingested: 2nd

Column Type Required Description and accepted values
source_id Text Yes Unique room ID. Must be unique across all hotels, e.g. AMSTY-207.
source_created_at DateTime No Created in the PMS (UTC).
source_modified_at DateTime No Last modified in the PMS (UTC).
name Text No Room number or name as shown to staff, e.g. 207, 207c.
category Text No Room type code from the PMS, e.g. DBL, STD, B4T.
is_accommodation Boolean No TRUE = bookable guest room. FALSE = non-guest unit (meeting room, parking space, dummy / virtual room).
bed_count Integer No Number of beds, e.g. 2.
source_location_id Text Yes The hotel this room belongs to. Must exist in locations.csv.

Guidelines

  • Room IDs must be unique across all hotels. Rooms are identified by source_id only, so room 101 in two hotels would overwrite each other. If your PMS reuses room numbers, prefix them with the hotel code: AMSTY-101, BERHB-101.
  • Set is_accommodation = FALSE for placeholder units that many PMSs exports, such as "no room assigned", "PM" (pseudo room), meeting rooms or parking.
  • Use the same codes in category as in reservations.csv → room_category.
  • Leave bed_count empty if unknown instead of writing 0.

5. guests.csv

Guest and company profiles from the PMS.

Key: source_id · Refers to: locations (optional) · Ingested: 3rd

Column Type Required Description and accepted values
source_id Text Yes Unique, permanent profile ID from the PMS. Must be unique across all hotels.
source_created_at DateTime No Created in the PMS (UTC).
source_modified_at DateTime No Last modified in the PMS (UTC).
type Text Yes Accepted values: Person, Company.
first_name Text No First name.
last_name Text No Last name.
full_name Text Yes Persons: last_name, first_name (e.g. Rossi, Giulia). Companies: the company name.
country Text No Country of residence. Recommended: ISO 3166-1 alpha-2 code, e.g. DE, GB, IT.
birthday Date No Date of birth, YYYY-MM-DD, e.g. 1987-04-23.
email Text No One email address, lower case, no spaces.
phone Text No Recommended: international format with + and country code, no spaces, e.g. +393471234567.
gender Text No Accepted values: Male, Female, Diverse, Unknown. Empty is read as Unknown.
source_location_id Text No Hotel where the profile was created (only if the PMS keeps one profile database per hotel).
address Text No Street and house number.
city Text No City.
nationality Text No Recommendation: ISO 3166-1 alpha-2 code, e.g. IT.
state Text No State / province, only where relevant (US, CA, AU, ...), e.g. CA.
zip Text No Postal code as text, so leading zeros are kept, e.g. 01067.
language Text No Preferred language. Recommended: ISO 639-1 code, e.g. en, de, it.

Guidelines

Why this file matters most

The CDP merges profiles from all sources into one de-duplicated profile per real person, based on name, email, phone and birthday. Clean, consistent values in these columns directly improve how well duplicates are recognized and how well guests can be reached.

  • Map gender codes before export. PMS values such as M, F, W, X, Mr, Other are not accepted and stop the guest import. Map them to Male, Female, Diverse or Unknown, or leave the field empty.
  • type is mandatory and must be exactly Person or Company.
  • full_name is mandatory even when first_name and last_name are filled. Build it from the two fields if the PMS does not provide it.
  • Keep source_id stable: the same person must keep the same ID in every export, so the existing profile is updated instead of a new one being created.
  • Export companies, travel agents and groups as profiles with type = Company. They are needed as debtors in revenues.csv and as Booker / Debtor in the mappings. Leave person-only fields (birthday, gender) empty for them.
  • Do not send placeholder values such as noemail@hotel.com, test@test.com, 0000000000, 1900-01-01 or N/A. Leave the field empty. Placeholders shared by many guests can cause unrelated people to be merged into one profile.
  • OTA relay emails (e.g. @guest.booking.com) are real but temporary addresses. Send them as they are.
  • The ISO formats for country, nationality, phone and language are not enforced by the importer, but strongly recommended for matching quality and segmentation.
  • An invalid birthday format (e.g. 23.04.1987) stops the guest import. Use YYYY-MM-DD or leave it empty.
  • Only export personal data you are permitted to transfer, and exclude guests who must not be processed according to your data protection rules.

6. reservations.csv

Room reservations. One row = one room for one stay.

Key: source_id · Refers to: locations, rooms · Ingested: 4th

Column Type Required Description and accepted values
source_id Text Yes Unique, permanent reservation ID. Must be unique across all hotels.
source_created_at DateTime No Booking date (UTC).
source_modified_at DateTime No Last modification (UTC).
source_room_id Text No Assigned room. Must exist in rooms.csv if filled.
room_name Text No Room number / name, e.g. 207.
room_category Text No Booked room type code, same codes as rooms.category.
booking_source_id Text No Not evaluated. May be left empty.
booking_source Text No Booking channel. See guidelines for recognised names.
is_direct_booking Boolean No TRUE for direct bookings, otherwise FALSE.
rate_id Text No ID of the rate plan.
rate Text No Rate plan code / name, e.g. BAR, NONREF, CORP-ACME.
arrival DateTime Yes Planned arrival date at 00:00:00.000, e.g. 2026-11-03 00:00:00.000.
departure DateTime Yes Planned departure date at 00:00:00.000. Must be after arrival.
checked_in_at DateTime No Actual check-in time (UTC).
checked_out_at DateTime No Actual check-out time (UTC).
duration Integer No Not evaluated. The CDP calculates nights from departure minus arrival.
has_checked_in Boolean No TRUE once the guest has checked in.
has_cancelled Boolean No TRUE if the reservation is cancelled.
has_paid Boolean No TRUE if the reservation is paid.
source_location_id Text Yes Hotel of the reservation. Must exist in locations.csv.
status Text No Accepted values: Booked, CheckedIn, CheckedOut, NoShow, Cancelled, or empty.
cancelled_at DateTime No Cancellation time (UTC).
adults_count Integer No Number of adults.
children_count Integer No Number of children (0 if none).
pax_count Integer No Total guests = adults_count + children_count.
market_segment Text No Your market segment code, e.g. LEISURE, CORP, GROUP.
cancellation_policy Text No Cancellation policy code / description.
forecast_revenue Decimal No Gross expected revenue of the stay in the reservation currency, e.g. 398.00.
forecast_revenue_net Decimal No Net expected revenue (excl. VAT) in the reservation currency.
forecast_revenue_eur
forecast_revenue_usd
forecast_revenue_net_eur
forecast_revenue_net_usd 		Decimal No Not evaluated. The CDP converts from currency and forecast_revenue automatically. Leave empty.
currency Text No ISO 4217 code, e.g. EUR, GBP, CHF, USD.
gds_number Text No GDS booking reference, if any.

Guidelines

Reservation status: accepted values only

An unknown status value stops the entire daily import: reservations, guest mappings, stay infos and revenues are then not updated. Map every PMS status to one of the five values below before export.

Value Typical PMS statuses has_checked_in has_cancelled checked_in_at checked_out_at cancelled_at
Booked Confirmed, Reserved, Guaranteed, Definite FALSE FALSE empty empty empty
CheckedIn In-House, Arrived, Checked in TRUE FALSE filled empty empty
CheckedOut Departed, Checked out TRUE FALSE filled filled empty
NoShow No-Show FALSE FALSE empty empty empty
Cancelled Cancelled, Canceled, Void FALSE TRUE empty empty filled
  • Status and flag columns describe the same thing and must not contradict each other (see table above). Note the spelling Cancelled with double "l".
  • One row = one room for one stay. If a guest books 3 rooms in one booking, send 3 rows, each with its own source_id.
  • Reservations contain no guest ID. Guests are linked to reservations only through guest_reservation_mappings.csv. A reservation without a mapping has no guest in the CDP.
  • Send arrival and departure as dates at 00:00:00.000. The number of nights is calculated as departure minus arrival in whole days. If times are attached (arrival 15:00, departure 11:00), a 2-night stay is counted as 1 night. Actual times belong in checked_in_at / checked_out_at.
  • Keep source_id stable for the whole life of the reservation. Modifications and cancellations are sent again with the same ID and an updated source_modified_at, not as a new row.
  • If no room is assigned yet (typical for future bookings), leave source_room_id empty but fill room_category. An unknown source_room_id does not reject the reservation; it is imported without a room.
  • A reservation with an empty or unknown source_location_id is skipped.
  • booking_source is free text. These names are recognised automatically (not case-sensitive, also as part of a longer name):
    • booking.com → Booking.com · expedia → Expedia · agoda → Agoda · airbnb → Airbnb · hotelbeds → Hotelbeds
    • direct, direkt, web, online, pms → Direct
    • portal → Portal
    • Any other value is kept as delivered and reported under "Other".
  • Use the OTA brand name for OTA bookings (e.g. Expedia), not generic terms. A value like "Online Travel Agency" contains "online" and would be classified as Direct. Use the same spelling every time.
  • Revenue amounts: dot as decimal separator, no symbols (1234.50, not 1.234,50 or €1,234.50). An unreadable forecast_revenue amount is stored as empty.
  • adults_count + children_count = pax_count. Use whole numbers and 0 rather than empty when there are no children.

7. guest_reservation_mappings.csv

Defines who is on a reservation and in which role. This is the only link between guests and reservations, so it must never be skipped.

Key: source_reservation_id + source_guest_id · Refers to: reservations, guests · Ingested: 5th

Column Type Required Description and accepted values
source_reservation_id Text Yes Reservation ID. Must exist in reservations.csv.
source_guest_id Text Yes Guest profile ID. Must exist in guests.csv.
is_main_guest Boolean Yes TRUE for the main guest of the reservation, otherwise FALSE.
type Text Yes Accepted values: MainGuest, Booker, AdditionalGuest, Debtor.

Guidelines

  • One row per guest per reservation. The combination reservation + guest is the key. If the same pair appears twice, the later row overwrites the earlier one.
  • If one person has several roles (e.g. books and stays), send one row with the most important role, in this order: MainGuest > Booker > Debtor > AdditionalGuest.
  • Exactly one main guest per reservation: one row with type = MainGuest and is_main_guest = TRUE. This is the person staying in the room, who normally receives guest communication such as surveys.
  • Accompanying guests: AdditionalGuest, FALSE. Someone who booked but does not stay: Booker, FALSE. A company or agency paying the bill: Debtor, FALSE (the company must exist as a profile in guests.csv).
  • Any other type value (e.g. CompanyContact, Guest, Companion) stops the mapping import.
  • Roles of an already imported guest/reservation pair are currently not updated by later files. Make sure type and is_main_guest are correct in the first delivery.
  • Every non-cancelled reservation should have at least one mapping row.

Examples

Situation Rows for reservation R1 (guest → type, is_main_guest)
Guest books and stays alone G1 → MainGuest, TRUE
Assistant books for her manager G2 (manager) → MainGuest, TRUE
G3 (assistant) → Booker, FALSE
Two colleagues share a room, company pays G4 → MainGuest, TRUE
G5 → AdditionalGuest, FALSE
C1 (company profile) → Debtor, FALSE

8. guest_reservation_infos.csv

Stay dates of each individual guest on a reservation. Usually identical to the reservation dates; they differ when guests share a room but not the whole stay (e.g. a colleague joins on night 2).

Key: source_reservation_id + source_guest_id · Refers to: reservations, guests · Ingested: 6th

Column Type Required Description and accepted values
source_reservation_id Text Yes Reservation ID. Must exist in reservations.csv.
source_guest_id Text Yes Guest profile ID. Must exist in guests.csv.
arrival DateTime Yes Guest's arrival date at 00:00:00.000.
departure DateTime Yes Guest's departure date at 00:00:00.000.
checked_in_at DateTime No Actual check-in time (UTC).
checked_out_at DateTime No Actual check-out time (UTC).
pre_checked_in_at DateTime No Time of online pre-check-in, if offered (UTC).
pre_checked_out_at DateTime No Time of express / pre-check-out, if offered (UTC).
cancelled_at DateTime No Cancellation time, if cancelled (UTC).

Guidelines

  • One row per guest per reservation; the pair source_reservation_id + source_guest_id must be unique.
  • Both IDs must exist in their files, otherwise the row is skipped.
  • The guest's arrival / departure should fall within the reservation's arrival / departure.
  • If the PMS does not track per-guest dates, create one row per mapped guest and copy the dates from the reservation.
  • Leave pre_checked_in_at / pre_checked_out_at empty if the hotel does not offer online check-in or express check-out.

9. revenues.csv

Posted revenue lines linked to a reservation.

Key: source_id · Refers to: reservations, guests (debtor) · Ingested: 7th (last)

Column Type Required Description and accepted values
source_id Text Yes Unique posting ID. Must be unique across all hotels.
source_created_at DateTime Yes Posting date / time (UTC).
source_modified_at DateTime No Last modification (UTC).
name Text Column required Description of the posting, e.g. Room Night, Breakfast. Column must exist; value may be empty.
type Text Yes Accepted values: Room, Item.
source_category_id Text Column required ID of the PMS revenue group. Column must exist; value may be empty.
source_category Text Column required Name of the PMS revenue group, e.g. Accommodation, Food, Spa. Column must exist; value may be empty.
count Integer Yes Quantity, whole number, e.g. 1, 2.
currency Text No ISO 4217 code, e.g. EUR. If empty, the hotel's default currency is used.
amount Decimal Yes Gross amount (incl. VAT) in the posting currency, e.g. 179.00. Negative for corrections / refunds.
amount_net Decimal No Net amount (excl. VAT) in the posting currency.
amount_eur
amount_usd
amount_net_eur
amount_net_usd 		Decimal No Not evaluated. The CDP converts automatically. Leave empty.
vat_percent Decimal No VAT rate as a number, e.g. 7, 19, 8.1.
source_location_id Text No Not evaluated. The hotel is taken from the linked reservation.
source_reservation_id Text Yes Reservation the posting belongs to. Must exist in reservations.csv.
source_debtor_id Text Yes Who pays: the guest, or a company / agency profile. Must exist in guests.csv.

Guidelines

  • type: use Room for room rate / accommodation postings and Item for everything else (food, beverage, spa, parking, extras). Any other value stops the revenue import. Put the detailed PMS revenue group into source_category.
  • amount and count must be valid numbers. An unreadable value (e.g. 1.234,50 or 1,5 or 2.5 in count) stops the revenue import.
  • One row per posting. A 3-night stay with breakfast typically produces 3 room rows plus 3 breakfast rows (or 1 breakfast row with count = 3). Corrections and refunds are separate rows with negative amounts; do not edit the original row.
  • amount includes VAT, amount_net excludes it: amount ≈ amount_net × (1 + vat_percent / 100).
  • vat_percent is the rate as a number: 19, not 0.19 and not 19%.
  • Every revenue must belong to a reservation. Revenue without a stay (e.g. restaurant walk-ins, spa day guests) cannot be imported and should not be exported.
  • source_debtor_id must exist in guests.csv, otherwise the posting is skipped. Export the company / agency profiles that appear as debtors.
  • Plausibility check: after check-out, the sum of amount for a reservation should be close to its forecast_revenue.

10. Worked example: one stay across all files

Giulia Rossi books a double room at the Amsterdam hotel for 2 nights through Booking.com. Her partner Marco stays with her. She has breakfast both mornings. Timestamps are in UTC; only the most relevant columns are shown for reservations and revenues (the real files contain all columns, with unused ones left empty).

locations.csv

Column Row 1
source_id AMSTY
secondary_source_id AMSTY
source_created_at 2024-01-10 08:00:00.000
source_modified_at 2026-05-02 12:12:00.000
name Hotel Amsterdam Centre
room_count 120

rooms.csv

Column Row 1
source_id AMSTY-207
source_created_at 2024-01-10 08:00:00.000
source_modified_at 2024-01-10 08:00:00.000
name 207
category DBL
is_accomodation TRUE
bed_count 2
source_location_id AMSTY

guests.csv

Column Row 1 Row 2
source_id G-1001 G-1002
source_created_at 2026-09-01 16:22:10.000 2026-11-03 14:40:00.000
source_modified_at 2026-11-05 10:03:00.000 2026-11-03 14:40:00.000
type Person Person
first_name Giulia Marco
last_name Rossi Bianchi
full_name Rossi, Giulia Bianchi, Marco
country IT IT
birthday 1987-04-23
email giulia.rossi@example.com
phone +393471234567
gender Female Male
source_location_id AMSTY AMSTY
address Via Roma 12
city Milan
nationality IT IT
state
zip 20121
language it it

reservations.csv (selected columns)

Column Row 1
source_id R-5001
source_room_id AMSTY-207
room_category DBL
booking_source Booking.com
is_direct_booking FALSE
rate BAR
arrival 2026-11-03 00:00:00.000
departure 2026-11-05 00:00:00.000
checked_in_at 2026-11-03 14:41:00.000
checked_out_at 2026-11-05 09:55:00.000
has_checked_in TRUE
has_cancelled FALSE
status CheckedOut
source_location_id AMSTY
adults_count 2
children_count 0
pax_count 2
forecast_revenue 398.00
currency EUR

guest_reservation_mappings.csv

Column Row 1 Row 2
source_reservation_id R-5001 R-5001
source_guest_id G-1001 G-1002
is_main_guest TRUE FALSE
type MainGuest AdditionalGuest

guest_reservation_infos.csv

Column Row 1 Row 2
source_reservation_id R-5001 R-5001
source_guest_id G-1001 G-1002
arrival 2026-11-03 00:00:00.000 2026-11-03 00:00:00.000
departure 2026-11-05 00:00:00.000 2026-11-05 00:00:00.000
checked_in_at 2026-11-03 14:41:00.000 2026-11-03 14:41:00.000
checked_out_at 2026-11-05 09:55:00.000 2026-11-05 09:55:00.000
pre_checked_in_at 2026-11-02 19:10:00.000
pre_checked_out_at
cancelled_at

revenues.csv (selected columns)

Column Row 1 Row 2 Row 3
source_id REV-1 REV-2 REV-3
source_created_at 2026-11-03 23:00:00.000 2026-11-04 23:00:00.000 2026-11-05 08:30:00.000
name Room Night Room Night Breakfast
type Room Room Item
source_category_id 10 10 20
source_category Accommodation Accommodation Food
count 1 1 2
currency EUR EUR EUR
amount 179.00 179.00 40.00
amount_net 164.22 164.22 36.70
vat_percent 9 9 9
source_reservation_id R-5001 R-5001 R-5001
source_debtor_id G-1001 G-1001 G-1001

Things to notice

  • AMSTY, AMSTY-207, G-1001 and R-5001 are written identically in every file.
  • Giulia booked and stays, so she has one mapping row as MainGuest / TRUE. Marco is AdditionalGuest / FALSE.
  • Arrival and departure are dates at 00:00:00.000 (2 nights); actual check-in/out times are in UTC.
  • Breakfast is Item; the detail "Food" goes into source_category.
  • Room revenue (2 × 179.00) + breakfast (40.00) = 398.00 = forecast_revenue.

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