Platform · Goibibo

Goibibo Data Scraping

Same group as MakeMyTrip. So the question is not whether to collect it — it is whether collecting both buys anything.

Goibibo data scraping collects hotel rates, room types, plan conditions and availability across Indian markets. It sits in the same group as MakeMyTrip, so inventory overlap is substantial. The useful engagement is therefore usually the difference between the brands rather than a second full panel — and we measure the overlap before quoting.

Two of the rows on your travel hub are one company. That is worth knowing before commissioning two engagements.

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goibibo.jsonl LIVE FEED

{"brand":"goibibo","group":"same_as_makemytrip", "property_id":"gi-44120","city":"Example city", "rate":4200,"currency":"INR", "rate_plan_type":"nonrefundable", "panel_schedule_id":"india-ota-sync"} {"overlap_with_group_brand":0.84, "recommendation":"collect divergence only", "note":"two rows on your hub are one company"} {"rate_gated":true,"gated_reason":"app_only_fare", "gated_share":0.31, "payment_condition_text":"as published", "effective_price":"null"}

3 of 1,884,220 rate-plan rows · Indiagroup overlap measured · payment offers are conditions · schema v1.0

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to Goibibo or its owners. Goibibo 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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Goibibo at a glance

How we handle Goibibo specifically

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

Brand: Goibibo — India
The group: Same as MakeMyTrip
Consequence: Substantial inventory overlap
So: Measure it before commissioning both
What differs: Brand-level promotions, wallet mechanics, app pricing
Indian specifics: App-gated fares are heavy. Share reported
Payment: Payment-linked discounts are common. Conditions, not prices
Refresh: Daily per stay date; sub-daily around peak travel periods

Platform specifics

Sibling brands, and Indian OTA mechanics

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

Measure the overlap first

Group-level supply sharing means most properties appear on both brands, frequently at the same rate for the same plan.

Property overlap is high in most Indian markets.
Rates are frequently identical for matched property and plan.
Promotions, wallet mechanics and app-only pricing differ at brand level.
So the value is in divergence, not in a duplicate catalogue.

overlap_with_group_brand is measured on your own markets in the pilot. Where overlap is very high we recommend collecting divergence only — the same position as our Hotels.com and Postmates pages.

And a shared schedule where both are collected

If the point is measuring divergence, the brands must be observed together. panel_schedule_id is shared, because a gap measured from records taken hours apart on a fast-moving OTA contains a timing artefact.

App gating and payment conditions are heavier here

App-only pricing

Indian OTAs use app-exclusive fares and discounts heavily. Where a rate requires the app or a signed-in session, it is null with a reason and gated_share is reported.

We do not create accounts, use client credentials or emulate apps — the boundary on our access page. On this market that produces a meaningful gap, and reporting it is the honest handling.

Payment-linked discounts

Bank card, wallet and UPI-linked offers are common and are conditions rather than prices. A discount requiring a specific card is not available to a customer without it.

payment_condition_text is captured as published and effective_price is null with a reason where the discount is payment-conditional — the position our bank card offer page sets out in full.

Standard OTA mechanics

Rate plan as the record, both dates on every record with lead time derived, struck-through references captured and never treated as prior prices. As on our MakeMyTrip page.

Scope

What we collect on Goibibo, 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

overlap_with_group_brand measured in the pilot before commissioning
A shared schedule where both group brands are collected
App-gated rates null with a reason, with gated_share reported
payment_condition_text as published, with no effective price computed
One record per rate plan, with conditions structured
Stay date and observation date, with lead time derived
reference_price_displayed captured, never treated as a prior price
Brand-level promotions recorded separately from group inventory
Review counts and ratings, without reviewer identity

❌ What we do not, and why

A second full panel sold where group overlap is very high
App emulation, account creation or client credentials
An effective price computed from a payment condition
A struck-through reference treated as price history
Bookings, occupancy, guest or reviewer identity

Core Goibibo fields

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

Field	What it is on this platform
brand / group	A group brand, not an independent OTA
property_id / city	The property and where it is
room_type / rate_plan_id / rate_plan_type	The record is the plan
stay_date / observed_at / lead_time_days	Both dates and the derived axis
rate / currency	As displayed
overlap_with_group_brand	Measured per market in the pilot
rate_gated / gated_reason / gated_share	App and member gating, and the share
payment_condition_text	As published. Not a price
effective_price / effective_null_reason	Null where payment-conditional
reference_price_displayed	As displayed. Not a prior price
panel_schedule_id	Shared where both brands are collected

Use cases

What teams do with Goibibo data

Deciding whether to collect both group brands

Overlap measured on your own markets before commissioning, with a recommendation to collect divergence rather than a duplicate catalogue where the share is high.

Brand-level promotional divergence

Promotions, wallet mechanics and app pricing differ at brand level even where inventory is shared, which is the part worth collecting.

Indian OTA gating measurement

App-gated share reported per market, which on this market is meaningful enough that a public-only view understates promotional depth substantially.

Plan-level rate comparison

Rate plans as the record, so this brand is compared to other OTAs on matched plans rather than on lowest displayed price.

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

Send us a Goibibo 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.

Goibibo is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Goibibo 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 Goibibo-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

Goibibo data scraping: frequently asked questions

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

Same group, different consumer brand. Inventory overlap is substantial and rates are frequently identical for matched property and plan.

Two of the rows on your travel hub are one company, which is worth knowing before commissioning two engagements.

That is measurable and we measure it first. Where overlap is very high we recommend collecting divergence — brand-level promotions, wallet mechanics and app pricing — rather than a duplicate catalogue.

If both are collected, they share a schedule so the divergence is not a timing artefact.

Enough to matter, and we report it per market. Indian OTAs use app-exclusive fares heavily, and we do not emulate apps, create accounts or use client credentials.

The gated share is the honest handling of a gap we will not close.

No. A discount requiring a specific card is a condition rather than a price — it is not available to a customer without that card.

We capture the condition text as published and leave effective price null with a reason.

No, and we do not treat it as one. Building a discount series from displayed reference figures manufactures history out of marketing copy.

Usually less than a full OTA engagement where you already collect the group's other brand, because the right programme captures divergence.

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

See real Goibibo 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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