Platform · MakeMyTrip

MakeMyTrip Data Scraping

Q: How do you handle MakeMyTrip coupons?

By recording discount_funder as platform, hotel or undetermined. A platform-funded coupon is the OTA's customer acquisition cost and tells you nothing about the hotel's pricing; a hotel-funded discount does. Treating every reduction as a hotel decision is a common and misleading error.

Q: What does MakeMyTrip collection cost?

We quote individually. The distinctive driver is the grid: properties times stay dates times rate plans times observation dates. A single city with a 90-day forward window is already a large record count. We usually design a stay-date panel rather than sweeping a full calendar. One scoping call, a free pilot within 24 hours, then a fixed monthly quote. Request a quote.

A hotel does not have a price. It has rate plans, and each one is a different product with different conditions.

MakeMyTrip data scraping collects hotel rates, room types, rate plan conditions and availability across Indian cities. The structural rule, as on any OTA: the record is a rate plan, not a hotel. Refundable and non-refundable, breakfast-included and room-only are different products, and a single hotel price is an average of things a guest never chooses between.

An inquiry last quarter asked for MakeMyTrip, Booking.com and Expedia with Jaipur, Gurgaon and Lucknow as priority markets. Tier-two Indian cities are where OTA rate behaviour is least like the international picture.

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mmt_rates.jsonl LIVE FEED

{"property_id":"mmt-44120","city":"Jaipur","city_tier":"tier2", "room_type":"Deluxe Double","rate_plan_id":"rp-nonref-nobf", "stay_date":"2026-11-14", "observed_at":"2026-08-25T06:00Z", "lead_time_days":81, "rate_excl_tax":4200,"tax_amount":504, "rate_incl_tax":4704,"currency":"INR", "headline_basis":"excl_tax", "refundable":false,"breakfast_included":false} {"property_id":"mmt-44120","rate_plan_id":"rp-ref-bf", "stay_date":"2026-11-14","rate_excl_tax":5350, "refundable":true,"breakfast_included":true, "note":"same room, same night — a hotel-level average describes neither plan"} {"property_id":"mmt-88012", "discount_funder":"undetermined", "caution":"coupon funding unclear — not attributed to the hotel"}

3 of 6,880,410 rate-plan rows · 90-day forward windowrecord = rate plan · both dates on every row · schema v1.0

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

How we handle MakeMyTrip specifically

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

Platform: MakeMyTrip — India's largest OTA
The record: A rate plan, not a hotel and not a room
Two dates: Stay date and observation date. A rate series needs both
Lead time: Derived from the two. It is the axis the curve moves on
Inclusions: Breakfast, cancellation and taxes as flags, not folded into the rate
Coupons: MMT-funded coupons kept separate from hotel-funded discounts
Tier-two cities: Different rate behaviour from metros. Both worth covering
Refresh: Daily per stay date; sub-daily near high-demand dates

Platform specifics

What a usable OTA rate dataset needs

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

Two dates, and most feeds carry one

A hotel rate observation has two dates and they answer different questions.

Stay date — the night being priced.
Observation date — when we saw that price.

A feed carrying only one is close to uninterpretable. The same night costs different amounts observed 90 days out and 3 days out, and without both dates you cannot tell whether a price moved because the market moved or because the booking window closed.

We record stay_date, observed_at and derived lead_time_days. The pricing curve as a stay date approaches is the finding in most engagements, and it only exists if both dates are present.

This also shapes the schedule: a useful dataset observes the same stay dates repeatedly rather than observing today's prices daily. Those are different collection designs and only the first produces a curve.

Rate plans, and why a hotel price is a fiction

A hotel on MakeMyTrip carries many rate plans against the same room: refundable and non-refundable, breakfast-included and room-only, pay-now and pay-at-hotel, plus member and coupon variants.

A single hotel price averages products a guest chooses between, so it describes none of them.
The cheapest plan is usually non-refundable, so a lowest-price comparison compares a restricted product against a flexible one.
Rate parity analysis is meaningless without plan-level matching, since the same hotel on two OTAs may be showing different plans.

We deliver one record per rate plan with refundable, breakfast_included, payment_timing and the cancellation window as structured fields. A lowest-price rollup is available as a computed view with the plan it came from named.

Coupons, taxes and tier-two behaviour

Coupon funding

MakeMyTrip runs platform-funded coupons alongside hotel-funded discounts, and they behave differently: a platform coupon is a customer acquisition cost to the OTA and tells you nothing about the hotel's pricing, while a hotel-funded discount does.

We record discount_funder where it is determinable and undetermined where it is not, rather than treating every reduction as a hotel decision.

Taxes

Indian hotel taxation varies by rate band, so two hotels quoting similar pre-tax rates can differ meaningfully at checkout. We capture rate_excl_tax, tax_amount and rate_incl_tax where all three are shown, and flag which basis the displayed headline used.

Tier-two cities

The inquiry behind this page named Jaipur, Gurgaon and Lucknow. Tier-two Indian markets have thinner inventory, sharper event-driven spikes and less rate-plan variety than metros. A panel built only on Delhi and Mumbai would miss all of that, and the collection design — how many properties, how many stay dates — needs to differ.

Scope

What we collect on MakeMyTrip, 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

One record per rate plan, with the room type it applies to
Stay date and observation date, plus derived lead time
Refundable, breakfast and payment timing as structured flags
Cancellation window where published
Rate excluding tax, tax amount and rate including tax where shown
Discount funder where determinable, undetermined where not
Property identity, star rating and location
Availability state, distinct from a property not being listed
Tier-two city coverage designed separately from metros

❌ What we do not, and why

A single hotel price averaging different rate plans
Occupancy, bookings or how full a property is
Guest or reviewer personal data
A discount attributed to the hotel where funding is unclear
Rates behind a signed-in or member session

Core MakeMyTrip 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 and where it is
room_type / rate_plan_id	The record is the plan, not the property
stay_date / observed_at / lead_time_days	Both dates, and the axis derived from them
rate_excl_tax / tax_amount / rate_incl_tax / currency	Tax basis made explicit
headline_basis	Which of the three the displayed price used
refundable / cancellation_window	Structured, not inferred from copy
breakfast_included / payment_timing	Inclusions as flags
discount_funder	platform, hotel or undetermined
availability_state	Available, sold out or not listed — three states
star_rating / property_type	As published
city_tier	So tier-two behaviour can be analysed separately

Use cases

What teams do with MakeMyTrip data

Rate parity monitoring across OTAs

Plan-level records so MakeMyTrip, Booking.com and Expedia are compared on matched plans rather than on lowest displayed price, which is where parity analysis usually goes wrong.

Pricing curve by lead time

The same stay dates observed repeatedly with lead time derived, producing the curve that daily snapshots of today's prices can never show.

Tier-two market entry pricing

Jaipur, Gurgaon, Lucknow and similar markets covered with a panel designed for thinner inventory, rather than a metro design applied to a different market shape.

Promotional funding analysis

Discount funder separated where determinable, so an OTA acquiring customers is not mistaken for a hotel discounting its own inventory.

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

Send us a MakeMyTrip 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.

MakeMyTrip is usually collected alongside its competitors

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

MakeMyTrip data scraping: frequently asked questions

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

Because the same night costs different amounts observed 90 days out and 3 days out. With only one date you cannot tell whether a price moved because the market moved or because the booking window closed.

We record both and derive lead time, which is the axis the pricing curve actually moves on.

Because a hotel does not have a price. It has refundable and non-refundable, breakfast-included and room-only, pay-now and pay-later — different products with different conditions.

A single hotel price averages things a guest chooses between. And the cheapest plan is usually non-refundable, so a lowest-price comparison pits a restricted product against a flexible one.

Yes, and it only works at plan level. The same hotel on two OTAs frequently shows different plans, so comparing lowest displayed prices produces parity findings that are artefacts of plan mix.

We match on room type and plan conditions, and flag where no comparable plan exists on one side rather than forcing a comparison.

By recording discount_funder as platform, hotel or undetermined. A platform-funded coupon is the OTA's customer acquisition cost and tells you nothing about the hotel's pricing; a hotel-funded discount does.

Treating every reduction as a hotel decision is a common and misleading error.

No. Occupancy is not published, and inferring it from rate availability is the same error as inferring Airbnb occupancy from calendar blocks — the relationship exists but is not stable enough to sell as a number.

We deliver availability states with timestamps and lead time, and the inference stays yours.

We quote individually. The distinctive driver is the grid: properties times stay dates times rate plans times observation dates. A single city with a 90-day forward window is already a large record count.

We usually design a stay-date panel rather than sweeping a full calendar. One scoping call, a free pilot within 24 hours, then a fixed monthly quote. Request a quote.

See real MakeMyTrip 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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