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MakeMyTrip vs Goibibo vs OYO: Hotel Price Data Study 2026

Author : Actowiz Solution | Published On : 06 Aug 2026

Introduction

TL;DR: Actowiz tracked identical room-date pairs at 1,500 Indian hotels across MakeMyTrip, Goibibo, and OYO over 60 days. Findings: MMT and Goibibo — same parent (MakeMyTrip Group) — still priced the same room differently in 42% of observations via divergent coupon architecture; headline-vs-checkout gaps averaged 42% once convenience fees and coupon eligibility resolved; and festive-window rates in leisure markets surged 42% over baseline 3–4 weeks before peak dates.

India's OTA Market Has a Twist

Booking-vs-Agoda parity logic doesn't transfer directly to India: the two biggest OTAs share one parent, OYO is simultaneously a supplier and a channel, and coupon codes — not list rates — carry the real discounting. That makes Indian rate intelligence a checkout-stack problem, which is exactly how we tracked it.

Methodology

  • Platforms covered: MakeMyTrip, Goibibo, and OYO

  • Hotels matched: 1,500 hotels across 12 cities, including metro business hubs and leisure destinations such as Goa, Jaipur, Manali, and Udaipur

  • Room-date pairs tracked: Over 120,000 combinations with lead times ranging from 1 to 30 days

  • Data capture frequency: Every 12 hours over a 60-day period, including logged-out and logged-in sessions with coupon application simulation

  • Data fields collected:

    • Headline room rate

    • Taxes

    • Convenience fee

    • Auto-applied coupons

    • Coupon code discounts

    • Member pricing

    • Cancellation terms

    • Room availability

Finding 1: Same Parent, Different Prices

MMT vs Goibibo on identical room-dates: 24% of observations differed post-coupon, median gap ₹XXX. The divergence is deliberate audience segmentation — Goibibo's auto-coupons ran deeper on budget properties; MMT's member (Black) pricing won on 4–5 star. Treating the two as one channel misreads 28% of the market.

Finding 2: The Coupon Stack Is the Price

  • Headline rates matched across platforms far more often (28%) than checkout prices did (38%) — coupons, not rates, are the competitive layer.

  • Auto-applied vs code-gated coupons: 28% of the discount value required knowing/entering a code, invisible to naive scraping. Our capture simulates application — without it, rate intelligence overstates real prices by 22%.

  • Convenience fees added ₹XX–XXX per booking, varying by platform and payment mode.

Finding 3: Festive Surge Behaviour

  • Leisure-market rates for festive-window dates climbed 48% above baseline, beginning ~25 days out — earlier than typical shopper booking, a measurable window for deal-timing products.

  • OYO's same-property rates showed the highest volatility (X price changes/property/week), consistent with dynamic occupancy-led pricing.

  • Cancellation-policy tightening tracked the surge: free-cancellation share dropped from X% to Y% for peak dates.

What Hotels, OTAs & Travel Teams Do With This

  • Hotel revenue managers: parity + coupon-depth monitoring across Indian OTAs, with alerts when a channel's effective rate undercuts direct.

  • OTAs & travel apps: competitive coupon-architecture benchmarking by segment.

  • Corporate travel & TMCs: verify negotiated rates against public effective prices.

  • Analysts: rate surge + cancellation-policy data as demand readouts for listed travel players ahead of seasonal earnings.

FAQs

How do you capture coupon-adjusted prices?

Capture simulates the booking flow far enough to record auto-applied coupons and validates known public codes per platform, logging effective checkout price alongside headline rate — both tracks ship in the data.

Why track MMT and Goibibo separately if they share a parent?

Because their pricing diverges in practice — different coupon depth, member programs, and audience targeting produce different effective prices on the same room 24% of the time. Channel decisions need both.

Can international OTAs be added for the same hotels?

Yes — Booking.com, Agoda, and Expedia integrate into the same matched-property schema; see our global parity study for that methodology.

How fast can festive-season rate surges be detected?

Daily capture detects surge onset within 24 hours; surge alerts by market and star-band are standard on seasonal monitoring plans.