In online travel, every booking is won or lost in comparison.
A traveller lines up hotels on various platforms, then books wherever they get the best deal. The gap between winning and losing that booking can be a few hundred rupees, and it can change by the hour.
According to Google and Kantar’s Travel Rewired report, Indian travellers reference an average of seven sources while planning a trip. In a market where travellers compare across search, OTAs, YouTube, and travel platforms before booking, price visibility is no longer a back-office revenue task; it is a conversion lever.
For Cleartrip, this made competitor pricing high-stakes decision. But manual price checks couldn’t keep pace with a market that never stops moving.
Without continuous visibility, the revenue team risked two costly outcomes – pricing too high and losing bookings, or discounting too deep and losing margin.
To fix this, Cleartrip partnered with mFilterIt to build a competitive intelligence system that moves as fast as the market does. See how it helped them.
The Cleartrip Case Study: Pricing at the Speed of the Market
The Challenge: Thousands of hotels and moving prices
On Google hotels, the same property can appear with prices from multiple OTAs on a single comparison screen. This makes price competitiveness instantly visible to the customer.
For Cleartrip’s hotels revenue team, that raised several hard questions:
- With price variations being continuous for each of the thousands of hotels and different check-in dates, how do you compete?
- How do you ensure that your pricing model doesn’t take a blanket approach, thereby reducing your conversions or cutting into profits?
- And since travelers evaluate even your ratings, reviews, amenities and descriptions of the property, how do you know if you match up to your competitor?
Manual checks could not answer these at scale. Without continuous competitive intelligence, the team lacked a complete view of market pricing, competitor promotions, and listing quality.
The Approach: Two layers of competitive intelligence
A system that combined a wide view of the market with a deep view of the closest rival.
Layer 1: The market benchmark
The pricing intelligence and analysis tool continuously monitored 45,000+ distinct hotel properties on Google Hotels across multiple check-in dates, and the coverage was not capped.
This gave Cleartrip its price position against every competing OTA, along with pricing trends across travel dates. The Google Hotels dataset served as the primary market benchmark for pricing decisions.
Layer 2: The competitor deep dive
For 5,000+ strategically selected hotels, our tool tracked Cleartrip’s largest OTA competitor in detail:
- Pricing intelligence: Selling price, coupons and promotional offers
- Content intelligence: Property descriptions, ratings, review counts, amenities and hotel highlights
The second layer is where the thinking shifts. Cleartrip was no longer only asking “Are we cheaper?” It was also asking, “Is our listing more convincing?”
The Foundation: Data you can trust at scale
mFilterIt’s platform switches between multiple data collection methods, backed by dynamic network routing and failover infrastructure, so monitoring continues even when one method is temporarily restricted. For Cleartrip, that meant consistent daily market visibility, more complete data, and faster detection of competitor price changes.
