Treebo Powers Dynamic Pricing Across 100+ Cities With Reliable Competitor Rate Data

About Treebo

Treebo is India’s largest branded budget hotel chain. It partners with independent, standalone hotels across more than 100 Indian cities and brings them under one branded standard, covering consistent quality, standardized amenities, and a shared technology stack, backed by an on-the-ground quality program. Its mission is to give budget travelers the same certainty about quality that branded hotels offer at higher price points, without the higher price. Delivering on that promise depends on getting the price right on every room, every night, in every city Treebo operates in.

Challenge

Treebo prices a perishable product at scale. A room left unsold on the night of check-in is revenue lost forever, not revenue deferred. Pricing every room correctly, every night, across more than 100 cities created several hurdles:

Perishable inventory, unforgiving timing:

Every unsold room-night is permanently lost revenue, so each rate has to reflect live demand before the booking window closes.

Fragmented market data:

Accurate rates depend on current pricing and availability from comparable hotels across dozens of micro-markets, refreshed often enough to act on before a booking window closes.

Defining the competitor set:

Raw numbers are not enough. Treebo had to judge which competitor hotels genuinely compare to a given property, weighing distance and review scores, not just price.

Scale and maintenance:

Collecting this by visiting listing portals by hand, city by city, does not scale to 100-plus cities, and it breaks every time a portal redesigns its pages.

These issues turned in-house market data collection into a constant drain on engineering time, and any gap in the data translated directly into mispriced rooms and lost revenue.

Why PromptCloud

Treebo’s selection criteria centered on coverage, cadence, and offloading maintenance entirely:

Coverage across portals and markets:

Structured rate and availability data from every listing portal and every micro-market Treebo tracks, not a single competitor source.

Frequency on demand:

Data refreshed at whatever cadence each pricing decision requires, rather than whenever staff had time to check.

Zero maintenance overhead:

When a portal changes its listing layout, absorbing that change becomes PromptCloud’s job, not an interruption to Treebo’s pricing.

Reliability at scale:

A dependable feed that the revenue and engineering teams could plan around across every city in the network.

PromptCloud’s managed web data pipeline delivered that combination: broad portal and market coverage, delivery on the schedule Treebo’s pricing models needed, and no scraper code for Treebo to maintain in-house. The practical result was a market-data layer the pricing team could simply rely on rather than constantly defend.

Implementation

Where PromptCloud sits in the pipeline

PromptCloud handles the market-data acquisition layer beneath Treebo’s in-house pricing engine:

Market data acquisition:

PromptCloud collects structured rate and availability data across every portal and micro-market Treebo tracks, delivered on a set schedule.

Feeding the pricing engine:

That feed powers Treebo’s in-house system, which blends competitor pricing, projected room-night performance, and real-time performance into a single rate designed to maximize revenue per available room.

Signalling inventory decisions:

Live market signals drive action. If listings show a micro-market trending toward a sellout, that is a direct cue to raise rates ahead of demand.

Results

With market-data collection handled, Treebo’s revenue and engineering teams saw measurable change:

Complete market coverage:

Rate and availability data flowing from every portal and micro-market Treebo tracks, on a consistent schedule instead of ad hoc manual checks.

Sharper pricing:

A pricing engine blending three live signals into one rate recommendation, fed by data the team no longer chases by hand.

Smarter inventory decisions:

Rates raised on live signals, such as a micro-market trending toward a sellout.

Engineering time reclaimed:

Hours redirected from scraper maintenance to the pricing models and to Hotel Superhero, the SaaS product Treebo built for other hotel owners.

Room to scale:

A data science function robust enough to support expansion across 100-plus cities without a proportional increase in manual research headcount.

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