Industry
Hotel Rate Intelligence
Partnership Since
2025
Location
Europe
Daily OTA Rate Intelligence for a Hotel Revenue-Management Platform
How DataDwip keeps 40+ rate sources fresh every morning for a fast-growing hospitality software company.
Illustrative example. This case study is written to the shape of a typical DataDwip engagement: the client is not named, and the figures are representative of the work rather than audited results from a single account.
Client overview
Our client is a European revenue-management platform serving 1,800+ independent hotels and small groups across 12 countries. Its software recommends room rates each morning based on competitor prices, demand signals and each property's history. Because those recommendations depend on competitor rates collected from online travel agencies, brand websites and metasearch engines, the freshness and completeness of that data is the product's most visible quality signal.
The challenge
The client had built its own rate-collection scrapers in-house. As the customer base grew, so did the number of sources: every new market added OTAs, regional booking sites and hotel brand websites with their own structures and protections. By early 2025 the two engineers responsible for collection were spending most of their week repairing broken scrapers rather than building product, and the company was hiring a third.
Silent breakage: Site redesigns and anti-bot changes cut records without alerts, so gaps were discovered by customers seeing missing competitor rates on the morning dashboard.
Missed delivery windows: Overnight runs regularly finished after 06:00 local time, the cut-off for the pricing engine, forcing rate recommendations to fall back on the previous day's data.
Coverage on hold: Expansion into two new markets was delayed because the existing team could not onboard the 15+ additional sources those markets required while keeping the current set running.
The solution: a dedicated data acquisition team
DataDwip put a dedicated team of three named engineers, a shared QA lead and a delivery manager on the client's rate collection. Within two weeks the team had audited the full source list, rebuilt the fragile collectors on DataDwip infrastructure and moved delivery to the client's Snowflake warehouse in the client's own schema. Collection now runs every night, is validated before hand-off and is repaired by the DataDwip team when a source changes.
01
Scale
40+ OTA, brand-site and metasearch sources across 12 countries are collected each night, producing around 180,000 rate records a day, with new sources onboarded every month as the client enters new markets.
02
Freshness
Every run is scheduled to complete before the client's 06:00 pricing cut-off. Delivery times are logged per source and reported weekly, so the client can commit to its own customers with confidence.
03
Data accuracy
Each delivery passes automated checks for completeness, currency, duplicates and drift, followed by manual sample QA. Schema changes carry 30 days' notice, so the client's pricing engine never breaks on a surprise.
Results
Within the first quarter the client stopped treating rate collection as an engineering problem and started treating it as a service it could rely on.
On-time delivery
97% of nightly runs delivered before the 06:00 cut-off across the quarter, up from an estimated 70% with the in-house pipeline, with every late run explained in the weekly digest.
Coverage expanded
Two new markets launched six weeks after kick-off, with 17 new sources onboarded and maintained by the DataDwip team without any client engineering time.
Engineering time returned
The client's two collection engineers moved to product work full time, and the planned third hire was not made. Breakages (14 in the first quarter) were repaired by DataDwip in an average of under a day.
The client now holds DataDwip to a written freshness, coverage and data-quality SLA, reviews a weekly source-health digest, and plans source expansion with the team every quarter rather than firefighting it every week.
Data your pricing engine can trust every morning
The engagement gave the client something an in-house pipeline never had: predictable, measured, continuously maintained competitor data.
Complete coverage
Every competitor rate the pricing engine needs, from every channel the client's hotels compete on, collected on one schedule.
Predictable freshness
A delivery window written into the contract and measured every day, not a best effort that depends on who is on call.
Stable schema
Rate data lands in the client's warehouse in the client's schema, with notice on every change, so downstream models stay intact.
Scales with the roadmap
New markets and sources are a monthly routine for the DataDwip team, not a project that competes with product work.
Looking Ahead
The client is now extending collection to metasearch and package-rate sources and increasing frequency to twice daily in its most competitive markets. Because the team, infrastructure and SLAs are already in place, each expansion is scoped in a quarterly review and delivered in weeks. "Rate data used to be the thing that woke us up at night. Now it's just there." — Head of Product, client
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