Hotel Rate Intelligence & Machine Learning Pricing Platform
A competitor rate intelligence and ML-based future pricing platform for a hotel chain operating hundreds of properties across the UK and EU.
Near real-time
competitor price monitoring across hundreds of hotels, multiple times daily
ML-inferred
occupancy visibility from pricing signals without needing competitor booking data
365-day forward
price recommendations factoring events, holidays, and demand forecasts
Tens of millions
in additional annual revenue potential from closing the pricing reaction gap
The brief
Major UK & EU Hotel Chain
HospitalityA competitor rate intelligence and ML-based future pricing platform for a hotel chain operating hundreds of properties across the UK and EU.
The challenge
What needed to change
Our client owns and manages several hundred hotels across the UK and EU. Hotel chains publish nightly room rates up to 365 days in advance, reviewing them daily and increasing prices as occupancy rises. Because these adjustments are manual, any delay in recognising increased occupancy or a competitor rate movement means missed revenue at scale. The client needed real-time competitive intelligence: visibility of competitor pricing at every location, the ability to infer competitor occupancy from pricing signals, and automated suggestions to adjust their own pricing before opportunities were lost.
Key requirements
What we needed to deliver
The solution
How we solved it
We engineered a web data collection platform allowing our client to define specific competitors, locations, and scraping frequencies. The system gathers nightly prices for the upcoming 365 days from competitor websites, checking each location multiple times daily.
Anti-scraping bypass and data warehouse
A key engineering challenge was bypassing the anti-scraping mechanisms deployed by competitor booking sites. Collected pricing data is stored in a purpose-built data warehouse, providing a clean, queryable history of competitor pricing across all locations.
Machine learning: occupancy inference and real-time alerts
Collected data is processed using machine learning to identify unusual price changes — triggering real-time alerts — and to infer competitor occupancy levels from pricing patterns, giving visibility into demand surges even when booking figures are not publicly available.
Predictive rate optimisation
A second ML model analyses historical rates and occupancy data, combined with known future bookings, upcoming events, and public holidays, to generate estimated future rate potential for any date within the next year. The system outputs recommended prices at configurable certainty levels to room pricing software and web clients for near-automated revenue management.
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