Revenue That Optimizes Itself
a regional hotel group with 40+ properties
RevPAR increase
higher occupancy on shoulder nights
saved per revenue manager weekly
A small revenue management team set prices across dozens of properties using spreadsheets and instinct, updating rates a few times a week while demand shifted hourly. Local events, competitor moves, and booking pace changes went unnoticed until occupancy reports arrived. Rooms sold too cheap on high-demand nights and sat empty on soft ones, and the team had no capacity to watch every market at once.
How we approached it
Consolidated booking pace, competitor rates, local event calendars, and historical demand into a single revenue data platform.
Trained gradient-boosted demand models that forecast occupancy by property, room type, and date, repricing continuously as bookings come in.
Paired the models with an LLM copilot that explains each pricing move in plain language and lets revenue managers ask why a rate changed.
Ran the system in recommendation mode across five pilot properties first, moving to automated pricing only after the team trusted the calls.
“Every property now gets the attention our two best performers used to get. The system watches all forty markets, and my team handles strategy instead of spreadsheets.”
Yes. Managers can pin rates or set guardrails per property at any time, and the copilot explains every automated move so overrides are informed rather than defensive.
Event calendars feed the demand models directly, and unusual booking pace triggers an alert with a suggested response, so new events are caught by behavior even when they are not on a calendar.
Pilot properties showed measurable gains within the first quarter of automated pricing, and the full portfolio reached the reported lift over the first year of operation.
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