Industries / 01
Hotel Revenue Management
Price with the whole market in view.
Pricing, forecasting, and competitive intelligence from people who have worked inside hotel revenue teams.
Overview
How we approach it.
Hotel pricing breaks down when each property prices its own way and revenue leaders spend their week reconciling spreadsheets. Data from the PMS, CRS, channel manager, and rate shops sits in separate places, group business gets decided without the full displacement picture, and the portfolio never settles on one approach.
We bring those sources into one revenue data layer, then build demand forecasting and pricing at the room-type and rate-code level on top of it, with accept or reject guidance for group and catering business. Alerts flag rate parity violations and competitor moves. The people doing the work have sat inside hotel revenue teams. For one operator, the result was one pricing strategy across every property while the portfolio grew more than 400% in a year.
Systems we connect
Where your data lives today.
The problem
What gets in the way.
Revenue leaders stuck reconciling spreadsheets
Pricing practices that vary from property to property
Group business decided without the full displacement picture
What we build
What changes.
- 01
Unified revenue data across PMS, CRS, channel manager, and rate shops
- 02
Demand forecasting and pricing at the room-type and rate-code level
- 03
Total revenue optimization across spa, F&B, golf, and fees
- 04
Accept or reject guidance for group and catering business
- 05
Alerts for rate parity violations and competitor moves
In practice
Work we've shipped.
Travel & Leisure
Revenue management that kept pace with 400% growth
A centralized pricing platform and a new revenue management team for a portfolio that grew over 400% in a year.
400%+
portfolio growth within a year
2+
points of RevPAR Index over comparable hotels
Travel & Leisure
Billions of rate recommendations a year
An AI-augmented pricing architecture that moved a hotel operator from spreadsheet pricing to billions of rate recommendations a year.
Billions
rate recommendations generated a year
Related services
How we deliver it.
FAQ
Questions buyers ask.
01How do hotels use machine learning for pricing?
Hotels use machine learning to forecast demand and recommend rates at the room-type and rate-code level, reading market signals as they change instead of relying on spreadsheets. For one hotel operator, we designed an AI-augmented pricing architecture that generates billions of rate recommendations a year. The models sit on a unified data layer fed by the PMS, CRS, channel manager, and rate shops.
02How should a hotel decide whether to accept or reject group business?
A hotel should judge group business against what it displaces, not just against its own revenue. We build accept or reject guidance for group and catering business that puts the full displacement picture in front of the decision. It draws on the same unified revenue data that drives forecasting and pricing.
03How do you keep pricing consistent across a growing hotel portfolio?
You keep pricing consistent by centralizing it on one platform, built on operational and market data, that every property prices from. We built that for an operator whose portfolio grew more than 400% in a year, and helped establish and train the revenue management team that runs it. The result was one pricing strategy across every property and more than 2 points of RevPAR Index over comparable hotels.
Bring us the hard problem.
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