AI Revenue Management for Hotel Chain
Chain of 4 hotels in Tuscany - 95 employees, 280 total rooms across Florence, Siena, and the Tuscan coast
+41% (from 78 to 110 EUR)
Average RevPAR
+12 points (from 62% to 74%)
Average annual occupancy
+620,000 EUR/year
Room revenue
-90% (from 15h/week to 1.5h)
Pricing management time
+28%
Low season ADR
The problem to solve
Room prices were manually updated twice a week based on intuition and experience. The average annual occupancy was 62% with a RevPAR of 78 EUR, significantly below the Tuscan market potential. No dynamic differentiation across sales channels.
What we implemented
AI revenue management system that analyzes in real time: occupancy, booking pace, local events, weather, competitor pricing, seasonal history, and demand by channel. It automatically updates prices across all channels (booking engine, OTAs, tour operators) every 4 hours with a differentiated strategy per segment.
Measured impact
Average RevPAR
+41% (from 78 to 110 EUR)
Average annual occupancy
+12 points (from 62% to 74%)
Room revenue
+620,000 EUR/year
Pricing management time
-90% (from 15h/week to 1.5h)
Low season ADR
+28%
Timeline
8 weeks (4 development + 2 PMS/channel manager integration + 2 tuning)
Investment
40,000 - 60,000 EUR (Digital Innovation Grant up to 100,000 EUR)
Similar projects
Want similar results in your company?
We'll analyze your processes for free and show you where AI can generate the greatest impact.