Maple Grand – Recovering from Low Season Losses with AI Yield Management
“The Pickup Summary is brilliant — it showed us where bookings were slowing down before the numbers fell. We were able to react immediately and fill rooms again.”
— Emma Collins, Revenue Analyst, Maple Grand Hotels
Background
Maple Grand, a 110-room business hotel in Pune, faced a drastic fall in occupancy during the low season months of May–July. Their static pricing model couldn’t react fast enough to changing market conditions, leading to nearly 25% year-over-year revenue loss. With manual rate adjustments and limited market insight, the hotel struggled to maintain competitiveness during slower periods.
Challenges
- Low-season occupancy dropped below 40% despite steady demand in nearby competitors.
- Rates remained unchanged for weeks even when the market shifted.
- Manual intervention slowed down recovery actions and increased rate disparity across channels.
Hotelitix Solution
Hotelitix implemented AI Yield Manager and Pickup Summary Analytics to enable Maple Grand’s team to respond dynamically to market demand. The AI Rate Generation Engine forecasted demand up to 30 days in advance and automatically recommended daily price adjustments. Additionally, Smart Event Detection identified upcoming trade expos and local events, triggering automated rate increases during high-demand dates.
Results
- Recovered 28% of lost revenue within two months of implementation.
- 22% increase in weekday occupancy during the low season.
- Reduced rate-change decision time from hours to just minutes.
+28%
Revenue Recovered
+22%
Weekday Occupancy Gain
↓90%
Reduction in Manual Effort
“Hotelitix helped us transform reactive pricing into proactive strategy. We no longer wait for losses to happen — the AI tells us when to act.”