TL;DR
Last-minute bookings are up, compressing lead times and making traditional forecasting less reliable. Revenue management teams must pivot to real-time, multi-source demand and pricing data—including OTA, brand.com, GDS, and metasearch feeds—while factoring in event-driven surges.
The Rise of Last-Minute Bookings
Travel behavior is changing—and not in a way that leaves revenue managers much breathing room. According to recent data highlighted by Skift, last-minute bookings have surged, surpassing the long-term average by roughly 5% in March 2025. By June, the U.S. market saw vacation rentals jump 10% year-over-year, while hotels scraped by with a 1% increase. The shift is clear: travelers are holding off until the final days—or even hours—before departure.
For revenue management teams, this isn’t just a quirky consumer trend. It’s a structural shift in demand patterns that changes how pricing data must be collected, interpreted, and acted upon.
1. Shorter Booking Windows = Less Reliable Forecasts
Traditional forecasting models rely heavily on historical booking curves—steady demand ramps leading up to check-in. When those curves compress, forecasting accuracy suffers. Instead of weeks of gradual demand buildup, teams may now see a flood of bookings inside a 72-hour window, forcing faster, more aggressive pricing decisions.
The data challenge here is granularity: to understand and respond to sudden spikes, revenue systems need high-frequency, real-time inputs—not just daily snapshots.
2. Events as Demand Catalysts
Major events—concerts, sports championships, festivals, conventions—are becoming even more impactful in a last-minute booking world. When travelers decide on impulse to attend, bookings can spike sharply just days before arrival.
Revenue managers must integrate event calendars and predictive event impact models into their pricing logic. Detecting an upcoming high-demand event five days out could mean the difference between selling out at optimal rates and leaving revenue on the table.
When paired with historical event data, these models can predict both the timing and magnitude of a booking surge, enabling more precise rate adjustments.
3. Price Sensitivity Is Shifting
In yield management theory, booking lead time is a key segmentation variable—guests who book late are often less price-sensitive, making higher rates viable. But as late booking becomes mainstream, this assumption weakens. More price-sensitive guests are now part of the last-minute crowd, meaning old elasticity models may misprice inventory.
Revenue managers will need to re-test these assumptions, using streaming booking data from multiple sources and competitor rate intelligence to pinpoint the real willingness-to-pay for each segment.
4. Real-Time Data Across Multiple Sources
In a last-minute booking environment, pricing decisions can no longer be made on partial visibility. Relying on just brand.com numbers risks missing competitive rate opportunities or over-discounting. Revenue managers need multi-channel, real-time pricing intelligence that pulls from:
- OTA rate and pickup feeds – to monitor competitor pricing changes and demand velocity on high-volume channels.
- Brand.com data – to track direct booking behavior and evaluate the effectiveness of targeted rate plans.
- GDS booking patterns – to identify late surges from corporate or agency segments willing to pay higher rates.
- Metasearch price scraping – to see exactly how your rates stack up against competitors in real-time searches.
This consolidated pricing picture enables precise, time-sensitive adjustments—such as pushing rates higher when OTA demand spikes, holding rates steady if brand.com pickup is strong, or selectively discounting slow-moving room types. By aligning pricing actions to actual, live demand signals across all channels, teams can maximize ADR without sacrificing occupancy.
5. Open Pricing Outperforms Static Fences
The growing unpredictability of demand rewards flexibility. Open pricing strategies—where rates adjust independently by room type, length of stay, or channel—allow hotels and rentals to respond to small market changes without blunt, across-the-board rate shifts.
For example, a property could raise rates on premium suites for last-minute luxury travelers while discounting standard rooms on a slow night, maximizing yield across segments.
Strategic Recommendations
Revenue management teams can prepare for the last-minute era by:
- Upgrading RMS capabilities to handle high-velocity, multi-source data feeds
- Integrating event impact models for real-time rate calibration
- Re-calibrating elasticity models to reflect new guest behaviors
- Running rapid A/B price tests in compressed booking windows
- Layering in competitor and metasearch intelligence for sharper market positioning
Conclusion: Agility Is the New Advantage
The rise of last-minute bookings doesn’t have to be a threat—it can be an opportunity. Properties that adapt their pricing data strategies to shorter lead times, integrate event-driven insights, and leverage multi-source real-time demand data can outmaneuver competitors who cling to outdated forecasting curves.
In this new landscape, the winners will be those who stop asking “What did demand look like last year?” and start asking “What is demand telling me right now?”
