High-demand events (festivals, holidays, sports) create compression nights where demand outruns supply. That's when dynamic pricing is essential—but only if you have clear rules: guardrails, step sizes, and stay controls.
Helpful glossary terms
- MLOS, CTA, CTD: stay controls for peaks
- Rate Fences and BAR: protect pricing logic
1) Estimate the demand shape before touching rates
An event isn't just a "strong date". The key is how demand will build:
- Does demand start early (long booking window) or late?
- Is the peak one night or a 2–3 night run?
- Is demand mostly OTA or direct?
Use last year as a reference if you have it; otherwise use competitor context plus internal signals. In Sigma Revenue, you can view this quickly in Calendar View and Competitor Data.
2) Set guardrails: min, max, and step size
Event chaos usually comes from emotional decisions. A simple framework keeps you consistent:
- Min and max by room type/season;
- Step size (small, consistent changes);
- Review cadence (every 2–3 days, then more frequently).
For the basics, start with Min/Max rates and guardrails.
3) Use stay controls to protect peaks (and prevent short stays)
On compression nights, price is only half the strategy. Stay controls can have a bigger impact:
- MLOS: keep inventory for 2+ night stays.
- CTA: block new arrivals on the most valuable night while allowing through-stays.
- CTD: avoid departures on a high-pressure date.
These controls pair well with dynamic pricing because they shape demand, not just the rate.
4) Manage channels: don’t fill your best nights with your most expensive channel
The biggest peak-night risk is letting OTA inventory fill up early, before direct demand arrives. Practical rules:
- Protect direct availability and use rate fences instead of blanket discounts.
- Monitor rate parity to avoid channel chaos.
- If needed, restrict OTA availability instead of pushing price down.
If your goal is a healthier mix, see how to reduce OTA dependence.
5) Execute with data, then do a quick post-mortem
The best teams do two things: disciplined execution and a short review after the event.
- When did pickup accelerate?
- Which rate step delivered the best result?
- Did stay controls reduce unwanted short stays?
- How did your channel mix shift?
Sigma Revenue combines Real-time Data with Rate Recommendations and automation via Autopilot (within your guardrails).