Test pricing, amenities, and policies to maximize guest value.
Hospitality teams make decisions about pricing, promotions, and guest experience that directly affect RevPAR, satisfaction scores, and repeat bookings. Decision Process helps you run controlled experiments across properties, room types, and booking windows — with results you can trust.
Experiment Templates
Ready-to-run experiments
Dynamic vs. Flat Room Pricing
“Demand-based dynamic pricing increases RevPAR vs. flat weekly rates.”
Conditions
- Flat weekly rates (control)
- Dynamic pricing
Metrics
- RevPAR (USD)
- Occupancy Rate (%)
- Average Daily Rate (USD)
Welcome Amenity Comparison
“A food & beverage amenity drives higher satisfaction than a room upgrade offer.”
Conditions
- No amenity (control)
- F&B credit
- Room upgrade offer
Metrics
- Guest Satisfaction (1–10)
- Review Score (stars)
- Upsell Rate (%)
Cancellation Policy Flexibility Test
“Flexible cancellation policy increases bookings without materially raising cancellations.”
Conditions
- 48hr cancellation (control)
- 24hr cancellation
Metrics
- Occupancy Rate (%)
- Cancellation Rate (%)
- Repeat Guest Rate (%)
Worked Example
Dynamic vs. flat weekly pricing across 6 hotel properties
A hotel group randomly assigns 6 comparable properties to either flat weekly rates (control) or demand-based dynamic pricing. Measured over 12 weeks covering peak and off-peak periods. Primary metrics: RevPAR and occupancy rate.
Results: RevPAR (USD)
Flat weekly rates (control)
mean: $112.40
95% CI: $104.20–$120.60
Dynamic pricing
mean: $131.80
95% CI: $122.90–$140.70
P(better) = 96%
Dynamic pricing increases RevPAR by +$19.40 / night (d = +0.68, large effect) at 96% posterior probability. Occupancy rate held steady — the lift comes from rate optimization, not volume. Recommendation: roll out dynamic pricing engine to all properties ahead of peak season.
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