Southwest Hotel Group · Gaming & Hospitality · Las Vegas, NV · 14 weeks
Dynamic pricing AI deployed across 12 properties
Southwest Hotel Group relied on manual rate setting, using weekly competitor reports and past occupancy data. We developed and implemented a dynamic pricing AI for 12 properties that adjusted rates in real time. This system increased revenue by $3.8 million in the first year.
Challenge
The revenue management team relied on weekly competitor rate reports and 90-day occupancy data to set prices manually. This approach missed same-day demand shifts from local events, weather changes, and competitor moves. As a result, they were losing an estimated $3M to $5M in annual revenue from poor pricing decisions.
Approach
We created a pricing model that pulled real-time data from competitor rates, local events, weather, and booking speed. It updated rate recommendations every four hours for each property and room type. We also developed a dashboard displaying these recommendations with clear explanations, enabling the team to accept or adjust prices instantly.
Outcome
We rolled out dynamic pricing across all 12 properties by week 14. Over the first year, this approach added $3.8 million in revenue compared to the previous manual pricing method. Meanwhile, the revenue management team’s override rate fell from 40 of every 100 in the first month to 12 of every 100 by month six, reflecting growing confidence in the model’s recommendations.
We spotted real-time signals that manual tracking completely missed.
Alright, here’s what was going down. We used to rely on these slow weekly competitor reports and old data to set prices. That meant we were always a step behind. Like when a Taylor Swift concert got announced just three days out, or when a competitor dropped their prices by $18 out of nowhere in the afternoon, we had zero clue until it was too late. So, we built an AI that watches all this stuff in real time and adjusts prices instantly. No waiting around. Just quick moves that keep us ahead.
How We Got More People Using the Tool by Making Insights Super Clear
The revenue team wasn’t sold on this black-box pricing model at first. So, we built a simple dashboard that showed the recommended price plus the top three reasons why. Once they could actually see what was behind the numbers, trust started to build. The payoff? Price overrides fell from 40 of every 100 down to 12 of every 100 in just six months.
How We Spotted Model Slip-Ups and Boosted Accuracy by Adding Overrides
Every time we overrode the model, we retrained it right away. If that override improved things, the model gave more weight to that choice. If it made things worse, the model pulled back fast. Over a year, this back-and-forth pushed accuracy up by six more correct picks per 100 cases.
Results
- $3.8M Incremental revenue in first year
- 12 Properties on dynamic pricing
- 4 hrs Rate optimization cycle
- 12 of every 100 pricing decisions Override rate at month 6
We knew manual pricing was holding us back, but we didn’t expect it to cost $3.8 million in the first year. Now, the model pays for itself every month.