The science team at Pace have spent the last 12 months building an engine to model price sensitivity. And the results are looking very, very promising
What you see above is groundbreaking. It is an analysis of one room-category and check-in night for a Pace customer. Our platform generates forecasted booking curves by price. Such that every price has its own forecasted booking curve.
What is groundbreaking about this is that it gives hotels a map of possible pricing patterns. As an illustration, for optimal results you might need to follow a high price early on in the curve, then switch to a lower price as you approach check-in.
In short, you can understand exactly where and how to focus on ADR versus occupancy.
As you can see the model is still quite crude - price sensitivity is modelled as linear with price and constant with time. Improvements to the models are underway.
Of course, we don’t expect anybody to use charts like this in their daily work. That’s why we’re also working on a redesign of our app to present these findings in clear and actionable ways.
More on this very soon.
Meanwhile you might find this podcast on price sensitivity interesting: