There are several ways to grow listing revenue: more marketing for demand, wider distribution for exposure, or revenue management to find the price that best matches supply and demand. Revenue management is attractive because it should raise revenue far more than it raises cost — the technical cost of pushing new rates through a PMS API is low. What you actually pay for is the model that makes the pricing decision and the person managing it.
So when selecting a system, it’s critical to know the underlying pricing model. All pricing models are not the same, and the better one you choose, the fewer manual changes your revenue manager makes and the more revenue is generated.
The two types of model
Rules-based models can be described with if/then statements — “if the average market price drops on day X, drop mine by the same amount,” or “if no bookings come in over five days, drop price 5%.” They replicate what you’d do by hand, at far greater scale. Price-optimization models (also called probability-based models) instead do more complex calculations to solve for a theoretically optimal price, in two steps: a forecast step and an optimization step.
Rules-based models
These have been on the market longest and are what most property managers know. The user sets an average “base price,” and the model moves it up or down based on competitor prices in the area. Additional rules layer on — discounting schedules that cut price as the inventory’s spoil date nears, and minimum-stay strategies configured the same way.
Optimization models
New to the STR industry and less familiar to operators, these solve for the best price rather than adjusting a base. The process of solving for the revenue-maximizing price is called optimization — the same approach airlines and advanced hotel operators use.
Gains from each model
Some gains come before any model: setting prices manually by seasonality and day of week already moves RevPAR. A base-price model then automates many of the changes you used to make by hand, and stays competitive as the market moves. Optimization is the next step up — it ingests far more information to make more nuanced, more accurate decisions, and it eliminates the need to set base prices at all.
Why every model is limited
No pricing model is perfect or ever will be — ultimately it’s trying to predict future human behavior, which even humans do poorly. Manual pricing is limited by time; a base-price model is limited by the human-set base it builds every future date on; an optimization model is limited by the data it can ingest and how well it’s tuned.
What’s right for your business?
For the highest revenue potential, the science of optimization wins. But there’s a cost in time or money to running any model, and if you already run a base-price model the switch is a judgment call. As a rough guide by portfolio size: the more properties you manage, the more automation helps — and past roughly 50 units, science-based optimization becomes a necessity.