What “optimization” actually means
In mathematics, optimization means solving a function to find the point of highest expected revenue. That is what distinguishes an optimization model from the industry-standard base-price model: one adjusts prices logically, the other computes the mathematically best price.
The $250 seat or the $115 seat?
Consider a classic revenue-management decision: a seat with a 50% chance of selling at $250, versus a 90% chance at $115. Multiply probability by price and the answer flips intuition.
50% × $250 = $125 · 90% × $115 = $103.50
The first option yields higher expected revenue despite the lower booking probability. An optimization model reasons in exactly these terms; a rules engine does not.
Rules-based models: logical, but not optimal
Base-price models use if-then criteria — if season is X, change price by Y — rather than probability analysis. The adjustments are sensible, but they follow heuristics instead of identifying the highest expected-revenue point.
Why airlines went to rules — and came back
In the mid-2010s, some airlines shifted toward rules-based systems when probability estimates degraded amid low-cost carriers and capacity swings. But the majors kept optimization models with analyst support, and the industry eventually recognized that, set up correctly, optimization models simply make more money.
The single-listing problem
Vacation rentals face a binary booked-or-vacant reality, unlike hotels with many rooms. Historical data is messy — owner stays, maintenance blocks — and with one unit there is no chance to recover from a forecasting error. In that context, a well-built rules model can outperform a poorly configured optimization approach.
What Quibble forecasts instead: shopper choice
Rather than traditional demand forecasting, Quibble estimates the probability that a guest books your listing when it appears on their screen versus competitors. That shopper-choice model yields both revenue-maximizing pricing and competitive insight.
Finding the true maximum
An optimization model tests expected revenue at every possible price point, and the real challenge is finding the global maximum rather than a local one — accounting for how each price change shifts booking probability for both your listing and the comps.