Revenue management, dynamic pricing, and price optimization can seem like different names for the same thing. They’re not — and if you’re investing in something that controls your price, the distinctions are worth knowing.
A bit of history
I first heard “dynamic pricing” in the mid-2010s as an airline analyst. Airlines have good forecasting and optimization models but archaic reservation and distribution systems that rely on infrequently updated historical data — so “dynamic” was largely about overcoming stale forecasts and stale distribution. Per Google Trends the term really took off around 2022, and other industries now use it to solve their own problems.
Dynamic pricing
“Dynamic” refers to the frequency of change — a price that updates often. What counts as frequent is subjective: airlines distribute prices slowly, while in STR you can push updates to almost any channel as often as you like. Crucially, dynamic pricing says nothing about the model or rules behind the change; it only tells you the price moves frequently.
Dynamic pricing does not explain what is happening behind the scenes causing the price to update; it just describes that the price does change frequently.
Optimized pricing
Like AI, “optimization” has two uses: the colloquial/marketing one, and mathematical optimization — a specific process that finds the revenue-maximizing price. When revenue managers say optimization, they should mean the mathematical kind. It describes HOW the price is set, and on its own it doesn’t have to be dynamic — that’s about frequency.
Optimization models on their own do not have to be dynamic; that is based on frequency.
Does changing prices increase revenue?
Every price change moves either toward a better price or away from one, so changing frequently only helps if the change is toward a better price. That’s why the pricing model matters far more than how often it updates — a really good model may not need to be as dynamic.
The best case: dynamic and optimized
The ideal is the practice of revenue management, using a mathematical-optimization model, updated dynamically — and Quibble does all three. When you invest in pricing software, focus more on how prices are set than how often they change. In short: revenue management varies price to grow revenue; dynamic pricing changes price frequently; optimization is the model that finds the optimal price.
Revenue management, not dynamic pricing
Dynamic pricing changes price over time by rules and signals such as season, day of week, lead time and occupancy; it manages price. Revenue management sets each night’s price to maximize expected revenue, which is price × probability of booking at that price. Price is the output of the optimization. That is why Quibble is a revenue management platform rather than a dynamic pricing tool.
Setting a price, then updating it
With complete information, pricing is simple: evaluate every possible price, calculate the revenue outcome of each and pick the maximum. Nobody has complete information, so firms estimate outcomes and hope the projections match real consumer behavior. Models range from a small owner’s mental math to systems with thousands of lines of code, and all of them need data.
Once a price is set, you collect data on how it performs, look for factors you missed and add model complexity. New data or a model change means recalculating, which usually produces a better price. “Dynamic” only describes how often that happens, and it is bounded by technical and business constraints: a monthly supermarket shelf price can count as dynamic, while a rideshare app updates by the minute.
If you do run a dynamic setup
- Understand your costs: fixed and variable costs belong in the floor, so rates cover expenses and do not just chase demand.
- Read the market, not just the neighbors: peak seasons, local events and demand drivers shape the year, but matching a neighbor’s drop is how a market spirals downward.
- Ask which model sets the price. A tool that explains its price is worth more than one that updates often.
- Large operators run dedicated teams for data, modeling and distribution. Smaller operators get the same capability through software, so the buying question is the same: how is the price set?
Go deeper: How optimization models work, Why dynamic pricing breaks in STR, The case against last-minute discounting.