The short answer
Apple does not publish App Store search volume. There is no equivalent of the monthly search figures available for web search, no public API that returns one, and no official number of searches for any phrase.
This is not a gap the industry talks about clearly, because the tools built on top of the App Store have to present something. So they present estimates, and the estimates look like measurements. A number rendered to two significant figures in a clean table reads as fact whether or not anything measured it.
The practical test. Put the same keyword into several ASO tools. You will get several different popularity or volume figures. None of them are lying — they are different models over the same public evidence, and the spread between them is a fair picture of how much uncertainty is really in the number.
What Apple does publish: Search Ads popularity
Inside Apple Search Ads, Apple shows a popularity score from 5 to 100 for keywords. This is a real Apple figure and the closest thing to volume that officially exists. Three things to understand before leaning on it:
- It is relative, not absolute. A 70 tells you a term is searched considerably more than a 40. It does not tell you how many people searched it, so you cannot forecast installs from it directly.
- The scale is compressed. It behaves logarithmically. The distance from 60 to 70 represents far more searching than the distance from 20 to 30, which makes naive comparisons misleading.
- Coverage is partial. You need an advertising account, and you see the terms Apple surfaces. Long-tail phrases — often exactly the ones a new app can win — frequently return nothing.
It is genuinely useful, particularly for comparing head terms against each other. It is not the complete demand picture people assume, and it is not available for most of the specific phrases that matter to a launch.
Why every tool gives you a different number
Every ASO tool is working from the same small set of public inputs: Apple’s autocomplete, the contents of result pages, how apps move across them over time, and Search Ads popularity where the tool has access. From those, each builds a model, and the model produces a number.
The disagreements are not errors. They are the honest consequence of estimating an unpublished quantity. The problem is not that estimates exist — it is that they are usually presented without any indication of how much evidence sits behind them, so a well-supported figure and a wild guess look identical in the interface.
That is the failure worth guarding against. A demand figure for a phrase Apple autocompletes, returning 190 apps whose titles clearly match, is a reasonable inference. The same figure for a phrase Apple has never suggested, returning a handful of loosely related apps, is a number with nothing underneath it. Both may render as “Popularity: 34”.
What to read instead of a volume number
You can make good keyword decisions without a volume figure. What you need is evidence, weighted by how much of it there is.
| Evidence | What it tells you | Strength |
|---|---|---|
| Apple autocomplete | Apple suggests the exact phrase | Strong — the store has no reason to suggest phrases nobody types |
| Result page size | Hundreds of apps considered relevant | Moderate — indicates a real category, not that people search this phrasing |
| Result relevance | Top results genuinely match the phrase | Moderate — apps have deliberately targeted it, so someone believes in the demand |
| Title defence | Competitors put the phrase in their app name | Strong — a company spent scarce characters on it |
| Page movement over time | Rankings shuffle across repeated captures | Strong — a static page suggests little competitive pressure behind the term |
| Search Ads popularity | Apple’s own relative score | Strong where available, absent for most long tails |
A phrase supported by four of these is a phrase you can act on. A phrase supported by none is a hypothesis, however confident the number next to it looks.
Using autocomplete properly
Autocomplete is the most underused free signal in ASO, and it rewards a little technique:
- Type partial phrases, not complete ones. Entering a full phrase mostly returns that phrase. Entering the first two words returns the completions people actually search, which is where the specific long tails come from.
- Order carries information. Suggestions are not alphabetical. What comes first is generally what is searched most.
- Check each storefront separately. Suggestions are localized and are not translations of each other. The German store will suggest phrases no amount of translating the US list would produce.
- Silence is a finding. If a phrase produces no suggestions anywhere, that is real evidence, and it should override a confident-looking volume estimate rather than the other way round.
Judging a number someone shows you
Whatever tool you use — including ours — the questions are the same:
- What is this derived from? If the tool cannot tell you, that is your answer.
- Is it a measurement or an estimate? For App Store search volume it is always an estimate. A tool that implies otherwise is telling you something about itself.
- How much evidence sits behind this specific keyword? Not the methodology in general — this row. Well-supported and unsupported keywords should not look identical.
- How old is the reading? Result pages change. A number captured months ago describes a page that may no longer exist.
- Does it agree with what you can see yourself? Search the phrase. If the tool says high demand and the store returns twelve loosely related apps, believe the store.
This is why GetRevuu shows a confidence score and the itemised evidence beside every keyword rather than a single popularity figure — including saying plainly when the evidence is too thin to support any reading at all. You can see how that feeds keyword selection in the keyword research guide, and how it separates from competition in keyword difficulty.