Public App Store search data gives you four honest signals: which apps rank for a keyword, how many apps compete for it, how strong the top results are, and the keyword's result metadata. True search volume exists only inside Apple's paid Search Ads API. Every free tool that shows you a “popularity” number is estimating — the question is whether it admits it.
Why keyword data is split into two worlds
When you search the App Store, Apple runs a private ranking system over a private index. Two facts about that system are public: the results it returns, and which keywords your app appears for (visible to you in App Store Connect). Everything else — how many people search a term, how impressions convert, what competitors spend — is Apple's business data.
The consequence is simple: there is no free source of App Store search volume. Paid ASO platforms license volume from Apple Search Ads data partnerships or model it from panels. Tools that claim to show “searches per month” without such a source are showing a model — sometimes a good one, sometimes a guess wearing a number.
The public data map
| Signal | Public? | Where it lives | What it tells you |
|---|---|---|---|
| Top apps for a keyword | Yes | iTunes Search API / app store search | Who you would compete with |
| Competing app count | Partly | iTunes Search API (capped at 200) | How crowded the term is |
| Ratings volume of top apps | Yes | iTunes Search API / product pages | How established the incumbents are |
| App Store suggestions | Yes | iTunes autocomplete (search box) | What Apple considers related |
| Search volume / impressions | No | Apple Search Ads API (paid) | True demand for the term |
| Your keyword ranks | Yes, for your app | App Store Connect | Where your app actually appears |
How honest estimates are built
Without volume data, you can still rank keywords usefully — if you are transparent about what each number means. AppClimb's approach derives two estimates from the public result set:
- Popularity (estimated demand) — how much competition pressure and top-result strength suggest the term is searched. A saturated result list with strong incumbents implies an active term.
- Difficulty (barrier to rank) — how hard it looks to reach the top results: how many apps compete, how many ratings the incumbents hold, and whether mega-brands dominate the first page.
These are directional, not oracle numbers. Two keywords with the same score can behave differently across countries and seasons. That is why AppClimb labels every score as an estimate and shows the underlying evidence (result count, top apps, ratings) next to the score instead of hiding it behind a single mysterious number.
What to never trust
- Precise monthly search volume without a stated source. No public API exposes it.
- Trends with no history. A 30-day chart needs 30 days of observations or an openly labeled baseline.
- Difficulty scores with no evidence.“72” means nothing unless you can see why.
If a tool won't tell you where its keyword numbers come from, assume they are modeled. If it tells you, you can judge whether the model is sane. Estimates are fine — secrecy is the problem.
The smallest useful workflow
- Search a candidate term and record popularity, difficulty, and result count.
- Open the top apps: are the incumbents relevant to your product, and how strong are their ratings?
- Prefer terms where difficulty is below the average of your niche and popularity is not near-zero.
- Track the same terms daily so the trend becomes real data instead of a one-day snapshot.
- Re-check after a metadata or release update and keep the history.
Where AppClimb fits
AppClimb is a keyword explorer built on public data. It shows official Apple Ads popularity and estimated difficulty with the evidence behind them, stores one daily snapshot per keyword in your browser, and labels estimated history as estimated. The free plan has honest daily limits; Pro ($8/month) adds cloud sync — and no plan ever invents volumes.
Open the keyword explorer , or read the guide to App Store keyword research.