What difficulty actually measures
App Store keyword difficulty is an estimate, on a 0 to 100 scale, of how hard it would be to rank well for a search phrase. It is not an Apple metric. Apple publishes no difficulty score, and every number you have seen is a tool’s own formula computed over public evidence from the store.
What it summarises is the strength of the result page: how established the apps ranking for the phrase are, how deliberately they target it, and how crowded the field is. That is genuinely useful information. It is just answering a question about the page rather than a question about you.
How it is calculated
Implementations vary, but almost all of them read the same three things from the top of the result page:
- Authority of the ranking apps. Rating counts, usually on a log scale, because the difference between 500 and 5,000 ratings matters far more than the difference between 200,000 and 205,000.
- Title optimization. How many of the top results carry the phrase, or its words, in the app name — weighted towards the top positions. An app with the exact phrase in its title is deliberately defending it.
- Saturation. How many apps the store considers relevant to the phrase at all.
Blend those, scale to 0-100, and you have a difficulty score. Because the weightings are each tool’s own choice, scores are not comparable between tools — a 62 in one is not a 62 in another. Use one tool’s numbers against each other, never across tools.
What a difficulty score cannot tell you
Difficulty averages the page. Averages hide exactly the thing a new app needs to find: unevenness.
Two result pages can both score 70. On the first, ten apps each hold 40,000 ratings and eight carry the exact phrase in their title. On the second, six apps hold over 100,000 ratings and four hold fewer than 2,000, nobody uses the phrase in a title, and the order changes weekly.
The first page is closed. The second has four soft slots and no one defending them. A single difficulty number cannot separate these, and the difference decides whether your launch works.
Specifically, a difficulty score is blind to:
- Where the softness is. Three weak apps in the top ten is an entry route; the average hides them.
- Positions 11 to 30. Entering the top twenty is a realistic first goal, and the tail is often far weaker than the head.
- Whether anyone is defending the phrase. A term nobody has put in a title is being won incidentally, and can be taken deliberately.
- Age and neglect. An app that has not shipped an update in two years is holding its slot on history alone.
- Whether the page moves at all. Some result pages have been static for months; others turn over weekly. A page that already lets new apps in is telling you something no snapshot can.
- Your own starting position. The same page is a different proposition for an app with 50,000 ratings and an app with none.
The better question: can a new app get in?
The question worth asking is narrower and more useful: could an app launched today, with no ratings and no brand, realistically enter the top ten or twenty for this phrase?
That is answerable from the same public evidence, read differently. Instead of averaging the page, you look for the seam in it:
- How many of the top ten are soft — under a couple of thousand ratings, holding position on relevance rather than reputation.
- How strong the tail is at positions 11 to 30, since that is the realistic first target.
- How many incumbents use the exact phrase in a title. None means nobody is defending it.
- How recently the top apps launched and were last updated. Recent arrivals prove new apps can rank; abandoned apps are weaker than their rating counts suggest.
- How much the page changes over time, measured across repeated captures rather than guessed from one.
- Whether paid apps hold the top slots, which leaves room underneath for a free entrant.
- Difficulty
- 84
- New-app rankability
- 25
- Soft slots in top 10
- 1
- Exact phrase in title
- 8 of 10
Hard, and hard in the way that matters. Both readings agree: this is a destination, not a launch target.
- Difficulty
- 54
- New-app rankability
- 70
- Soft slots in top 10
- 6
- Exact phrase in title
- 0 of 10
A middling difficulty score would put this in the same bucket as dozens of other terms. The second reading says something specific: six of the ten apps ranking are small, nobody has claimed the phrase in a title, and a focused new app has a real entry path.
Reading a result page by hand
You can do all of this manually, and it is worth doing once per candidate phrase so the numbers stop being abstract. Search the phrase in the store you care about, then note:
- How many of the top ten have fewer than about 2,000 ratings.
- How many carry the exact phrase in their app name.
- Roughly when the top few were last updated.
- What sits at positions 11 to 30 — is the tail as strong as the head?
- Whether the results actually answer the search, or answer something adjacent.
That last one is worth dwelling on. When people search something specific and the store returns generic apps from a neighbouring category, the mismatch is an opening — and it is a much stronger signal than low competition, because it means demand exists and is going unserved.
Thresholds worth actually using
Treat these as a starting frame rather than a rule, and always alongside demand evidence — a winnable keyword nobody searches is still worth nothing.
| Difficulty | Rankability | What it means for you |
|---|---|---|
| 0-55 | 62+ | Launch target. Winnable with metadata alone while the app is unknown. Spend your title and subtitle here. |
| 0-70 | 40+ | Growth target. Reachable once ratings build. Put these in the keyword field now and revisit after traction. |
| 70+ | Any | Destination. Worth naming so the roadmap points at it. Not worth spending launch metadata on. |
| Any | Under 40 | Skip for now regardless of difficulty — the page is held by apps you cannot displace yet. |
Sequencing keywords this way — what you can win now, what opens up next, what you are aiming at — turns a flat keyword list into a plan. The keyword research guide covers how to build the list in the first place, and GetRevuu computes both readings for every phrase automatically, including the capture history that makes page movement measurable.