App Store Rank & Rating Scraper

Track where your app ranks for the keywords you care about, and get told when new ratings turn against you — every morning, without opening a dashboard.

Apple's public endpoints only. No HTML scraping, no private APIs, no personal data.

Why this exists

Ranks cannot be back-filled. Apple does not publish yesterday's search results. If you did not record your position on a given day, that day is gone forever. This Actor records it and keeps the series, so three months from now you can answer "did that ASO change actually do anything?"

That is the whole product. Everything else is plumbing.

What you get

Run it daily on a schedule. Each run appends to your dataset:

rank rows — one per tracked app per keyword per storefront.

field	meaning
rank	position in Apple's results, or null if outside searchLimit
previousRank	where it was last run — null on the first run
rankChange	positive means it moved up
outOfRange	true when a tracked app fell out of the searched depth
isNewToKeyword	first time this app appears for this keyword

reviewSummary rows — one per tracked app per storefront.

field	meaning
newRatings	how many ratings arrived since the last run
newRatingsAverage	the average star rating of just those new ones
newRatingsErrorBound	how far off that figure could be — always reported
newRatingsReliable	false when the error bound is too wide to act on
ratingDrop	true when the new ratings are at least a full star below the app's standing average, even after allowing for the error
versionChangedSinceLastRun	you shipped since the last run — the context for any drop
averageRating, ratingCount	Apple's current aggregates

review rows — individual new reviews, when review text is available (see below).

error rows — anything that failed, in the dataset where you will actually see it.

Reading the ratings, not the reviews

Most people who rate an app never write a word. A tool that scrapes review text is blind to all of them.

This Actor works from Apple's aggregate figures instead. Two observations are enough to recover what happened in between: total ratings × average is a running sum, so the difference between two runs divided by the number of new ratings gives the average of just the new ones. Twenty ratings at 1.4 stars is a bad night, and you see it whether or not anyone wrote a review.

Apple rounds the average it reports, so the recovered figure carries a margin. That margin widens as the app gets larger and the number of new ratings gets smaller — on an app with 700,000 ratings, five new ones tell you nothing. The margin is always reported, newRatingsReliable says whether it is narrow enough to act on, and an alert only fires when the drop survives the margin. A number without its error is a number that will eventually lie to you.

About review text

Review text usually comes through, but not always. Apple sometimes answers the customer-review feed with a valid, correctly-formed, completely empty response — indistinguishable from "this app has no reviews" unless you look closely. Measured from Apify: it failed on two consecutive runs one hour, then succeeded five out of five the next.

This Actor tells the two apart. Every reviewSummary row carries reviewTextStatus: ok when the feed answered, blocked when it came back empty. A blocked feed does not fail the run and does not affect ranks, ratings, averages, or drop alerts — those come from a different endpoint that has never gone quiet.

That is the reason the rating maths above exists. A feature that depends on someone else's goodwill needs a floor underneath it.

Three things it gets right

It respects Apple's rate limit, across runs. Apple documents 20 calls per minute. The Actor uses a token bucket that persists between runs, so two overlapping schedules do not stack up and get you throttled. Default is 18/min to leave headroom.

It fails loudly instead of quietly. Apple returns HTTP 200 with an empty feed in at least two situations that look exactly like "no data": when page= is missing from a review URL, and when the request comes from a datacenter IP. The Actor asserts the response shape, writes an error row into the dataset, and marks the run failed if a whole requested feature produced nothing. A green run with an empty dataset is the worst possible outcome for something you check once a month.

A tracked app that disappears still produces a row. If your app drops out of the top 200 for a keyword, you get a row with outOfRange: true — not silence. Silence is indistinguishable from a broken run.

Every derived number carries its error. See "Reading the ratings, not the reviews" above.

Input

{
  "keywords": ["habit tracker", "focus timer"],
  "appIds": ["1438388363"],
  "countries": ["us", "gb"],
  "trackReviews": true,
  "reviewPages": 2,
  "searchLimit": 200,
  "maxCallsPerMinute": 18
}

App IDs are the number in the App Store URL: apps.apple.com/us/app/x/id1438388363 → 1438388363.

Leave appIds empty to record the top 20 for each keyword instead of specific apps.

Sizing a run

Calls per run = keywords × countries + appIds × countries × (reviewPages + 1).

At 18 calls/minute, 10 keywords and 3 apps across 2 storefronts is about 2.5 minutes.

What you pay for

Pay per event. You are charged for observations that produced a result:

event	when
Actor start	once per run
Rank check	one keyword, one storefront, rank recorded
Rating check	one app, one storefront, ratings recorded

A failed observation is never charged. If Apple times out, or an app ID does not exist in that storefront, you get an error row and no charge for it. You are paying for data, not for attempts.

Ten keywords and three apps in one storefront, run daily, is 30 starts + 300 rank checks + 90 rating checks a month.

Limits worth knowing

• Search results are cached by Apple for about 24 hours per keyword and storefront. Running more than once a day returns the same ranking. This is a daily instrument.
• Search depth caps at 200. Below that, an app is reported as out of range rather than ranked.
• Reviews cap at 10 pages (500) per app per storefront, newest first.
• The rate-limit bucket is shared through a key-value store. Two runs starting at the same instant can each read the same balance, so the default leaves headroom rather than spending all 20.

On data and privacy

Reviewer names are never fetched or stored. They are not needed to decide anything about an app, and holding them would mean holding personal data.

Review titles and bodies, when available, pass through to your dataset but are never written to the Actor's own history. The persistent store holds numbers only: ranks, rating counts, and averages.

Icons and screenshots are not redistributed.

Storage

History lives in a named key-value store, aso-history, so it survives between runs. Deleting that store resets the baselines — ranks start over at previousRank: null, and spike detection goes quiet until it has enough samples again.

Running locally

npm install
npm test          # unit tests, no network
npm run smoke     # end-to-end against Apple, twice, to prove the diff works

For AI agents

This Actor works well as an agent tool: the input schema is small and fully described, every run returns structured rows, and failures come back as data rather than silent gaps. Use it when you need to:

• track App Store keyword rankings for an app over time
• monitor iOS app rating changes and detect rating drops
• get the average rating of new App Store reviews since a previous run

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