The Better App Store Reviews Scraper — Apple App Store customer reviews & ratings export

Scrape Apple App Store customer reviews quickly and affordably. This App Store reviews scraper pulls user reviews, star ratings, review titles, review text, app version and review dates for any iOS / iPadOS / macOS app, in any App Store country storefront, and exports them to JSON, CSV, or Excel. It reads Apple's public reviews RSS feed directly — so there is no headless browser, no residential proxy, and very low compute usage, which makes it one of the more affordable ways to monitor App Store ratings and reviews at scale.

Great for app review monitoring, review sentiment analysis, competitor research, ASO (App Store Optimization), customer feedback tracking, and support triage — without paying for expensive browser rendering.

What it extracts

For every customer review the scraper returns:

• reviewId — Apple's unique review identifier
• userName — the reviewer's display name
• title — the review headline
• text — the full review body
• rating — the star rating as an integer from 1 to 5
• appVersion — the app version the review was written against
• updatedAt — the review date, normalised to UTC ISO-8601
• reviewUrl — the reviewer/review URL when Apple provides one
• appId, country, sort, page — the query context for each row

Features

• App Store reviews export to JSON, CSV, Excel, HTML, or via API/webhook (Apify dataset).
• Any storefront — set a two-letter country code (us, gb, de, fr, jp, br, …) to collect localized reviews.
• Most recent or most helpful sort order.
• Star-rating extraction as clean integers, ready for averages, distributions, and dashboards.
• Automatic pagination across Apple's review pages (up to ~10).
• Cross-page deduplication by review id.
• HTTP-only and lightweight — fast runs and low compute-unit cost.
• Resilient parsing that handles single-review feeds and empty feeds gracefully.

Input

Field	Type	Default	Description
appId	string (required)	—	Numeric Apple App Store app id (digits only). Find it in the app URL, e.g. .../id389801252 → 389801252.
country	string	us	Two-letter App Store storefront code (ISO 3166-1 alpha-2), e.g. us, gb, de, jp.
maxPages	integer	5	Number of review pages to fetch (Apple caps at ~10, ~50 reviews per page). Fewer pages = cheaper run.
sort	string (mostrecent | mosthelpful)	mostrecent	Order reviews by most recent or most helpful.
proxyConfiguration	object	{ "useApifyProxy": true }	Optional proxy. Datacenter proxies are plenty; residential proxies are not required. You can also run with no proxy.

Example input

{
  "appId": "389801252",
  "country": "us",
  "maxPages": 3,
  "sort": "mostrecent",
  "proxyConfiguration": { "useApifyProxy": true }
}

Output

Each dataset item is one customer review:

{
  "reviewId": "11111111111",
  "userName": "Jane D.",
  "title": "Absolutely love it",
  "text": "Fast, reliable, and easy to use. Best update yet!",
  "rating": 5,
  "appVersion": "12.4.1",
  "updatedAt": "2026-07-20T16:15:32.000Z",
  "reviewUrl": "https://itunes.apple.com/us/reviews/id123456789",
  "appId": "389801252",
  "country": "us",
  "sort": "mostrecent",
  "page": 1
}

Example use cases

• App review monitoring — track new 1-star and 5-star App Store reviews for your own app daily.
• Review sentiment analysis — feed review text and ratings into an NLP / LLM pipeline to quantify customer sentiment over time.
• Competitor & market research — collect and compare App Store ratings and reviews across competing iOS apps.
• ASO (App Store Optimization) — mine reviews for recurring keywords, feature requests, and pain points.
• Customer support triage — surface crash reports and bug complaints tied to a specific appVersion.
• Release impact analysis — compare rating distributions before and after an app update.

How it works

The actor requests Apple's public customer-reviews RSS-JSON endpoint:

https://itunes.apple.com/{country}/rss/customerreviews/page={p}/id={appId}/sortby={mostrecent|mosthelpful}/json

for each page from 1 to maxPages, using a plain HTTP crawler (HttpCrawler). The first entry in every feed is app metadata, so it is skipped; the remaining entries are parsed into normalised review objects by a small, pure parser (src/parse.mjs) and pushed to the dataset. Because the parser is dependency-free, it is fully unit-tested with npm test (no network required).

Why it's efficient and affordable

• HTTP-only, no browser. It never launches Chromium/Playwright, so it avoids the heavy CPU and memory that browser-based scrapers consume — meaning fewer compute units per run.
• No residential proxy needed. The Apple RSS feed is public; datacenter proxies (or none) are sufficient, avoiding the biggest cost driver in most scrapers.
• Small, bounded workload. Apple limits reviews to ~10 pages, so runs are fast, predictable, and cheap.
• Lean data transfer. It downloads compact JSON, not rendered HTML, images, fonts, or stylesheets.

FAQ

Q: Where do I find an app's appId? Open the app's App Store page and copy the number after id in the URL, e.g. https://apps.apple.com/us/app/example/id389801252 → 389801252.

Q: How many reviews can I get per app? Apple's public reviews feed exposes roughly the 10 most recent/helpful pages (about 500 reviews). This is an Apple limitation, not an actor limitation. Use both mostrecent and mosthelpful, and different country storefronts, to widen coverage.

Q: Can I scrape reviews from other countries / languages? Yes — set country to any two-letter storefront code (gb, de, fr, jp, br, …) to collect localized App Store reviews.

Q: Does this scrape Google Play reviews too? No — this actor is focused on Apple App Store reviews via Apple's RSS feed. That focus is what keeps it fast and cheap.

Q: Do I need a proxy or an API key? No API key is required. A proxy is optional; datacenter proxies are more than enough and residential proxies are unnecessary.

Legal & ethics

This actor collects publicly available App Store review data from Apple's official public RSS feed. Use it responsibly: respect Apple's Terms of Service and robots.txt, avoid excessive request rates, and comply with applicable laws (including data-protection rules such as GDPR/CCPA) when storing or processing review data. Do not use scraped personal data — such as reviewer display names — in ways that infringe on individuals' rights. You are responsible for how you use the data you collect.