Apple App Store Reviews Scraper

Extract Apple App Store reviews into a clean, structured dataset you can analyze, monitor, and move into downstream workflows.

Actor: https://apify.com/fetchcraftlabs/apple-appstore-reviews-scraper

Last reviewed: April 21, 2026.

Quick answer

Use this actor when you need public iOS review data in a structured export instead of checking App Store pages manually. It fits release monitoring, competitor review analysis, and reporting workflows where review text, ratings, authors, and dates need to be collected at repeatable scale.

At a glance:

• Input: a public Apple App Store URL, plus optional review depth and storefront controls.
• Output: normalized review rows with identifiers, rating, title/text fields, author, and timestamps.
• Best for: iOS review monitoring, post-release analysis, and competitor feedback exports.
• Not ideal for: private analytics, moderation tooling, or workflows that only need a quick manual spot check.

What it does

Give the actor a public App Store URL and it:

• Resolves the app metadata (storefront, id, slug).
• Fetches review batches from the App Store review API.
• Normalizes fields into a consistent dataset format.

Who this is for

This actor is useful when teams need review data they can compare over time instead of isolated screenshots from the store page.

• Product teams: track recurring complaints, regressions, and release feedback.
• Growth teams: compare sentiment across storefronts and campaigns.
• Support and operations: watch for rating shifts after launches or incidents.
• Researchers and agencies: benchmark multiple apps and export review text into analysis pipelines.

Common use cases

• Release monitoring: spot rating drops and recurring complaints after launches.
• Product & competitive research: compare sentiment across competitor apps and regions.
• Analytics & dashboards: track review volume, average rating, and top themes over time.
• Support & operations: forward low-star spikes to triage queues or alerting systems.

What you get

When to use it vs. when not to

Use this actor when:

• You want a repeatable export of public App Store reviews.
• You need enough review volume to compare apps, releases, or storefronts.
• You plan to feed the dataset into analytics, research, or monitoring workflows.

Look for another workflow when:

• You need data that is not publicly exposed by the App Store.
• You only need a few reviews and manual inspection is faster.
• You need engagement, response handling, or CRM actions rather than extraction.

Typical workflow

1. Paste an App Store URL.
2. Choose how many reviews to collect and which storefront (country) to use.
3. Run the actor and export results as JSON/CSV (or ingest directly into your stack).

Input overview

Inputs (high level)

Key inputs you’ll likely use:

• appUrl (required): full App Store URL (e.g. https://apps.apple.com/us/app/facebook/id284882215).
• maxReviews (optional): maximum number of reviews to fetch.
• country (optional): two-letter storefront code (e.g. us), overrides the URL country.
• proxyConfiguration (optional): Apify proxy or custom proxies for reliability at scale.

Example input:

{
  "appUrl": "https://apps.apple.com/us/app/facebook/id284882215",
  "maxReviews": 250,
  "country": "us",
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": ["DATACENTER"]
  }
}

Output shape

Each dataset item is normalized (IDs, rating, text fields, author, timestamp), for example:

{
  "reviewId": "1234567890",
  "appId": "284882215",
  "appName": "facebook",
  "rating": 4,
  "title": "Good overall",
  "text": "Solid experience, but notifications could be better.",
  "author": "User123",
  "date": "2024-08-18T12:34:56Z"
}

What the sample output tells you

The sample output is designed for downstream use rather than just reading on-page:

• reviewId and appId help keep exported rows traceable.
• rating, title, and text support qualitative review analysis.
• author and date make the record usable for trend windows and reporting.
• Normalized fields reduce cleanup before the dataset reaches dashboards or scripts.

Run it via API (JavaScript)

import { ApifyClient } from "apify-client";

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor("fetchcraftlabs/apple-appstore-reviews-scraper").call({
  appUrl: "https://apps.apple.com/us/app/facebook/id284882215",
  maxReviews: 250,
  country: "us",
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items[0]);

Limitations and notes

This page is scoped to the actor's public-facing behavior and example contract.

• The actor works with public App Store review data, not internal App Analytics data.
• Available review volume can vary by app and storefront.
• If pricing or actor capabilities are important to procurement or production planning, re-check the live Apify listing before large runs.
• If a downstream workflow depends on specific fields, validate the schema with a small test export first.

Pricing

Paid per result: $0.50 / 1,000 results.

FAQ

Is this actor useful for competitor monitoring?

Yes. It is a practical fit when you want to compare public review patterns across competing iOS apps or track feedback changes over time.

Can I target a specific storefront?

Yes. Use the optional country input when you want to fetch reviews from a specific storefront rather than relying only on the URL.

What should I validate before production use?

Run a smaller export first and confirm row volume, storefront behavior, and the exact fields your downstream system expects.

Related pages

• Compare the Android counterpart: Playstore Reviews Scraper.
• Browse more actor writeups on /blogs.
• Need implementation help? Use the contact page.

Next steps

• Schedule the actor to refresh review datasets weekly/monthly.
• Connect outputs to Google Sheets, BigQuery, or your BI stack.
• Add alerts for rating dips, review volume spikes, or keyword clusters.