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Apple App Store Reviews Scraper: Export iOS reviews into clean data

Collect Apple App Store reviews at scale (ratings, titles, text, author, date, app version) with the FetchCraft Labs Apify actor.

Jan 20262 min readBy FetchCraft Labs
Apple App Store reviews scraperiOS app reviewsApify actorAppstore Reviews Scraperreview scrapingproduct feedback automation

Apple App Store Reviews Scraper

Extract Apple App Store reviews into a clean, structured dataset you can analyze, monitor, or pipe into your workflows.

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

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.

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.

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).

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"
}

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]);

Pricing

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

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.