Apple App Store Review & Competitor Change Monitor

What does Apple App Store Review & Competitor Change Monitor do?

Track public Apple App Store reviews and listing changes across apps and storefronts. This stateful App Store API monitor alerts you to newly observed reviews, edited reviews, versions, prices, ratings, descriptions, and screenshots, with before-and-after data for scheduled ASO and competitor monitoring.

Unlike a one-time App Store reviews API scraper, this Actor remembers each app and storefront between runs and writes Dataset records when something changes. Use one run to establish a baseline, then schedule the Actor to check for updates.

Quick start

1. Click Try with existing reviews to preview Facebook's US reviews. It emits those reviews only when that app-country target has no saved review baseline. You can also create an input with an app ID such as Facebook (284882215).
2. Choose storefronts such as us, gb, or de.
3. Run once to create a baseline. By default, existing reviews are stored but not emitted.
4. Save a task and schedule it daily or weekly to detect changes since the last successful check.
5. Connect the Actor to alerts with the .n8n/workflows/apple-app-store-change-alerts.json in this repository.

Example input:

{
  "apps": ["284882215"],
  "countries": ["us", "gb"],
  "includeReviews": true,
  "negativeReviewMaxRating": 3,
  "emitExistingReviewsOnFirstRun": false,
  "resetState": false
}

Set emitExistingReviewsOnFirstRun to true to emit currently visible reviews when review monitoring first creates its baseline. This is useful for a preview or an initial import. Normal monitoring keeps the default false to avoid flooding the Dataset with history.

What changes does the Actor detect?

It watches the app's version, price, average rating, screenshots, descriptive listing metadata, and visible written reviews. Review events include the star rating and review text. review_updated events include the current review fields plus before and after objects.

Every event has an idempotent eventId, app and storefront identity, change type, and observation time. baseline_created marks the initial state. A successful check with no changes adds no change rows.

Example: initial baseline

{
  "changeType": "baseline_created",
  "appId": "284882215",
  "appName": "Facebook",
  "country": "us",
  "version": "...",
  "reviewsStored": 10,
  "observedAt": "2026-10-08T07:00:00Z"
}

Example: newly observed review

{
  "changeType": "new_review",
  "appName": "Facebook",
  "country": "us",
  "reviewId": "1234567890",
  "rating": 1,
  "isNegative": true,
  "title": "Latest update",
  "body": "The app stopped working after the update.",
  "appVersion": "...",
  "reviewCreatedAt": "2026-10-09T06:00:00Z",
  "observedAt": "2026-10-09T07:00:00Z"
}

Example: listing change

{
  "changeType": "price_changed",
  "appName": "Facebook",
  "country": "us",
  "field": "price",
  "before": 0,
  "after": 2.99,
  "observedAt": "2026-10-09T07:00:00Z"
}

How to schedule App Store monitoring

Create an Apify task for each saved app group and storefront combination, then add a daily, hourly, or weekly schedule in Apify Console. State is stored by app ID and country, so successive runs of the same Actor keep independent histories for each storefront.

Ready-to-run public tasks:

• Try with existing reviews
• US competitor listing monitor
• Multi-country competitor monitor

For scheduled Slack alerts, import the workflow template at .n8n/workflows/apple-app-store-change-alerts.json from the project repository. It uses built-in n8n nodes and was import-tested with n8n 2.42.5. The workflow starts one Actor run, polls every 15 seconds, retrieves all Dataset pages, and sends selected review and listing-change events.

Set up a Header Auth credential with header Authorization and value Bearer YOUR_APIFY_TOKEN, then assign it to the Apify HTTP Request nodes. Add a Slack credential and channel ID, and replace the sample Facebook app ID and storefront. Keep the template inactive until both credentials and the channel are configured; execute it manually once to verify the result, then activate the daily 07:00 UTC schedule. Baselines and empty Datasets send no change messages; failed, partial, charge-limited, and timed-out runs send operational warnings. Live Slack delivery requires your own credentials and test channel.

Example Slack messages:

App Store change: Facebook (us) — new_review — 1/5 stars — Latest update
The app stopped working after the update.

App Store change: Facebook (us) — version_changed / version
Before: 576.0.0
After: 582.0.0

How much does monitoring cost?

The Actor charges $0.002 per successfully completed app-storefront check. Checks are charged whether or not the listing changed. Five apps across five storefronts checked once per day for 30 days cost 5 × 5 × 30 × $0.002 = $1.50 in check charges.

Apify displays the applicable run limits and estimated cost before starting a run. A review check can take longer than a listing-only check because it also requests the review feed.

Input

apps accepts numeric App Store IDs or complete apps.apple.com URLs containing an ID. countries is a list of two-letter storefront codes; it defaults to us. One run supports up to 100 apps, 20 countries, and 500 app-country checks.

See the Input tab for the full configuration. includeReviews enables review monitoring, negativeReviewMaxRating sets the negative-review threshold, emitExistingReviewsOnFirstRun opts into emitting the review baseline, and resetState deliberately starts fresh state for the requested targets.

Output and review coverage

The Dataset has Overview, Reviews, and Listing changes views. Download it in JSON, CSV, Excel, or another format supported by Apify.

Apple's public RSS feed exposes only recent reviews. At the current 50-review page size, the Actor scans at most ten pages (up to about 500 reviews) when establishing a review baseline. Later checks scan up to ten pages and stop after reaching a page of known reviews. Reviews outside those pages cannot be monitored, and review edits are only visible when the review is fetched.

For review IDs not already in saved state, the Actor emits new_review only when Apple's valid review timestamp is later than the saved review-baseline timestamp. It stores older or undated reviews without alerting. This timestamp is a source-provided review update time; it indicates when the review was first observed after the baseline, not verified publication time. Existing review records are emitted only when emitExistingReviewsOnFirstRun is enabled.

MCP and API access

Connect an AI client to the hosted Apify MCP server using your Apify credential, then ask:

Run the Apple App Store Review & Competitor Change Monitor for app 284882215 in the US. Show me negative reviews and listing changes.

An Actor call returns the run and storage IDs. Fetch the run's Dataset items to read its changes; baseline and run summary are stored separately. The Apify MCP guide explains how to connect clients and retrieve Dataset results.

For direct integrations, use the API tab or the Apify client libraries. Use the official n8n integration to start an Actor and fetch Dataset results inside an automation.

Troubleshooting

• The first run has no review text: This is expected when using the baseline-only default. Enable emitExistingReviewsOnFirstRun to preview currently visible reviews.
• A target has no output on a later run: A successful run emits no change events when nothing changed. Check the run's OUTPUT summary for per-target errors or a spending limit.
• An app is unavailable: Confirm its ID and that it is present in each requested storefront.
• A review was not detected: Apple exposes a bounded recent feed. Increase check frequency to reduce the chance that a review falls outside the pages available to the Actor.
• Resetting state creates a fresh baseline: Set resetState only when you intentionally want to replace the saved target history.

For API details, use the API tab. Report issues in the Issues tab and include a run ID and storefront when possible.