Mobile App Review Intelligence

Mobile App Review Intelligence turns recent public Apple App Store reviews into product, support, ASO, and competitor-research signals.

Instead of exporting a wall of raw review text, the actor analyzes the latest public reviews for each app and returns one compact row per app. The row highlights what users are complaining about, what they praise, which app versions are mentioned, and what product or support teams should investigate next.

What You Can Use It For

• Monitor competitor app review pain points before planning your own roadmap.
• Find recurring complaints after an app update.
• Identify support, login, billing, crash, performance, and trust issues from recent reviews.
• Build ASO and product research dashboards from structured review intelligence.
• Track whether negative reviews cluster around specific app versions.
• Summarize public customer language for product marketing, support macros, and release planning.

Input

Provide Apple App Store app URLs or numeric app IDs.

{
  "appUrlsOrIds": [
    "284882215",
    "https://apps.apple.com/us/app/x/id333903271"
  ],
  "country": "us",
  "maxReviews": 30,
  "maxReviewPages": 2
}

Output

Each successful dataset row is focused on buyer-facing app intelligence:

• appName
• appId
• appStoreUrl
• currentVersion
• sellerName
• primaryGenre
• storeAverageRating
• storeUserRatingCount
• reviewsAnalyzed
• sampleAverageRating
• negativeReviewRate
• latestReviewDate
• recentReleaseNotes
• versionSignals
• complaintThemes
• praiseThemes
• productOpportunities
• reviewEvidence

How The Actor Works

The actor uses public Apple sources:

• iTunes Lookup API for app metadata.
• Apple App Store customer review feeds where Apple returns recent public review entries.
• Public App Store page review highlights and version history as a fallback when review feeds are sparse for a country or app.

It then classifies review text into practical themes such as crashes, login problems, subscription issues, performance, update bugs, support, notifications, privacy, and trust. The output keeps only short evidence snippets and summary signals, so the dataset is useful in a spreadsheet or automation workflow without cleanup.

Practical Workflow

1. Add competitor or owned app IDs.
2. Start with 20-30 reviews per app for a quick check.
3. Review complaint themes and negative review rate.
4. Compare version signals to detect update-related regressions.
5. Export results to your product dashboard, support workflow, or ASO research sheet.

Notes

• This actor analyzes public Apple App Store data only.
• It does not access private developer dashboards, user accounts, or non-public review data.
• Review availability can vary by country and app.
• The actor is designed for product research and monitoring, not for legal, medical, financial, or compliance conclusions.