How we analyze App Store competitor reviews without reading 500 of them

The problem

You want to know what users hate about competing iOS apps. The honest way: read hundreds of App Store reviews. The real way: nobody does that.

What we built

App Store Complaint Miner is an Apify Actor that does it for you.

Give it 1-5 App Store app IDs. It fetches reviews, runs them through an AI clustering pipeline, and returns:

• Complaint clusters with frequency and severity
• Missing features users are asking for
• Switching signals — reviews where users say they are leaving
• Cross-app gaps — problems appearing across multiple competitors
• Evidence — real review quotes supporting every finding

Example

Input: 3 fitness app IDs

Output:

TOP COMPLAINTS

1. Subscription cancellation is difficult — Frequency: 84 | Severity: HIGH
2. Missing Apple Health integration — Frequency: 61 | Severity: HIGH
3. Excessive notifications — Frequency: 47 | Severity: MEDIUM

How it works

1. Fetches reviews via Apple iTunes RSS (up to 500 per app)
2. Runs AI extraction in batches of 50 reviews
3. Clusters similar complaints semantically
4. Calculates frequency from actual review count — no estimates
5. Outputs structured JSON to Apify dataset

Try it

apify.com/kvko/app-store-complaint-miner

Pay per run — $0.99 per app analyzed. No subscription.

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Built by KVKO Lab — microSaaS tools for founders.