Competitor Review Teardown — Instant Battle-Card

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Stop reading 500 reviews by hand. Drop in your competitors → get a ranked battle-card you can act on today.

This Actor pulls a competitor's public reviews and runs a deterministic analysis engine over them — no LLM, no API keys, no per-run AI cost — and returns a battle-card, not a data dump:

• 🔴 Top complaints, ranked by frequency, each with the % of unhappy reviewers who mention it and verbatim quotes
• 🟢 Top praises (what you'll be measured against)
• 💡 Most-requested features (the roadmap gaps your competitor is ignoring)
• 🚪 Switch-trigger quotes — the exact words people use when they cancel or leave
• 📈 Sentiment over time (is the competitor getting better or worse?)
• 🎯 Ready-to-use ad angles templated from the top complaints

How it works

1. Paste one or more competitors (App Store app URLs or IDs).
2. The Actor fetches up to ~500 recent reviews per competitor from the public Apple App Store RSS feed.
3. A deterministic NLP engine clusters reviews into product themes (pricing, bugs, support, UX, performance, features, …), splits complaints vs praise by star rating, ranks them, and pulls representative quotes.
4. You get one clean battle-card record per competitor (plus a cross-competitor comparison when you give it 2+).

What you get (output)

One dataset record per competitor:

{
 "competitor": "Duolingo",
 "reviewsAnalyzed": 300,
 "avgRating": 3.88,
 "ratingDistribution": { "1": 31, "2": 18, "3": 22, "4": 40, "5": 189 },
 "topComplaints": [
 { "theme": "Pricing & billing", "mentions": 18, "pctOfReviews": 64.3,
 "sampleQuotes": ["Way too expensive for what it does.", "Charged me twice…"] }
 ],
 "topPraises": [ { "theme": "Ease of use / UI", "mentions": 41, "sampleQuotes": ["…"] } ],
 "mostRequestedFeatures": [ { "request": "wish there was an option for Mexican Spanish", "count": 3 } ],
 "switchTriggers": [ { "quote": "cancelled my subscription after…", "count": 5 } ],
 "sentimentTrend": [ { "month": "2026-05", "avgRating": 3.7, "reviews": 88 } ],
 "adAngles": ["Tired of Duolingo's pricing problems? 64% of their unhappy reviewers complain about it — here's the switch."]
}

Input

Pricing

Pay-per-event: a small flat actor-start fee, then one charge per competitor battle-card produced. You pay for the finished analysis, not per review — and a competitor with no findable reviews is never charged.

Who it's for

• Product marketers building battle-cards and switch campaigns
• Founders / PMs mining a rival's reviews for roadmap gaps
• Growth / paid-ads teams who need ad angles grounded in real customer language
• App developers sizing up the competition before a launch

What it is not

• It is not a raw review scraper — it returns the analysis, not 500 rows to read yourself.
• It does not use an LLM, so output is deterministic and reproducible (and free of hallucinated "insights").
• v1 covers the Apple App Store. Google Play and Trustpilot (which sits behind Cloudflare and needs a residential proxy) are on the roadmap.

Notes

• 100% public data via the official Apple iTunes RSS feed — no login, no key.
• Built and maintained by apify.com/bikram07.