App Store Opportunity Radar

Find App Store niches where demand already exists but users are still asking for something better.

This Actor searches Apple's public App Store data, reads a bounded set of recent public reviews, and ranks competitors by:

• proven demand from lifetime ratings;
• recent low-rating share and rating gap;
• recurring complaint themes;
• explicit feature requests;
• recent review velocity;
• time since the last app update.

It produces one clean row per app, ready for CSV, Google Sheets, an API workflow, or an AI agent.

Unofficial independent tool: This Actor is not affiliated with, sponsored by, or endorsed by Apple. Apple and App Store are trademarks of Apple Inc.

What makes it useful

Raw review scrapers give you thousands of rows to read. This Actor turns those reviews into a shortlist of product opportunities with the evidence attached. It uses deterministic analysis, so there is no LLM key, surprise AI bill, proxy, login, or invented quote.

Example use cases

• An indie developer compares the top habit trackers before building.
• An ASO agency finds recurring complaints in a client's keyword category.
• A product team watches competitor pain after a major release.
• An investor screens a mobile-app niche before deeper research.

Quick start

Run a ten-app market scan with:

{
  "searchTerms": ["habit tracker"],
  "country": "us",
  "maxAppsPerSearch": 10,
  "reviewsPerApp": 100
}

Or analyze exact competitors:

{
  "searchTerms": [],
  "appIds": [
    "https://apps.apple.com/us/app/example/id123456789",
    "987654321"
  ],
  "country": "us",
  "reviewsPerApp": 100
}

Input

Use one or both discovery modes:

1. searchTerms: category keywords such as habit tracker, invoice maker, or photo cleaner (up to 20 unique terms per run).
2. appIds: exact Apple app IDs or App Store URLs for direct competitor analysis. Up to 200 unique apps can be supplied per run. Set searchTerms to [] for a direct-only run because the input form starts with an example keyword.

Important controls:

• country: two-letter App Store storefront, default us.
• maxAppsPerSearch: 1-50 apps per keyword.
• reviewsPerApp: 0-500 recent reviews per app.
• minimumRatings: removes tiny apps from keyword discovery.
• negativeRatingThreshold: choose whether 1-2 or 1-3 stars count as negative.

Output

Every dataset row contains:

• analysisCoverage: review-intelligence or metadata-only;
• opportunityScore and scoreBand;
• App Store metadata and demand indicators;
• recent rating and negative-review share;
• ranked complaint themes with public review excerpts;
• feature-request evidence;
• transparent signals explaining the score;
• source URLs and analysis timestamp.

The OUTPUT record contains a run-level market summary with the top opportunities and most common pain points, plus the number of full review analyses and metadata-only results.

Example row:

{
  "analysisCoverage": "review-intelligence",
  "opportunityScore": 68.4,
  "scoreBand": "test",
  "appName": "Example App",
  "totalRatingCount": 28400,
  "recentReviewCount": 100,
  "negativeReviewShare": 0.31,
  "featureRequestCount": 12,
  "signals": [
    "high proven demand",
    "30%+ of sampled reviews are negative"
  ],
  "painPoints": [
    {
      "topic": "pricing-and-subscription",
      "count": 14
    }
  ]
}

Coverage and billing

The Actor tries two public Apple review feeds. When review entries are available, the row is labeled review-intelligence and triggers one app-analyzed event. At the current price of $0.01 per event, a ten-app scan costs at most $0.10, with platform usage included.

If Apple returns no public review entries, the Actor still returns useful App Store metadata, labels the row metadata-only, adds a data warning, and does not trigger the paid event. It never interprets an empty feed as proof that users have no complaints.

How to interpret the score

The score is a prioritization tool, not a promise of downloads or revenue.

• strong-test: strong enough to justify customer interviews, a landing page, or a small prototype.
• test: collect more evidence before building.
• watch: weak or insufficient evidence in this run.

Always validate distribution, willingness to pay, platform rules, and build cost before committing to a product.

Data boundaries

• Uses public Apple search and customer-review endpoints.
• Apple can return fewer reviews than requested.
• Review availability differs by country.
• No personal enrichment, login, CAPTCHA bypass, or private data.
• Deterministic topic rules are explainable but less nuanced than a human review.

Pricing

The current Store price is $0.01 per app-analyzed event, with platform usage included. Only rows with review intelligence trigger the event; metadata-only fallback rows are returned without that charge. Pricing should change only after observing repeat usage and compute margin.