Apify Store Quality Radar

Find Apify Store niches and competitors with real adoption, ratings, review counts, pricing, run-quality signals, and market-gap scores.

This Actor audits Apify Store search results and returns a ranked dataset that helps builders, agencies, and data teams decide what to build, avoid, improve, or monitor.

It is designed for people who do not want another raw catalog export. The output highlights demand, weak incumbents, pricing signals, public trust gaps, and practical buyer-workflow wedges.

The important idea: ratings and reviews are product research, not decoration. A listing with active usage but weak ratings, low review count, or confusing output is often a stronger opportunity than a niche with no competitors and no demand.

Why Use This Actor

Apify has thousands of actors. Clicking listings manually is slow, and raw Store metadata does not directly answer the important question:

Is this niche worth building in, and how could a new actor win?

This Actor turns public Store signals into decision-ready rows:

• Which actors have real 7-day and 30-day usage.
• Which competitors have weak ratings or thin trust signals.
• Which broad incumbents should be studied but not cloned.
• Which niches deserve deeper research before building.
• Which product angle could make a new actor more useful.

Best For

User	Workflow
Apify builders	Find niches with demand before coding.
Data product teams	Audit competitors, pricing, and usage signals.
Agencies	Discover actor gaps for lead-generation or automation products.
Indie hackers	Avoid saturated clones and find buyer-ready workflow ideas.
AI agents	Pull structured marketplace intelligence with stable fields.

What It Extracts

• Actor title, slug, URL, description, and categories.
• Total users, 30-day users, 7-day users, and bookmarks.
• Rating score and public review count.
• Review signal, review research URL, likely user pain points, and likely user likes inferred from public trust and usage signals.
• 30-day run count and success rate when exposed by the Store API.
• Pricing model, primary event, and estimated price per 1,000 results/events.
• Tags such as reviews, lead generation, AI/RAG, jobs, ads, social, ecommerce, real estate, and developer tools.
• Weakness signals such as weak rating, low trust for high demand, low success rate, thin description, or unclear pricing.
• A quality bar for what a better competing actor must prove before launch.
• Opportunity score, decision label, buyer wedge, and action note.

Input Example

{
  "searchTerms": ["review monitor", "reddit sentiment", "facebook ads library"],
  "maxActorsPerSearch": 25,
  "minUsers30Days": 1,
  "onlyWeakIncumbents": false,
  "includeGenericGiants": false
}

Output Example

{
  "platform": "apify_store",
  "searchTerm": "trustpilot reviews",
  "title": "Trustpilot Reviews Scraper",
  "slug": "example/trustpilot-reviews-scraper",
  "url": "https://apify.com/example/trustpilot-reviews-scraper",
  "totalUsers": 2000,
  "users30d": 120,
  "rating": 3.4,
  "reviewCount": 9,
  "reviewSignal": "active demand with visible review pain",
  "likelyUserPainPoints": [
    "runs may fail or require retries",
    "users may be unhappy with output quality or reliability",
    "review buyers likely need freshness filters, dedupe, sentiment, replies, and alert-ready notes"
  ],
  "likelyUserLikes": [
    "users are returning recently, so the workflow has recurring value",
    "users like review exports for monitoring reputation and support issues"
  ],
  "qualityBar": [
    "default input must return useful records, not an empty dataset",
    "dataset columns must be readable without opening JSON",
    "include source URL, scrapedAt, and dedupeKey on every row",
    "ship at least 10 pre-push tests plus real cloud smoke tests",
    "include rating, review text, dates, author fields, dedupe, sentiment, urgency, and reply/response signals"
  ],
  "reviewResearchUrl": "https://apify.com/example/trustpilot-reviews-scraper/reviews",
  "successRate30d": 78,
  "pricingModel": "PAY_PER_EVENT",
  "primaryPricePer1000": 2.0,
  "weaknesses": ["weak rating for visible demand", "lower 30-day run success rate"],
  "buyerWedge": "Add urgency scoring, response-gap detection, summaries, and monitor/delta mode. Compete on reliability and clearer first-run value.",
  "opportunityScore": 31.5,
  "decision": "BUILD_COUNTER_POSITIONING",
  "actionNote": "Strong candidate: demand exists and incumbent has visible weakness. Build only with a sharper buyer workflow and real smoke tests."
}

Decision Labels

Decision	Meaning
BUILD_COUNTER_POSITIONING	Demand exists and visible quality/trust gaps exist. Worth deeper competitor testing.
RESEARCH_DEEPER	Promising niche, but not enough evidence to build immediately.
WATCHLIST	Some signal. Monitor before building.
STUDY_ONLY_GENERIC	A broad giant. Use it as a demand map, not a clone target.
LOW_SIGNAL	Too little visible signal for a public launch.

How This Is Different

Many Apify Store scrapers export catalog rows. This Actor focuses on build decisions:

• It combines adoption, reviews, ratings, pricing, and run-quality signals.
• It flags weak incumbents instead of only listing popular actors.
• It turns ratings/reviews into research prompts: what users may dislike, what they probably value, and what quality bar a better actor must meet.
• It penalizes generic giants so new builders do not blindly clone saturated categories.
• It returns buyer-wedge notes that explain how a new actor could compete on output quality.

Notes And Limitations

• Uses Apify's public Store API. No Apify token is required for normal public Store data.
• Exact actor revenue is private and cannot be guaranteed from public Store data.
• Rating and run statistics are public trust signals, not guaranteed full review-text analysis. Use reviewResearchUrl to manually inspect visible review pages and issues before committing to a build.
• Use this as a research filter before manual competitor testing, not as a guaranteed revenue predictor.

Tested Coverage

The Actor is designed around public Store search responses and includes local tests for:

• Opportunity scoring.
• Weak-incumbent detection.
• Generic giant filtering.
• Pricing extraction from pay-per-event fields and tiered pricing.
• Bad input handling.