Apify Store Radar - Comprehensive Market Intelligence

Pure data collection tool for expert and LLM analysis of the Apify Store ecosystem

Collect comprehensive, factual data on 7,800+ Apify Store actors with detailed metrics for growth, quality, market position, monetization, and developer reputation. No predetermined gaps or scores - just rich, structured data for informed decision-making.

🎯 Purpose

This actor provides comprehensive, factual data about all actors in the Apify Store for expert or LLM analysis. It does NOT provide predetermined opportunity scores, recommendations, or gap analysis - only rich, structured data that experts can analyze.

Key Principle: Data collection, not opinion generation.

📊 Complete Data Structure

Each actor includes 30+ metrics across 9 categories:

1. Basic Information

Actor ID, title, developer, platform
Categories, rating, reviews, bookmarks
User metrics (total, MAU, WAU, 90-day)
Success rate, runs, builds

2. Growth Metrics

User Momentum: MAU/total_users ratio (engagement velocity)
Runs Per User: Usage intensity per active user
Retention Proxy: WAU/MAU ratio (weekly retention)
Engagement Score: Composite 0-10 metric

3. Quality Signals

Bookmark Rate: Engagement indicator
Review Engagement: User feedback rate
Maintenance Status: active/stale/abandoned
Reliability Score: Success rate weighted by volume
Days Since Last Run: Recency indicator

4. Market Position

Platform Rank: Position within platform (#1 = leader)
Category Rank: Position within Apify category
Popularity Percentile: Overall market position (0-100)
Saturation Index: Number of competitors
Is Market Leader: Top 5 flag

5. Monetization

Pricing Model: PAY_PER_EVENT, FREE, etc.
Price Range: Min/max across all tiers
Pricing Tiers: Number of available tiers
Has Free Tier: Boolean flag
All Pricing Events: Complete breakdown
Revenue Estimate: Monthly estimate (heuristic)
Pricing Strategy: freemium/premium/free

6. Schema Analysis

Has Input Schema: Schema availability
Detected Platforms: Platforms in schema
Config Complexity: Number of input fields
Requires Authentication: API key needed
Schema Quality Score: 0-10 rating

7. Developer Profile

Actor Count: Portfolio size
Average Rating: Across all their actors
Total Users: Combined user base
Is Apify Official: Official team flag
Picture URL: Developer avatar

8. Aggregator Analysis

Is Aggregator: Multi-platform flag
Aggregator Confidence: 0-1 score
Detected Platforms: Platforms mentioned
Complementary Platforms: Suggested combinations
Opportunity Score: Aggregation potential

9. Additional Fields

90-day users, total builds, total metamorphs
Agentic payment support
Notice status (deprecation)

🚀 Quick Start

Basic Usage (All Actors)

{
  "analysis_mode": "full",
  "max_results": 0
}

Quick Scan (Top 1000)

{
  "analysis_mode": "quick_scan",
  "max_results": 0
}

Filter by Platform

{
  "platform_filter": ["instagram", "linkedin"],
  "min_users": 1000,
  "min_rating": 4.0,
  "sort_by": "mau"
}

💡 Analysis Use Cases

For Automation Builders (n8n, Zapier, Make)

Identify actors suitable for workflows:

// Find reliable actors for automation
actors.filter(a =>
  a.quality_signals.maintenance_status === 'active' &&
  a.quality_signals.reliability_score > 1.0 &&
  a.mau > 500
)

For Lead Generation Agencies

Find data enrichment opportunities:

// High-demand lead gen tools
actors.filter(a =>
  a.categories.includes('LEAD_GENERATION') &&
  a.market_position.is_market_leader &&
  a.mau > 1000
)

For Market Intelligence

Quality improvement opportunities:

// Leaders with quality issues
actors.filter(a =>
  a.market_position.is_market_leader &&
  a.rating < 4.5 &&
  a.mau > 1000
)

For Competitive Analysis

Abandoned high-value actors:

// Replacement opportunities
actors.filter(a =>
  a.quality_signals.maintenance_status === 'abandoned' &&
  a.mau > 500
)

For Pricing Strategy

Market pricing analysis:

// By platform and position
actors
  .filter(a => a.platform === 'linkedin')
  .map(a => ({
    rank: a.market_position.platform_rank,
    strategy: a.monetization.pricing_strategy,
    price: a.monetization.price_min_usd,
    mau: a.mau
  }))

🤖 LLM Analysis Examples

Find Market Gaps

Analyze the dataset and identify platforms where:
1. The market leader has rating < 4.5
2. Total platform MAU > 5000
3. Maintenance status shows opportunity

Do NOT recommend keywords - analyze actual actor quality.

Developer Opportunities

Find platforms where:
1. Saturation index < 20 (low competition)
2. Platform total MAU > 1000 (proven demand)
3. No Apify official actors (opportunity)
4. Active maintenance (feasible market)

Pricing Intelligence

For each platform with >5 actors:
1. What's the dominant pricing strategy?
2. What do market leaders charge?
3. Is there a correlation between pricing and rating?

Aggregation Opportunities

Find platforms with:
1. Aggregation opportunity score > 8
2. Existing aggregators < 3
3. Complementary platforms with proven demand

📈 Dataset Views

6 optimized views available in Apify Console:

overview - Key metrics with ranks and engagement
market_leaders - Top 5 performers per platform
growth_analysis - High-growth actors (user_momentum)
pricing_competitive - Pricing strategy comparison
quality_reliability - Maintenance and reliability
developer_portfolio - Developer reputation

🔧 Input Configuration

Field	Type	Description	Default
`analysis_mode`	enum	`full`, `quick_scan`, `platforms_only`	`full`
`platform_filter`	array	Filter by platforms (e.g., `["google_maps"]`)	`[]`
`min_users`	integer	Minimum total users	`0`
`min_rating`	number	Minimum rating (0-5)	`0`
`sort_by`	enum	`popularity`, `mau`, `rating`, `total_users`	`popularity`
`max_results`	integer	Maximum results (0 = unlimited)	`0`
`skip_gap_analysis`	boolean	Skip aggregator gap analysis	`true`

📊 Output Files

Dataset

All actors with complete metrics
Filterable by any field combination
Export as JSON, CSV, Excel

KV Store

EXTRACTED_PLATFORMS: Platform summaries
FILTERED_GAP_FACTS: Aggregator gaps (if enabled)

🎓 Metrics Explained

Growth Metrics

User Momentum = MAU / Total Users

High (> 0.2): Strong engagement
Low (< 0.05): Declining usage

Runs Per User = Total Runs 30d / MAU

High (> 50): Power users
Low (< 5): Casual usage

Retention Proxy = WAU / MAU

High (> 0.5): Frequent returns
Low (< 0.2): One-time usage

Quality Signals

Maintenance Status:

active: Last run < 7 days
stale: 7-30 days
abandoned: > 30 days

Reliability Score = Success Rate × (1 + log₁₀(runs) / 10)

Weights success rate by confidence

Market Position

Platform Rank: Sorted by popularity within platform Saturation Index: Number of same-platform competitors Is Market Leader: True if platform_rank ≤ 5

⚠️ Important Limitations

What This Actor Does NOT Do:

❌ Keyword-based gap analysis (proven unreliable)
❌ Predetermined opportunity scores
❌ Recommendations or suggestions
❌ Feature detection via INPUT_SCHEMA (most private)

What This Actor DOES:

✅ Comprehensive factual metrics
✅ Market position indicators
✅ Quality and growth signals
✅ Developer reputation data
✅ Aggregator detection (multi-platform only)

Verification Always Required:

Before building any actor based on this data:

Search Apify Store manually
Check top actors' INPUT_SCHEMA
Read actor reviews and documentation
Test actors yourself
Assess technical feasibility
Verify legal/ToS compliance

💰 Pricing

Pay-per-result model:

Event: record-ingested (one per actor)
Full store: ~$7.80 for 7,800 actors
Quick scan: ~$1.00 for 1,000 actors

📅 Recommended Usage

Weekly: Track growth trends
Monthly: Competitive analysis before decisions
Ad-hoc: Before launching new actor

🛠️ Technical Details

Architecture

TypeScript with strict typing
Node.js 24+
Apify SDK 3.x, Axios, Zod
Apify Store API v2

Performance

Full store: 60-90 seconds
Quick scan: 30-45 seconds
Market position: O(n log n) per platform

📞 Support

Email: kontakt@barrierefix.de

📜 License

MIT License

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