🚀 Startup Opportunity Radar

Discover profitable online business opportunities from startup acquisition marketplaces.

Startup Opportunity Radar scans public marketplace listings from sources such as Flippa, Acquire.com, and optionally Indie Hackers, then turns raw listings into structured opportunity reports. It normalizes revenue and price metrics, estimates profit multiples, scores each listing from 0 to 100, and generates AI-ready summaries that can be used in dashboards, automations, and research workflows. The default setup is optimized for Flippa-first runs to maximize speed and stability.

🎯 What this Actor does

This Actor helps founders, operators, investors, and researchers answer questions such as:

• Which niches currently show the strongest acquisition opportunities?
• Which listings have healthy revenue relative to asking price?
• Which startup categories appear repeatedly across marketplaces?
• Which opportunities are worth deeper manual validation?

For each listing, the Actor attempts to extract:

• Title and listing URL 🌐
• Business model and niche 🏢
• Monthly revenue and monthly profit 💰
• Asking price and profit multiple 💵
• Traffic and monetization type 📈
• SEO keyword and backlink signals for traffic-based estimation 🔍
• Age of business 📅
• Tech stack, location, and description when available 💻
• Opportunity score ⭐
• AI-ready summary with suggested strategy angles 🤖

The Actor also generates aggregate insights, including:

• Top niches
• Average revenue by niche
• Best ROI opportunities

💡 Why use it

• Save time compared to manually checking dozens of listings
• Normalize inconsistent marketplace formats into one schema
• Filter low-quality opportunities before analysis
• Feed scored opportunities into AI agents, sheets, CRMs, or BI tools
• Build repeatable startup market monitoring workflows

⚙️ How it works

1. The Actor opens marketplace category and listing pages with PlaywrightCrawler.
2. It discovers listing detail URLs and avoids duplicate processing.
3. It extracts public business signals from each listing page.
4. It normalizes money, traffic, and business age values.
5. It calculates profitMultiple = askingPrice / monthlyProfit.
6. It applies a weighted opportunity scoring model.
7. It estimates traffic signals and computes estimatedProfit = traffic * conversionRate * estimatedUnitPrice.
8. It stores validated opportunities in the default dataset.
9. It saves aggregate insights to the default key-value store. 📊

📝 Input

Use the input form in Apify Console or provide JSON like this:

{
  "sources": ["flippa "],
  "maxListings": 200 ,
  "minRevenue": 1000 ,
  "maxPrice": 500000 ,
  "businessModel": "SaaS ",
  "minOpportunityScore": 50 ,
  "maxConcurrency": 3 
}

📝 Input fields

• sources: Marketplaces to scrape. Supported values: flippa, acquire, indiehackers
• maxListings: Maximum number of listing detail pages to process in one run
• minRevenue: Minimum monthly revenue required for a record to be saved
• maxPrice: Maximum asking price allowed for saved results
• businessModel: Optional text filter such as SaaS or Ecommerce
• minOpportunityScore: Minimum score required for a result to be saved
• maxConcurrency: Maximum number of concurrent browser pages ⚡
• proxyConfiguration: Optional Apify proxy settings

Output

📊 Dataset

Each dataset item contains a scored startup opportunity record.

Example:

{
  "id": "flippa-flippa-12345",
  "source": "flippa",
  "sourceId": "flippa-12345",
  "title": "AI Resume Builder",
  "niche": "AI tools",
  "url": "https://flippa.com/...",
  "monthlyRevenue": 12000,
  "monthlyProfit": 5000,
  "askingPrice": 120000,
  "profitMultiple": 24,
  "traffic": 48000,
  "seoKeywords": 6200,
  "backlinks": 1800,
  "conversionRate": 0.024,
  "estimatedUnitPrice": 49,
  "estimatedProfit": 56448,
  "monetization": "Subscription",
  "businessModel": "SaaS subscription",
  "ageOfBusinessMonths": 18,
  "location": "United States",
  "techStack": ["Next.js", "Stripe", "OpenAI"],
  "listingDescription": "AI resume builder for job seekers and career coaches.",
  "opportunityScore": 82,
  "scoreBreakdown": {
    "revenue": 0.24,
    "traffic": 0.1,
    "profitMultiple": 0.86,
    "age": 0.3
  },
  "aiSummary": {
    "niche": "AI tools",
    "avgRevenue": 12000,
    "profitMultiple": 24,
    "opportunityScore": 82,
    "businessModel": "SaaS subscription",
    "suggestedStrategy": [
      "Build a narrower AI tools workflow with faster onboarding.",
      "Use content-led acquisition and comparison landing pages.",
      "Package the offer around one measurable outcome instead of a general AI toolkit."
    ]
  },
  "extractedAt": "2026-03-06T20:00:00.000Z"
}

🗄️ Key-value store

The Actor stores additional records:

• INSIGHTS: top niches, average revenue by niche, and best ROI opportunities
• SUMMARY: run-level summary including saved count, sources, active filters, and allExtractedDatasetId

The Actor also writes all extracted records before filtering into a named dataset: all-extracted.

🎯 Opportunity scoring

The Actor estimates opportunity score using weighted heuristics based on:

• Revenue
• Traffic
• Profit multiple
• Business age
• Competition signals

The final result is normalized to a 0-100 range. Lower profit multiples and stronger business fundamentals generally produce better scores.

🎯 Typical use cases

• Monitor startup acquisition marketplaces each week
• Find underpriced SaaS or ecommerce businesses
• Build a lead list for acquisition research
• Track emerging niches across digital businesses
• Feed results into no-code automations or internal AI agents

⚠️ Limitations

• Some marketplace pages may require login or additional verification
• Public listing data can be incomplete or intentionally rounded
• Metrics such as revenue, traffic, or tech stack may not always be disclosed
• Opportunity score is a heuristic, not investment advice

⚡ Performance and cost

The Actor is designed to run efficiently with conservative browser concurrency and deduplication. The default setup targets up to 200 listings and is intended to stay practical within common Apify memory limits such as 512 MB, depending on source behavior and anti-bot responses.

To reduce cost:

• Keep maxListings close to your actual research need
• Use minRevenue, maxPrice, and minOpportunityScore filters
• Avoid unnecessarily high concurrency

📚 Source notes

• Flippa: Strong source for websites, content businesses, SaaS, and ecommerce listings
• Acquire.com: Optional source. Listing visibility can be heavily gated and may return limited public detail links.
• Indie Hackers: Optional supplemental source for product discovery and niche signals

🆘 Need help?

If you use this Actor in a production workflow, validate promising opportunities manually before making decisions. Marketplace listings change frequently, and the best use of this Actor is as a fast research and prioritization layer rather than a final source of truth.