Top-N Auto-Deep-Analysis Selector
Top-N Auto-Deep-Analysis Selector
After a skim crawl, the platform automatically identifies the most promising products and flags them for deep analysis — without requiring manual triage.
Overview
Once a directory skim completes, the system scores every discovered product using two readily-available surface signals:
- Review count — a proxy for market traction and demand.
- Pricing tier — a proxy for revenue potential.
The top N products (default: 20) are automatically flagged. You review the shortlist, make any adjustments, then confirm to launch the full deep-scrape pipeline against those products.
Workflow
1. Paste directory URL
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2. Skim crawl runs (all listings scanned for surface signals)
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3. Products ranked by review count + pricing tier
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4. Top 20 auto-flagged for deep analysis
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5. Review flagged list → adjust if needed → confirm
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6. Deep-scrape pipeline launches on confirmed products
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7. Full scored dossiers generated and ranked on dashboard
Configuration
The number of auto-flagged products is configurable:
| Parameter | Default | Description |
|---|---|---|
topN | 20 | How many top-ranked products to flag after a skim crawl |
Adjust topN to cast a wider or narrower net before the deep-analysis stage.
Flagging Signals
| Signal | What it measures |
|---|---|
| Review count | Market traction — products with more reviews indicate higher demand and user engagement |
| Pricing tier | Revenue potential — higher-priced products suggest willingness-to-pay in the segment |
These signals are deliberately lightweight — they are extracted during the fast skim pass without triggering a full deep scrape.
Review Step
Before the deep-scrape pipeline is triggered, you are presented with the auto-flagged list. At this point you can:
- Inspect each flagged product's surface-level data.
- Remove products that don't fit your research focus.
- Add products from the broader skim results that the auto-ranker may have missed.
- Confirm the final list to start deep analysis.
This confirmation gate ensures that expensive deep-scrape jobs are only run on products you actually want to analyse.
Relationship to Composite Scoring
The auto-flagging step uses a simplified surface score (review count + pricing tier) as a fast filter. Once a product passes through deep analysis, it receives the full composite opportunity score covering replicability, market demand, revenue potential, and competitive gaps — which drives the final dashboard ranking.