I'm building a market research product and want clean public web data feeds

Absolutely — if you want clean public web data feeds for a market research product, the best approach is usually to combine:

1. Official/public APIs
2. Public datasets and government sources
3. RSS/Atom and news feeds
4. Structured web sources with stable HTML
5. Licensed aggregators where quality matters more than raw scrape volume

Good categories of public web feeds

1) Government and statistical data

These are often the cleanest and most reliable.

• US Census API
• BLS API (labor, CPI, wages)
• BEA API (GDP, income, trade)
• SEC EDGAR filings
• EU Open Data Portal
• World Bank / IMF / OECD
• FRED economic time series

Best for:

• macro trends
• industry sizing
• employment/wages
• regional comparisons

2) Company and business data

Useful for firmographics, funding, and market mapping.

• OpenCorporates
• Companies House (UK)
• SEC / EDGAR
• Crunchbase if licensed
• GDELT for entity/event tracking in news

Best for:

• company profiling
• market monitoring
• competitor tracking

3) News and media feeds

Good for market sentiment and event detection.

• RSS feeds from major publications
• GDELT 2.1 global news/events database
• NewsAPI if your use case fits licensing
• Publisher-specific feeds where allowed

Best for:

• trend detection
• market alerts
• brand/competitor monitoring

4) E-commerce / product / pricing feeds

If you’re doing retail or consumer market research:

• retailer product pages with structured markup
• public product catalogs
• affiliate/product APIs
• marketplace feeds where permitted

Best for:

• price tracking
• assortment analysis
• product feature comparisons

5) Social and community data

Useful, but more limited and policy-sensitive.

• Reddit API
• YouTube Data API
• public forum RSS feeds
• public review platforms where terms allow access

Best for:

• customer sentiment
• topic discovery
• emerging demand signals

What makes a feed “clean”

Look for sources with:

• Stable schemas
• Consistent update frequency
• Deduplication identifiers
• Pagination or delta endpoints
• Permissive licensing
• Machine-readable formats like JSON, CSV, XML, RSS

Recommended architecture

For a market research product, a practical stack is:

• Ingestion layer: APIs, RSS, scheduled crawls
• Normalization layer: canonical fields, entity resolution
• Storage: raw + cleaned + indexed datasets
• Enrichment: geocoding, categorization, NER, dedupe
• Serving layer: search, alerts, dashboards, exports

Important caution

For web scraping, always check:

• site Terms of Service
• robots.txt
• copyright/database rights
• rate limits
• licensing for redistribution

For a commercial product, licensed or official sources are usually safer than broad scraping.

If you want, I can help with one of these next:

1. Build a starter list of public feeds by industry
2. Design a data ingestion architecture
3. Recommend specific APIs for your market segment
4. Compare scraping vs API vs licensed data for your use case

If you tell me your target market research niche — e.g. retail, SaaS, finance, healthcare, real estate, consumer brands — I can give you a curated feed list.