E-commerce data for market researchers

If you size markets and track category trends, your job is to turn millions of live listings into defensible numbers — average price, brand share, assortment depth, review velocity — at a scale no survey panel can match. ShopAPIS gives you the raw observations: price, brand, seller, rating, review_count and category for products across 70+ marketplaces in 30+ countries, so you aggregate real transactions-adjacent data instead of extrapolating from a sample.

A marketplace is the largest available census of what people actually buy and pay — so market sizing and trend analysis become a query over real listings, not a survey of stated intent. With global retail e-commerce sales forecast at $6.4 trillion in 2025 by eMarketer , that census now covers a fifth of all retail.

The researcher’s jobs-to-be-done

• Market sizing. Count distinct products, sellers, and brands in a category and a country, weight by review_count as a demand proxy, and estimate addressable volume from the actual catalog rather than a top-down guess.
• Pricing distribution. Pull the full spread of price for a category to compute median, quartiles, and the premium-vs-value split — and see how it shifts month over month.
• Trend and share tracking. Follow how brand share, new-SKU velocity, and average rating move over time to call a category rising or fading before it shows up in lagging retail reports.

This breadth-first work is what competitive intelligence delivers — full-category coverage across markets, not a single-SKU lookup.

Why researchers pull data rather than scrape it

Credible market research needs scale, consistency, and cross-country comparability. Hand-scraping a few categories yields a biased, unrepeatable sample and breaks the moment a marketplace changes layout. ShopAPIS returns one normalized schema across every marketplace and country, with currency and country on each record, so you can compare a category in Germany against the US without reconciling a dozen formats. Review counts and ratings come pre-parsed, turning a months-long collection project into a query you can re-run quarterly for a clean time series.

Example: a category pricing snapshot

You aggregate a category in one country and ShopAPIS returns the distribution-ready data:

{
  "category": "Robot Vacuums",
  "country": "US",
  "currency": "USD",
  "product_count": 1842,
  "seller_count": 367,
  "price": { "median": 249.0, "p25": 159.0, "p75": 429.0, "min": 79.0, "max": 1299.0 },
  "top_brands_by_review_volume": [
    { "brand": "iRobot", "review_count_total": 412980, "avg_rating": 4.4 },
    { "brand": "Roborock", "review_count_total": 298400, "avg_rating": 4.6 },
    { "brand": "Eufy", "review_count_total": 176220, "avg_rating": 4.3 }
  ],
  "fetched_at": "2026-06-05T08:30:00Z"
}

From one response you have the price spread, the long tail of 367 sellers, and a demand-weighted brand ranking — the spine of a market report, reproducible next quarter.

Cross-border comparison is the differentiator

The hardest market-research questions are comparative: is the premium tier growing faster in Germany than the US, is a brand that dominates one country a non-entity in another, where is white space for a new entrant. Because every ShopAPIS record carries country and currency and shares one schema across 30+ countries, you can line up the same category in five markets and compare medians, brand share, and review velocity directly — no per-country scraper, no manual currency reconciliation. That turns a fragmented, country-by-country collection effort into a single comparable dataset, which is the part traditional research panels cannot deliver at marketplace scale.

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