E-commerce data for ad & e-commerce agencies

If you run e-commerce or retail-media for clients, your job is to prove performance and stay ahead of each client’s competitors — across many brands at once, without standing up custom data plumbing for every account. ShopAPIS is the shared data layer behind your dashboards: it returns price, availability, seller, rating, review_count and the listing details for any client’s SKUs and their rivals across 70+ marketplaces in 30+ countries, so client reporting and competitive monitoring run on one feed instead of a dozen manual checks.

An agency manages many clients in parallel, so the winning data layer is one API that serves every account’s reporting and competitor monitoring — not a bespoke scraper per brand.

The agency’s jobs-to-be-done

• Client reporting. Pull each client’s live price, availability, Buy Box position, and rating on a schedule to populate share-of-shelf, price-competitiveness, and review-health widgets in your monthly deck — with timestamps that make the numbers defensible.
• Competitor monitoring. Track each client’s named competitors’ prices, promotions (was_price), and stockouts so you can flag a rival’s markdown or a launch the week it happens and adjust the client’s bidding or pricing advice. That maps to competitive intelligence.
• Reputation tracking. Watch ratings and review velocity across a client’s listings with review analytics so a 1-star spike becomes a flagged action item, not a surprise in the QBR.

Why agencies standardize on one API

Agencies scale by reusing infrastructure across accounts. Maintaining per-marketplace scrapers for every client is the opposite — it multiplies a fragile, high-maintenance dependency (block rates on these targets commonly exceed 50%) by your client count. ShopAPIS gives you one schema, one auth, one billing relationship covering every client, every marketplace, and every country, so onboarding a new account is a config change, not an engineering project. Your analysts spend time on insight and client strategy; the data arrives normalized and dated.

Example: a multi-client monitoring pull

One scheduled job covers several accounts at once, each with its competitor set:

{
  "report_date": "2026-06-05",
  "accounts": [
    {
      "client": "Acme Audio",
      "marketplace": "amazon",
      "client_price": 279.0,
      "client_rating": 4.6,
      "share_of_buy_box": 0.82,
      "competitors": [
        { "brand": "SonicWave", "price": 259.0, "was_price": 299.0, "availability": "in_stock" }
      ]
    },
    {
      "client": "BrightHome",
      "marketplace": "walmart-marketplace",
      "client_price": 149.0,
      "client_rating": 4.2,
      "share_of_buy_box": 0.55,
      "competitors": [
        { "brand": "NestLite", "price": 139.0, "availability": "low_stock" }
      ]
    }
  ],
  "fetched_at": "2026-06-05T08:30:00Z"
}

One response feeds two client decks: Acme’s main rival just cut price 40 dollars, and BrightHome’s competitor is low on stock — both surfaced the same morning for two separate accounts.

Retail media depends on this data too

For agencies running sponsored-product and retail-media campaigns — a channel eMarketer projects at $169 billion in global ad spend in 2025  — the same feed informs the spend, not just the deck. A client whose Buy Box share drops or whose competitor launches a markdown should not be pushed harder on ads until the listing is fixed — bidding into a losing offer wastes budget. By reading share_of_buy_box, availability, and competitor price before you adjust campaigns, you tie media decisions to the actual shelf state across every account. That connection between live marketplace data and ad spend is what lets an agency defend its retainer with outcomes rather than impressions, and it scales to every client off the one normalized feed.

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