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Working with ecommerce data, in depth.

Long-form articles on matching, pricing, research and catalog work: how each job is done, where the data comes from and what to check before you rely on it.

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  1. Best ecommerce data APIs in 2026: 13 options compared

    An ecommerce data API returns product, price and seller records as JSON. We compare 13 on coverage, access, published price and best use, checked 25 Oct 2026.

  2. Best MCP servers for product and price data in 2026

    An MCP server lets an AI agent call a data tool directly. We compare 12 MCP servers for product and price data on tools, access, pricing and best use.

  3. Build a price tracker with an AI agent: Python, datawebot MCP and SQLite

    Build a Python price tracker without writing a scraper: find products across US shops through an MCP server, keep dated rows in SQLite and report changes.

  4. Competitor price tracking across US retailers, with an AI agent

    How to track competitor prices across US retailers: build a basket, match the exact item, record the total price, set a check rhythm, and let an agent pull it.

  5. Price tracking APIs: what they return and what they don't

    A price tracker API can mean a search snapshot, seller offers or a stored price history. What each returns, a real sample payload and a checklist to choose.

  6. Ecommerce analytics tools: 16 picks, sorted by the job they do

    Ecommerce analytics tools do four jobs: store analytics, marketplace research, competitor price tracking and data APIs. Pick the job first, then the tool.

  7. Ecommerce market research: how to use marketplace data

    How to use marketplace listings for ecommerce market research: what the data can answer, a step-by-step method, the checks to run, and its honest limits.

  8. Price intelligence and digital shelf analytics: what they measure and how to get the data

    Price intelligence tracks competitor prices and promotions. Digital shelf analytics tracks how products appear online. Metrics, data and build vs buy.

  9. Product data enrichment: how to fix incomplete product catalogs

    Product data enrichment fills missing or wrong catalog fields like GTIN, brand, size and color from verified sources. Fields, sources, workflow, AI risks.

  10. Product matching: how to match the same SKU across stores and marketplaces

    Product matching links listings of the same item across stores. Why it is hard, the main methods, how to measure match quality, and a practical workflow.

  11. What is a minimum advertised price, and how do you monitor it?

    What a minimum advertised price is, how it differs from MSRP and resale price maintenance, and how MAP monitoring finds and documents violations.