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Tooling

Web scraping

Also known as: scraping, data scraping

Web scraping is the automated extraction of data from web pages, usually by fetching the HTML and parsing out specific elements into a structured format. It is used to gather information at a scale that manual collection cannot reach, such as prices, listings, reviews or search results. Legality and acceptable use depend on the site's terms, the data involved and local law.

What it is

A scraper requests pages like a browser would, then reads the returned markup to pull out named fields and write them to a spreadsheet, database or API response. Simple scrapers parse static HTML, while others render JavaScript with a headless browser because the content only appears after scripts run. Output is typically CSV or JSON so the data can be joined with other sources.

Why it matters

Search and growth teams depend on data that no single platform exports cleanly: competitor pricing, SERP layouts, catalogue coverage, review sentiment or the contents of AI answers. Scraping turns that scattered public information into something you can trend and act on. It also underpins much of the SEO tool market, so understanding it helps you judge where your vendors' data comes from and how reliable it is.

How it works

Practitioners either build scrapers with libraries and headless browsers, or buy a managed API that handles proxies, rendering and blocking for them. Responsible practice means respecting robots directives and site terms, rate limiting requests, identifying your agent, and caching so you do not re-fetch unchanged pages. Where an official API or data export exists, it is normally the better option because it is stable and sanctioned.

When it applies

Scraping applies when data you need is visible on public pages but not available through a feed, export or API, and the volume makes manual collection impractical. Check terms of service, data protection obligations and whether personal data is involved before you start.

Examples

  • A retailer scrapes competitor product pages nightly to monitor price and stock changes on 500 matched SKUs.
  • An SEO team crawls its own site and extracts schema markup from every template to find pages missing product data.
  • A research team collects the cited sources shown in AI answers for a set of queries to see which publishers are quoted most often.

How it is measured

  • Successful request rate versus blocked, throttled or failed requests
  • Field-level completeness and accuracy in the extracted records
  • Data freshness, measured as time since last successful fetch per page
  • Cost per thousand pages fetched, including proxy and rendering overhead

Related terms in Tooling

Primary research · August 2026

How ChatGPT Shortlists Software Brands

An audit across 10 categories and 60 buying questions. I recorded what ChatGPT reads, throws away and links to when a buyer asks it which software to buy, and what that decides.

60
Questions asked
10
Software markets
2,680
Results read
367
Links shown
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