Field data (also real user monitoring, RUM) is performance measured from actual visitors' devices and connections as they use a site — the basis for how Core Web Vitals are assessed for ranking, aggregated at the 75th percentile over a trailing 28-day window.
Definition
Field data (also real user monitoring, RUM) is performance measured from actual visitors' devices and connections as they use a site — the basis for how Core Web Vitals are assessed for ranking, aggregated at the 75th percentile over a trailing 28-day window. Lab data is performance measured in a controlled test on a fixed device and network profile, run on demand, giving a repeatable diagnostic snapshot.
Google's field dataset for eligible pages is the Chrome User Experience Report (CrUX).
The two answer different questions: field data tells you what real users actually experience and what counts for the page-experience signal; lab data tells you why, in a controlled way you can iterate against. They often disagree, because real users have slower devices, worse networks, and varied interaction patterns that a fast lab test does not capture.
In context
For an iGaming affiliate, the field-versus-lab distinction matters because a page can pass a lab test run from a fast connection while failing in the field for a real mobile-majority audience on mid-range phones and mobile networks — which is exactly the audience this niche serves. Optimising to a green lab score and assuming the job is done is a common mistake; the metric that affects the page-experience signal is the field data.
The practical approach is to monitor field data (CrUX for the site, or a RUM tool for more granular and faster feedback), use it to identify which page types and metrics are actually failing for real users, and then use lab tools to diagnose and iterate on fixes for those specific pages. After deploying a fix, field data takes weeks to reflect it (the 28-day trailing window), so lab tests confirm the change worked immediately while field data confirms it stuck.
Field data can also be sparse for low-traffic pages, where lab testing and site-level trends have to fill in. For affiliate-facing content, the framing is that field data is real-user performance and is what counts for ranking, that lab data is a repeatable diagnostic, that they often disagree because real users have slower devices and networks, and that the right workflow is to find failures in the field and diagnose and fix them in the lab, then wait for the field data to catch up.
Worked example
An affiliate's review pages show green lab scores but fail LCP in CrUX field data because real users are on slower phones and networks than the lab profile. The team uses lab tools to fix the LCP image and render-blocking CSS on those pages, confirms the improvement in the lab immediately, and sees the field data pass about three weeks later as the trailing window updates.
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