Core Web Vitals are three measurements Google collects from real visitors to your site. They are a genuine ranking input, but that undersells them: they measure the moments where visitors give up, which costs you business whether or not a search engine is watching.
LCP: Largest Contentful Paint
How long until the biggest thing on screen, usually the hero image or headline, has finished rendering. Target: under 2.5 seconds. This is the number most small business sites fail, and almost always for the same reason: an enormous unoptimised hero image, or a font that blocks text from painting.
- Serve the hero as WebP or AVIF, sized for the device rather than the largest possible screen.
- Preload the LCP image; let everything below the fold load lazily.
- Use font-display: swap so text paints in a fallback rather than waiting invisibly.
- Remove render-blocking scripts from the head.
INP: Interaction to Next Paint
How long after a tap or click before the page visibly responds. Target: under 200 milliseconds. This replaced First Input Delay because it measures every interaction, not just the first, which is a much harder and much fairer test.
Poor INP is almost always too much JavaScript competing for the main thread. Break long tasks up, defer what is not needed for the first screen, and audit third-party tags, because chat widgets and tag managers are frequent offenders.
CLS: Cumulative Layout Shift
How much the page jumps around while loading. Target: under 0.1. This is the one that makes people tap the wrong button, and it is also the easiest to fix permanently.
- Set width and height, or an aspect-ratio, on every image and video.
- Reserve space for anything injected later: banners, cookie notices, ads.
- Never insert content above something the visitor is already reading.
Lab data versus field data
Lighthouse gives you lab data: a simulated run that tells you what to fix. Search Console gives you field data from real visitors on real devices, which is what counts. A site can score well in the lab and still fail in the field, usually because real users are on slower phones than your test machine.
Fix the field numbers. Use the lab tool to find out why they are bad.
