15th September 2020
Core Web Vitals are widely referenced, but often misunderstood. They are not a checklist to chase, nor a shortcut to better rankings. They are a set of measurements that show where real users experience friction on a website.
This article explains what Core Web Vitals are, how Google uses them and how they should be assessed and prioritised in practice. It focuses on what actually matters commercially, where teams should spend their time and when performance issues can reasonably wait.
Core Web Vitals (CWV) are a set of metrics Google uses to measure real user experience on a website. Essentially, they are an objective way for Google to understand how pages actually perform for users.
Core Web Vitals focus on three behaviours that directly affect how people use a site:
These metrics are based on real user data, reflecting what happens on real devices.
Google already evaluates relevance, content quality and authority. Core Web Vitals sit alongside those signals. They do not replace them. They help Google decide which pages deliver a better experience when the intent and content are broadly similar.
While they are a way for Google to measure user experience, they also matter commercially. This is because slow, unstable or unresponsive pages lose users before they convert.
From an SEO perspective, Core Web Vitals act as a supporting signal. They rarely rescue weak content or poor relevance. They can, however, hold back otherwise strong pages if the experience is consistently poor.
Google focuses on three measurable behaviours that reflect how users actually experience a page. Together, they cover loading speed, visual stability and responsiveness. Each metric has clear thresholds that indicate whether a page is helping or hindering user journeys.
Largest Contentful Paint (LCP) measures how long it takes for the main content on a page to become visible and usable. The scores are broken down like this:
Good: 2.5 seconds or less
Needs improvement: 2.5 to 4 seconds
Poor: Over 4 seconds
Google evaluates this using real user data at the 75th percentile.
If the main content loads late, users hesitate. Decision-making slows, engagement drops and abandonment increases. A page that does not feel ready will not convert.
Poor LCP is usually driven by slow server response, heavy images, render-blocking resources or too much JavaScript competing for early load time.
Cumulative Layout Shift (CLS) measures how much a page layout moves while it is loading. For CLS, the scores are broken down like this:
Good: 0.1 or less
Needs improvement: 0.1 to 0.25
Poor: Over 0.25
Layout shifts break trust. Users mis-click, lose their place and feel less confident completing actions, especially on product and checkout pages.
CLS issues typically come from images without fixed dimensions, late-loading fonts, injected banners or dynamic elements that push content around after the page appears.
Interaction to Next Paint (INP) measures how quickly a page responds after a user interacts with it. Scores can be broken down like this:
Good: 200 milliseconds or less
Needs improvement: 200 to 500 milliseconds
Poor: Over 500 milliseconds
This reflects the overall responsiveness of the page, not a single click.
A page can look fast and still feel broken. Slow interactions create uncertainty and frustration, even when content has already loaded.
Poor INP is usually caused by heavy JavaScript, long-running tasks on the main thread or complex client-side logic triggered by user actions. These patterns tend to affect many interactions, not just one.
Too many teams measure Core Web Vitals without knowing which data Google actually uses. The first thing to be clear on is this: Google bases its assessments on real user data, not lab tests or one-off page checks.
Google surfaces Core Web Vitals data in a small number of places. The primary source is Google Search Console, which reports how groups of pages perform for real users over time. PageSpeed Insights can support this, but only when you focus on its field data, not the simulated scores.
Field data matters most. This data comes from real users on real devices, across a range of network conditions. Lab data has a role, but only as a diagnostic aid. It helps explain why a problem exists. However, it does not tell you how widespread the problem is or whether it affects commercial performance.
Teams often spend time improving Core Web Vitals scores without seeing any commercial return. This is because not every page matters equally, and not every metric needs fixing immediately. Here are the main considerations:
Prioritise pages that drive value: Start with pages that attract the most traffic and carry the strongest intent. Category pages, key product pages and core landing pages usually sit at the top of this list. Improving performance on low traffic or informational pages rarely moves revenue.
Fix systemic issues, not individual pages: If multiple pages share the same problems, focus on the underlying cause. Image handling, font loading, JavaScript execution and layout patterns are common examples. Fixing these once often improves performance across large numbers of URLs.
Know when issues are acceptable short term: Not every Core Web Vitals issue needs an immediate fix. If a page has low traffic, low intent or limited commercial value, a marginal score may be acceptable in the short term. Some improvements are expensive and deliver little return.
Core Web Vitals are most effective when they are treated as a diagnostic tool, not a target in isolation. The real value comes from using them to prioritise improvements on high intent pages, fix systemic problems and avoid wasting time on changes that will not move the needle.
This work should sit within a broader, structured approach to performance, rather than ad hoc fixes or score chasing. For teams looking to address these issues properly, Core Web Vitals should feed directly into a wider page speed optimisation strategy that focuses on measurable impact.
Klaudia Majewska is an SEO Account Manager responsible for planning, executing and reporting on SEO campaigns across a range of clients. Her work focuses on turning strategy into consistent, measurable performance through clear priorities and ongoing optimisation. Klaudia has a strong technical SEO background and works closely with emerging AI-led search formats. She specialises in making sure products and services are structured and presented in ways that perform across both traditional search results and newer AI-driven search experiences.
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