How are Core Web Vitals measured? How do you know your fixes have had the desired effect and when will you see the results in Google Search Console? The Core Web Vitals are a set of three metrics designed to measure the “core” experience of whether a website feels fast or slow to the users, and so gives a good experience. Web pages will need to be within the green measurements for all three Core Web Vitals to benefit from any ranking boost. Let’s figure it out.
How are Core Web Vitals measured? How do you know your fixes have had the desired effect and when will you see the results in Google Search Console? Let’s figure it out.
Google has announced that from May 2021 (edit: the date was just moved to June 2021), they will start to consider “Page Experience” as part of Search ranking, as measured by a set of metrics called Core Web Vitals. That date is approaching quickly and I’m sure lots of us are being asked to ensure we are passing our Core Web Vitals, but how can you know if you are?
Answering that question is actually more difficult than you might presume and while lots of tools are now exposing these Core Web Vitals, there are many important concepts and subtleties to understand. even the Google tools like PageSpeed Insights and the Core Web Vitals report in Google Search Console seem to give confusing information.
Why is that and how can you be sure that your fixes really have worked? How can you get an accurate picture of the Core Web Vitals for your site? In this post, I’m going to attempt to explain a bit more about what’s going on here and explain some of the nuances and misunderstandings of these tools.
The Core Web Vitals are a set of three metrics designed to measure the “core” experience of whether a website feels fast or slow to the users, and so gives a good experience.
The three Core Web Vitals metrics (Large preview)
Web pages will need to be within the green measurements for all three Core Web Vitals to benefit from any ranking boost.
This metric is probably the easiest understood of these — it measures how quickly you get the largest item drawn on the page — which is probably the piece of content the user is interested in. This could be a banner image, a piece of text, or whatever. The fact that it’s the largest contentful element on the page is a good indicator that it’s the most important piece. LCP is relatively new, and we used to measure the similarly named First Contentful Paint (FCP) but LCP has been seen as a better metric for when the content the visitor likely wants to see is drawn.
LCP is supposed to measure loading performance and is a good proxy for all the old metrics we in the performance community used to use (i.e. Time to First Byte (TTFB), DOM Content Loaded, Start Render, Speed Index) — but from the experience of the user. It doesn’t cover all of the information covered by those metrics but is a simpler, single metric that attempts to give a good indication of page load.
This second metric measures the time between when the user interacts with a page, clicking on a link or a button for example, and when the browser processes that click. It’s there to measure the interactivity of a page. If all the content is loaded, but the page is unresponsive, then it’s a frustrating experience for the user.
An important point is that this metric cannot be simulated as it really depends on when a user actually clicks or otherwise interacts with a page and then how long that takes to be actioned. Total Blocking Time (TBT) is a good proxy for FID when using a testing tool without any direct user interaction, but also keep an eye on Time to Interactive (TTI) when looking at FID.
A very interesting metric, quite unlike other metrics that have come before for a number of reasons. It is designed to measure the visual stability of the page — basically how much it jumps around as new content slots into place. I’m sure we’ve all clicked on an article, started reading, and then had the text jump around as images, advertisements, and other content is loaded.
This is quite jarring and annoying for users so best to minimize it. Worse still is when that button you were about to click suddenly moves and you click another button instead! CLS attempts to account for these layout shifts.
One of the key points to understand about Core Web Vitals is that they are based on field metrics or Real User Metrics (RUM). Google uses anonymized data from Chrome users to feedback metrics and makes these available in the Chrome User Experience Report (CrUX). That data is what they are using to measure these three metrics for the search rankings. CrUX data is available in a number of tools, including in Google Search Console for your site.
The fact that RUM data is used, is an important distinction because some of these metrics (FID excepted) are available in synthetic or “lab-based” web performance tools like Lighthouse that have been the staple of web performance monitoring for many in the past. These tools run page loads on simulated networks and devices and then tell you what the metrics were for that test run.
So if you run Lighthouse on your high-powered developer machine and get great scores, that may not be reflective of what the users experience in the real world, and so what Google will use to measure your website user experience.
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