We analyzed 208,085 webpages to learn more about Core Web Vitals.
First, we established benchmarks for Cumulative Layout Shift, First Input Delay, and Largest Contentful Paint.
Then, we looked into the correlation between Core Web Vitals and user experience metrics (like bounce rate).
Thanks to data provided by WebCEO, we were able to uncover some interesting findings.
Let’s dive right into the data.
Here is a Summary of Our Key Findings:
1. 53.77% of sites had a good Largest Contentful Paint (LCP) score. 46.23% of sites had “poor” or “needs improvement” LCP ratings.
2. 53.85% of websites in our data set had optimal First Input Delay (FID) ratings. Only 8.57% of sites had a “poor” FID score.
3. 65.13% of analyzed sites boasted good optimal Cumulative Layout Shift (CLS) scores.
4. The average LCP of the sites we analyzed clocked in at 2,386 milliseconds.
5. Average FID was 137.74 milliseconds.
6. The mean CLS score was 0.14. This is slightly higher than the optimal score.
7. The most common issues impacting LCP were high request counts and large transfer sizes.
8. Large layout shifts were the #1 cause of poor CLS scores.
9. The most common issue affecting FID was an inefficient cache policy.
10. There was a weak correlation between Core Web Vital scores and UX metrics.
11. We did find that FID did tend to slightly correlate with page views.
53.77% of Websites Had an Optimal Largest Contentful Paint Score
Our first goal was to see how each site performed based on the three factors that make up Google’s Core Web Vitals: Largest Contentful Paint, Cumulative Layout Shift, and First Input Delay.
Specifically, we wanted to determine the percentage of pages that were classified as “good”, “needs improvement” and “poor” inside of each site’s Search Console.
To do this, we analyzed anonymized Google Search Console data from 208k pages (approximately 20k total sites).
Our first task: analyze LCP (Large Contentful Paint). In simple terms, LCP measures how long it takes a page to load its visible content.
Here’s how the sites that we analyzed fared: