Cumulative Layout Shift: What it measures, when it works (and doesn't), and how to use it
Back in May, we shared that SpeedCurve supports Google's Core Web Vitals in both our synthetic monitoring and real user monitoring tools. Two of the Web Vitals – Largest Contentful Paint (LCP) and First Input Delay (FID) – were actually available in SpeedCurve for quite a while prior to the announcement. The newcomer to the scene was Cumulative Layout Shift (CLS), and, not surprisingly, it's the metric that's gotten the most questions.
A few of the questions I've been asked (or asked myself) about Cumulative Layout Shift:
- What does CLS measure?
- How is it calculated?
- What does it mean in terms of actual user experience?
- Does it correlate to user behaviour or business metrics in any measurable way?
- What are the (inevitable) gotchas?
- Ultimately, how much should we care about CLS?
Six months in, I've had a chance to gather and look at a lot of data, talk with customers, and learn from our friends in the performance community. Here's what I've learned so far.
Engagement charts: See correlations between performance and user engagement
One of the best – and worst – things about real user monitoring is that it gives you unparalleled access to massive amounts of user data. The problem is when all this data leads to data indigestion. How do you know where to begin? And how do you know what to leave out in order to present a clear case for performance?
At SpeedCurve, we care about more than just showing you all your data. We want to show you the most important data. And we want to make it easy for you to share that data with people throughout your organization. That’s why we’re excited about the newest addition to our family of visualizations: engagement charts.
