Understanding Cumulative Layout Shift (CLS): Is It Truly a Fair Ranking Factor?
As Google’s Core Web Vitals prepare to influence search rankings starting in 2021, web developers and SEO professionals are paying close attention to metrics that affect user experience—one of which is Cumulative Layout Shift (CLS). While promising in theory, the practical implications and measurement nuances of CLS raise important questions about its fairness and accuracy as a ranking signal.
What Is Cumulative Layout Shift (CLS)?
CLS measures visual stability by quantifying unexpected layout shifts during page load and interaction. A low CLS score indicates a stable page—elements load and appear as expected—whereas a high score signals disruptive shifts that can frustrate users. Google emphasizes CLS because a stable layout enhances user satisfaction and engagement, both critical to SEO performance.
The Assumption of User-Initiated Changes
Google’s guidelines suggest that CLS partly accounts for user-initiated actions impacting layout stability. This makes intuitive sense; for example, a user clicking a button or tapping a link might naturally trigger layout changes. However, in practice, many high CLS reports stem from interactions that aren’t explicit clicks but still cause layout shifts—such as hovering or scrolling.
Common Causes of Unexpected Layout Shifts
Several scenarios contribute to elevated CLS scores, often stemming from actions that are not direct user clicks:
-
Hover-Triggered Shifts: Hovering over product images, information boxes, or multimedia previews can cause elements to resize or move unexpectedly. For example, enlarging a thumbnail or displaying additional info on hover can inadvertently increase CLS.
-
Lazy Loading Content: As pages load, lazy-loaded images or sections may push content downward, especially if their placeholder space isn’t reserved properly. This can cause shifts during scroll or load completion.
-
Delayed Post-Click Actions: Actions like loading user reviews or expanding sections after a delay can result in layout shifts if they don’t reserve space beforehand.
Real-World Examples from E-Commerce Giants
Examining websites like Amazon reveals how these issues manifest in real-world scenarios. For instance, on Amazon UK’s desktop site:
- Hovering over product images to enlarge them can temporarily cause layout shifts.
- The “Explore more items” section loading in the footer can move page content unexpectedly during scroll.
- Clicking on review counts to view reviews causes the page to scroll, adding to the CLS score.
Similarly, Amazon’s Prime Video pages exhibit high CLS figures (e.g., 2.42 on desktop), predominantly due to hover-induced previews and content loading that isn’t optimally reserved or managed.
Measurement Pitfalls and Google’s Own Tools
Perhaps most surprisingly, Google’s web.dev/measure tool reports a CLS of 0.25 for its own examples. This increased score isn’t due to poor design but rather because the layout shifts occur when the tool loads dynamic content or when results are fetched—essentially, they follow the same issues seen on commercial sites.
The core problem here is measurement design: if space isn’t reserved for dynamic content or adaptive elements, shifts are inevitable and unfairly penalized. A more refined approach would involve reserving ample space upfront, but this practice isn’t always feasible or practical, especially for complex or highly dynamic pages.
Is the Focus on CLS Justified?
Given these insights, one must ask: is CLS an entirely fair or accurate ranking factor? If many high CLS scores are influenced by behaviors beyond the page’s initial load—like hovering or delayed content—then perhaps CLS, as currently measured, includes factors that shouldn’t directly impact perceived user experience.
Furthermore, the variability in CLS scores across different scenarios and sites raises concerns about its consistency and reliability as a ranking metric. It appears that the way CLS is measured and the context of interactions significantly affect the outcome, sometimes unfairly penalizing sites whose layout shifts are incidental or harmless.
Conclusion: Rethinking CLS Metrics and Expectations
While the goal of Core Web Vitals is to improve user experience, the current state of CLS measurement suggests a need for refinement. Developers should focus on reserving space for dynamic content, minimizing unnecessary hover-induced shifts, and understanding that not all layout changes equally impact user perception.
As the digital landscape evolves, ongoing dialogue and experimentation will be essential to ensure that metrics like CLS serve their intended purpose without unfairly penalizing sites for benign or unavoidable shifts.
What are your thoughts on CLS? Have you encountered unexpected layout shifts impacting your site’s performance? Share your experiences and insights below.
