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NEW! RUM Compare dashboard

Exploring real user (RUM) data can be a hugely enlightening process. It uncovers things about your users and their behavior that you never might have suspected. That said, it's not uncommon to spend precious time peeling back the layers of the onion, only to find false positives or uncertainty in all that data.

At SpeedCurve, we believe a big part of our job is making your job easier. This was a major driver behind the Synthetic Compare dashboard we released last year, which so many of you given us great feedback on.

As you may have guessed, since then we've been hard at work coming up with the right way to explore and compare your RUM datasets using a similar design pattern. Today, we are thrilled to announce your new RUM Compare dashboard!

With your RUM Compare dashboard, you can easily generate side-by-side comparisons for any two cohorts of data. Some of the many reasons you might want to do this include:

  • Improve Core Web Vitals by identifying the tradeoffs between pages that have different layout and construction
  • Triage a performance regression related to the latest change or deployment to your site by looking at a before/after comparison
  • Explore and compare different out-of-the-box cohorts, such as device types, geographies, page labels, and more
  • Analyze A/B tests or experiments to understand which had the most impact on user behavior, as well as performance 
  • Optimize your funnel by understanding differences between users that convert or bounce from your site and users who don't
  • Evaluate CDN performance by exploring the impact of time-of-day traffic patterns

Let's take a tour...

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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.

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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. 

Load Time vs Bounce Rate

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