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Mobile INP performance: The elephant in the room

Earlier this year, when Google announced that Interaction to Next Paint (INP) will replace First Input Delay (FID) as the responsiveness metric in Core Web Vitals in *gulp* March of 2024, we had a lot to say about it. (TLDR: FID doesn't correlate with real user behavior, so we don't endorse it as a meaningful metric.)

Our stance hasn't changed much since then. For the most part, everyone agrees the transition from FID to INP is a good thing. INP certainly seems to be capturing interaction issues that we see in the field.

However, after several months of discussing the impending change and getting a better look at INP issues in the wild, it's hard to ignore the fact that mobile stands out as the biggest INP offender by a wide margin. This doesn't get talked about as much as it should, so in this post we'll explore:

  • The gap between "good" INP for desktop vs mobile
  • Working theories as to why mobile INP is so much poorer than desktop INP
  • Correlating INP with user behavior and business metrics (like conversion rate)
  • How you can track and improve INP for your pages

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Does Interaction to Next Paint actually correlate to user behavior?

Earlier this year, Google announced that Interaction to Next Paint (INP) is no longer an experimental metric. INP will replace First Input Delay (FID) as a Core Web Vital in March of 2024.

Now that INP has arrived to dethrone FID as the responsiveness metric in Core Web Vitals, we've turned our eye to scrutinizing its effectiveness. In this post, we'll look at real-world data and attempt to answer: What correlation – if any – does INP have with actual user behavior and business metrics?

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10 things I love about SpeedCurve (that I think you'll love, too)

This month, SpeedCurve enters double digits with our tenth birthday. We're officially in our tweens! (Cue the mood swings?)

I joined the team in early 2017, and I'm blown away at how quickly the years have flown by. Every day, I marvel at my great luck in getting to work alongside an amazing team to build amazing tools to help amazing people like you!

In the spirit of celebration, I thought it would be fun to round up my ten favourite things to do in SpeedCurve (that I think you'll like, too). Keep scrolling to learn how to:

  1. Fight regressions and stay fast
  2. See the impact of performance on your business
  3. Benchmark your site against your competitors
  4. Track third parties to make sure they're not quietly hurting performance
  5. Make sure you're tracking the best metrics for your pages
  6. Get a prioritized list of performance recommendations
  7. Bookmark and compare synthetic tests and RUM sessions so you can quickly find and fix performance issues
  8. Run A/B tests so you see how code changes affect your performance and user engagement metrics
  9. Get customized weekly reports
  10. Motivate your team with a wall-mounted monitor showing your favourite charts 

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Exploring performance and conversion rates just got easier

Demonstrating the impact of performance on your users – and on your business – is one of the best ways to get your company to care about the speed of your site.

Tracking goal-based metrics like conversion rate alongside performance data can give you richer and more compelling insights into how the performance of your site affects your users. This concept is not new by any means. In 2010, the Performance and Reliability team I was fortunate enough to lead at Walmartlabs shared our findings around the impact of front-end times on conversion rates. (This study and a number of other case studies tracked over the years can be found at WPOstats.)

Setting up conversion tracking in SpeedCurve RUM is fairly simple and definitely worthwhile. This post covers:

  • What is a conversion?
  • How to track conversions in SpeedCurve
  • Using conversion data with performance data for maximum benefit
  • Conversion tracking and user privacy

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Why you need to know your site's performance plateau (and how to find it)

"I made my pages faster, but my business and user engagement metrics didn't change. WHY???"

"How do I know how fast my pages should be?"

"How can I demonstrate the business value of performance to people in my organization?"

If you've ever asked yourself any of these questions, then you could find the answers in identifying and understanding the performance plateau for your site.

What is the "performance plateau"?

The performance plateau is the point at which changes to your website’s rendering metrics (such as Start Render and Largest Contentful Paint) cease to matter because you’ve bottomed out in terms of business and user engagement metrics.

In other words, if your performance metrics are on the performance plateau, making them a couple of seconds faster probably won't help your business.

The concept of the performance plateau isn't new. I first encountered it more than ten years ago, when I was looking at data for a number of sites and noticed that – not only was there a correlation between performance metrics and business/engagement metrics – there was also a noticeable plateau in almost every correlation chart I looked at. 

A few months ago someone asked me if I've done any recent investigation into the performance plateau, to see if the concept still holds true. When I realized how much time has passed since my initial research, I thought it would be fun to take a fresh look.

In this post, I'll show how to use your own data to find the plateau for your site, and then what to do with your new insights.

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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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