I've been writing about page size and complexity for years. If you've been working in the performance space for a while and you hear me start to talk about page growth, I'd forgive you if you started running away. ;)
But pages keep getting bigger and more complex year over year – and this increasing size and complexity is not fully mitigated by faster devices and networks, or by our hard-working browsers. Clearly we need to keep talking about it. We need to understand how ever-growing pages work against us. And we need to have strategies in place to understand and manage our pages.
In this post, we'll cover:
Chances are, you're here because of Google's update to its search algorithm, which affects both desktop and mobile, and which includes Core Web Vitals as a ranking factor. You may also be here because you've heard about the most recent potential candidates for addition to Core Web Vitals, which were just announced at Chrome Dev Summit.
A few things are clear:
If you're new to Core Web Vitals, this is a Google initiative that was launched in early 2020. Web Vitals is (currently) a set of three metrics – Largest Contentful Paint, First Input Delay, and Cumulative Layout Shift – that are intended to measure the loading, interactivity, and visual stability of a page.
When Google talks, people listen. I talk with a lot of companies and I can attest that, since Web Vitals were announced, they've shot to the top of many people's list of things to care about. But Google's prioritization of page speed in search ranking isn't new, even for mobile. As far back as 2013, Google announced that pages that load slowly on mobile devices would be penalized in mobile search.
Keep reading to find out:
If you want to understand how people actually experience your site, you need to monitor real users. The data we get from real user monitoring (RUM) is extremely useful when trying to get a grasp on performance. Not only does it serve as the source of truth for your most important budgets and KPIs, it help us understand that performance is a broad distribution that encompasses many different cohorts of users.
While real user monitoring gives us the opportunity for unparalleled insight into user experience, the biggest challenge with RUM data is that there's so much of it. Navigating through all this data has typically been done by peeling back one layer of information at a time, and it often proves difficult to identify the root cause when we see a change:
"What happened here?"
"Did the last release cause a drop in performance?"
"How can I drill down from here to see what's going on?"
"Is the issue confined to a specific region? Browser? Page?"
Today we're excited to release a new capability – your RUM Sessions dashboard – which allows you to drill into a dataset and explore those sessions that occurred within a given span of time.
I love conversations about performance, and I'm fortunate enough to have them a lot. The audience varies. A lot of the time it’s a front-end developer or head of engineering, but more and more I’m finding myself in great conversations with product leaders. As great as these discussions can be, I often walk away feeling like there was a better way to streamline the conversation while still conveying my passion for bringing fellow PMs into the world of webperf. I hope this post can serve that purpose and cover a few of the fundamental areas of web performance that I’ve found to be most useful while honing the craft of product management.
So, whether you are a PM or not, if you're new to performance I've put together a few concepts and guidelines you can refer to in order to ramp up quickly. This post covers:
Let's get started...
Getting up to speed on Core Web Vitals seems to be at the top of everyone's to-do list these days. Just in time for the holidays, we are happy to bring you our new Vitals dashboard to help you get a huge jumpstart.
We love to visualize performance data (in case you haven't heard). We love it even more when we can be true to one of our biggest motivations at SpeedCurve: leveraging both RUM and Synthetic data to help you take action on what matters most.
We’ve been pretty vocal about Core Web Vitals since Google announced this initiative last spring. We love the idea of having a lean, shared set of metrics that we can all rally around – not to mention having a broader conversation about web performance that includes teams throughout an organization.
For many site owners, the increased focus on Core Web Vitals is driven by the fact that Google will be including them as a factor in search ranking in May 2021. Other folks are more interested in distilling the extremely large barrel of performance metrics into an easily digested trinity of guidelines to follow in order to provide a delightful user experience.
We’ve had some time to evaluate and explore these metrics, and we're committed to transparently discussing their pros and cons.
The purpose of this post is to explore First Input Delay (FID). This metric is unique among the three Web Vitals in that it is can only be measured using real user monitoring (RUM), while the other two (Largest Contentful Paint and Cumulative Layout Shift) can be measured using both RUM and synthetic monitoring.
In this post we'll cover:
Let's dig in!
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:
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.
One of the big challenges with Google's new Cumulative Layout Shift (CLS) metric is understanding which elements actually moved on the page, when they moved, and by how much. To help with debugging your CLS scores, we've added a new visualization to SpeedCurve that shows each layout shift and how each individual shift adds up to the final cumulative metric.
For each layout shift, we show you the filmstrip frame right before and right after the shift. We then draw a red box around the elements that moved, highlighting exactly which elements caused the shift. The Layout Shift Score for each shift also helps you understand the impact of that shift and how it adds to the cumulative score.
Is your site fast? Adding to a string of recent announcements including Lighthouse v6 and Core Web Vitals, Google has introduced Fast page labelling in Chrome 85 for Android. If you are curious about what this means for your site and how you can get in front of it, read on!
I’m really excited to have become the latest member of the SpeedCurve family. I’ve known Mark for 20 years, since he started lecturing in design here in New Zealand. I was one of his first students and we’ve always kept in touch. Our careers have overlapped at various points, it’s the nature of a small country with an even smaller web design industry. I remember attending the Webstock conference where Mark first presented SpeedCurve in the Start-Up Alley competition. He won, netting the prize money and a trip to the USA to present his new startup. We all know how relevant and useful his startup has become and I’m thrilled to have this opportunity to get involved in its development.
Google recently announced an initiative called 'Web Vitals', which focuses on three performance metrics they consider essential for improving the user experience:
With the exception of FID, all of these metrics are available in both RUM and Synthetic. FID requires a real user for calculation and therefore is only available in RUM. In place of FID for Synthetic, we recommend tracking JS Long Tasks or Total Blocking Time as an alternative CPU metric.
Here at SpeedCurve, the past few months have found us obsessing over how to define and measure user happiness. We've also been scrutinizing JS performance, particularly as it applies to third parties. And as always, we're constantly working to find ways to improve your experience with using our tools. See below for exciting updates on all these fronts.
As always, we love hearing from you, so please send your feedback and suggestions our way!
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We're excited to announce the availability of the First Input Delay metric as part of LUX, SpeedCurve's RUM product.
We're excited to announce that we've launched Last Painted Hero as an official metric. Last Painted Hero is a synthetic metric that shows you when the last piece of critical content is painted. Keep reading to learn how Last Painted Hero works, why (and how) we created it, and how it can help you understand how your users perceive the speed of your pages.
When choosing the right performance metric, my soapbox for the last few years has been "not every pixel has the same value". In other words, rather than chase dozens of different performance metrics, focus on the metrics that measure what's critical in your page.
Here at SpeedCurve, we think it's good to focus on rendering metrics, because they're a closer approximation to what the user experiences. There are some good rendering metrics out there, like start render and Speed Index, but the downside to these metrics is that they give every pixel the same value. For example, if the background renders and some ads render, that could improve your start render time and Speed Index score, but it might not have a big impact on the user's experience. Instead, it's better to measure the parts of the page that matter the most to users. We call those parts of the page the "hero elements".
I’m at Shop.org this week, having really interesting conversations with online retailers. What I love about talking with this crowd is that – like me – they're super focused on user-perceived performance. Not surprisingly, we have a lot to talk about.
Making customers happy is the not-so-secret secret to retail success. Delivering a fast, consistent online experience has been proven to measurably increase every metric retailers care about – from conversions and revenue to retention and brand perception. (In fact, there's so much research in this area, I dedicated an entire chapter in my book to it. You can also find a number of great studies on WPO Stats.)
Delivering great, fast online experiences starts with asking two questions:
The good news is that most of the issues that are making pages slow for your shoppers are right on your pages, which means you have control over them. Here's an overview of the most common performance issues on retail sites, and how you can track them down and fix them.
The key to a good user experience is quickly delivering the content your visitors care about the most. This is easy to say, but tricky to do. Every site has unique content and user engagement goals, which is why measuring how fast critical content renders has historically been a challenging task.
That's why we're very excited to introduce Hero Rendering Times, a set of new metrics for measuring the user experience. Hero Times measure when a page's most important content finishes rendering in the browser. These metrics are available right now to SpeedCurve users.
More on how Hero Rendering Times work further down in this post. But first, I want to give a bit of back story that explains how we got to here.
A couple of month ago, someone asked if I'd written a page bloat update recently. The answer was no. I've written a lot of posts about page bloat, starting way back in 2012, when the average page hit 1MB. To my mind, the topic had been well covered. We know that the general trend is that pages are getting bigger at a fairly consistent rate of growth. It didn't feel like there was much new territory to cover.
Also: it felt like Ilya Grigorik dropped the mic on the page bloat conversation with this awesome post, where he illustrated why the "average page" is a myth. Among the many things Ilya observed after analyzing HTTP Archive data for desktop sites, when you have outliers that weigh in at 30MB+ and more than 90% of your pages are under 5MB, an "average page size" of 2227KB (back in 2016) doesn't mean much.
The mic dropped. We all stared at it on the floor for a while, then wandered away. And now I want to propose we wander back. Why? Because the average page is now 3MB in size, and this seems like a good time to pause, check our assumptions, and ask ourselves:
Is there any reason to care about page size as a performance metric? And if we don't consider page size a meaningful metric, then what should we care about?