Matt’s publishing a newsletter on the past, present, and future of tracking:
The last 100 years have been a journey to see how to measure ghosts - how to measure the invisible audiences at the end of technological distribution networks. With every decade, these ghosts have come more and more into focus, ending with a the last ten years of social media and digital advertising that has created unimaginable amounts of data about everything we see, read, click and like.
He sees the pendulum swinging the other way now …for those who can afford it:
If there’s one constant in the economics of audience data over the last 100 years, is that we only get free services if we pay for them with our attention. This has been true for commercial radio and television, free newspapers, mobile games and digital content. If we want privacy, we have to pay for it, and not everyone can afford this. Will the right to become a ghost only be for the people with money to buy premium products?
This is a very useful new feature in Calibre, the performance monitoring tool. Now you can get data about just how much third-party scripts are affecting your site’s performance:
The best way of circumventing fear and anxiety around third party script performance is to capture metrics that clearly articulate their performance impact.
A performance budget is a clearly defined limit on one or more performance metrics that the team agrees not to exceed, and that is used to guide design and development.
And I agree about the four attributes required for a performance budget to succeed. It must be:
Concrete
Meaningful
Integrated
Enforceable
The point is not to let the performance budget try to stand on its own, somewhere hidden in company documentation collecting dust. You need to be proactive about making the budget become a part of your everyday work.
A fascinating look at standards in the real world. In this case, it’s the kilogram, which is shedding its French Revolutionary roots in favour of the Planck constant.
Our messy human measurements have transcended their messy humanness; they have been melded with an eternal truth.
Mention of The Master Of The Kilogram reminded me of The Keeper Of Time.
We continued with some more performance work this week. Having already covered some of the nitty-gritty performance tactics like font-loading, image optimisation, etc., we wanted to take a step back and formulate an ongoing strategy for performance.
When it comes to web performance, the eternal question is “What should we measure?” The answer to that question will determine where you then concentrate your efforts—whatever it is your measuring, that’s what you’ll be looking to improve.
I started by drawing a distinction between measurements of quantities and measurements of time. Quantities are quite easy to measure. You can measure these quantities using nothing more than browser dev tools:
overall file size (page weight + assets), and
number of requests.
I think it’s good to measure these quantities, and I think it’s good to have a performance budget for them. But I also think they’re table stakes. They don’t actually tell you much about the impact that performance is having on the user experience. For that, we need to enumerate moments in time:
time to first byte,
time to first render,
time to first meaningful paint, and
time to first meaningful interaction.
There’s one more moment in time, which is the time until DOM content is loaded. But I’m not sure that has a direct effect on how performance is perceived, so it feels like it belongs more in the category of quantities than time.
Next, we listed out all the factors that could affect each of the moments in time. For example, the time to first byte depends on the speed of the network that the user is on. It also depends on how speedily your server (or Content Delivery Network) can return a response. Meanwhile, time to first render is affected by the speed of the user’s network, but it’s also affected by how many blocking elements are on the critical path.
By listing all the factors out, we can draw a distinction between the factors that are outside of our control, and the factors that we can do something about. So while we might not be able to do anything about the speed of the user’s network, we might well be able to optimise the speed at which our server returns a response, or we might be able to defer some assets that are currently blocking the critical path.
Factors
1st byte
server speed
network speed
1st render
network speed
critical path assets
1st meaningful paint
network speed
font-loading strategy
image optimisation
1st meaningful interaction
network speed
device processing power
JavaScript size
So far, everything in our list of performance-affecting factors is related to the first visit. It’s worth drawing up a second list to document all the factors for subsequent visits. This will look the same as the list for first visits, but with the crucial difference that caching now becomes a factor.
First visit factors
Repeat visit factors
1st byte
server speed
network speed
server speed
network speed
caching
1st render
network speed
critical path assets
network speed
critical path assets
caching
1st meaningful paint
network speed
font-loading strategy
image optimisation
network speed
font-loading strategy
image optimisation
caching
1st meaningful interaction
network speed
device processing power
JavaScript size
network speed
device processing power
JavaScript size
caching
Alright. Now it’s time to get some numbers for each of the four moments in time. I use Web Page Test for this. Choose a realistic setting, like 3G on an Android from the East coast of the USA. Under advanced settings, be sure to select “First View and Repeat View” so that you can put those numbers in two different columns.
Here are some numbers for adactio.com:
First visit time
Repeat visit time
1st byte
1.476 seconds
1.215 seconds
1st render
2.633 seconds
1.930 seconds
1st meaningful paint
2.633 seconds
1.930 seconds
1st meaningful interaction
2.868 seconds
2.083 seconds
I’m getting the same numbers for first render as first meaningful paint. That tells me that there’s no point in trying to optimise my font-loading, for example …which makes total sense, because adactio.com isn’t using any web fonts. But on a different site, you might see a big gap between those numbers.
I am seeing a gap between time to first byte and time to first render. That tells me that I might be able to get some blocking requests off the critical path. Sure enough, I’m currently referencing an external stylesheet in the head of adactio.com—if I were to inline critical styles and defer the loading of that stylesheet, I should be able to narrow that gap.
A straightforward site like adactio.com isn’t going to have much to worry about when it comes to the time to first meaningful interaction, but on other sites, this can be a significant bottleneck. If you’re sending UI elements in the initial HTML, but then waiting for JavaScript to “hydrate” those elements into working, the user can end up in an uncanny valley of tapping on page elements that look fine, but aren’t ready yet.
My point is, you’re going to see very different distributions of numbers depending on the kind of site you’re testing. There’s no one-size-fits-all metric to focus on.
Now that you’ve got numbers for how your site is currently performing, you can create two new columns: one of those is a list of first-visit targets, the other is a list of repeat-visit targets for each moment in time. Try to keep them realistic.
For example, if I could reduce the time to first render on adactio.com by 0.5 seconds, my goals would look like this:
First visit goal
Repeat visit goal
1st byte
1.476 seconds
1.215 seconds
1st render
2.133 seconds
1.430 seconds
1st meaningful paint
2.133 seconds
1.430 seconds
1st meaningful interaction
2.368 seconds
1.583 seconds
See how the 0.5 seconds saving cascades down into the other numbers?
Alright! Now I’ve got something to aim for. It might also be worth having an extra column to record which of the moments in time are high priority, which are medium priority, and which are low priority.
Priority
1st byte
Medium
1st render
High
1st meaningful paint
Low
1st meaningful interaction
Low
Your goals and priorities may be quite different.
I think this is a fairly useful framework for figuring out where to focus when it comes to web performance. If you’d like to give it a go, I’ve made a web performance chart for you to print out and fill in. Here’s a PDF version if that’s easier for printing. Or you can download the HTML version if you want to edit it.
I have to say, I’m really enjoying the front-end consultancy work we’ve been doing at Clearleft around performance and related technologies, like offline functionality. I’d like to do more of it. If you’d like some help in prioritising performance at your company, please get in touch. Let’s make the web faster together.
Our efforts to measure and improve UX are packed with tragically ironic attempts to love our users: we try to find ways to improve our app experiences by bloating them with analytics, split testing, behavioral analysis, and Net Promoter Score popovers. We stack plugins on top of third-party libraries on top of frameworks in the name of making websites “better”—whether it’s something misguided, like adding a carousel to appease some executive’s burning desire to get everything “above the fold,” or something truly intended to help people, like a support chat overlay. Often the net result is a slower page load, a frustrating experience, and/or (usually “and”) a ton of extra code and assets transferred to the browser.
Even tools that are supposed to help measure performance in order to make improvements—like, say, Real User Monitoring—require you to add a script to your web pages …thereby increasing the file size and degrading performance! It’s ironic, in that Alanis Morissette sense of not understanding what irony is.
Stacking tools upon tools may solve our problems, but it’s creating a Jenga tower of problems for our users.
This is a great article about evaluating technology.
(Andy feels this is like criticising GDP, but GDP measures something that actually happened, whereas NPS, like horoscopes or tea-leaf readings, rely on clairvoyance.)
A good analysis, but my takeaway was that the article could equally be called Why it’s tricky to measure Client-side Rendering performance. In a nutshell, just looking at metrics can be misleading.
Pre-classified metrics are a good signal for measuring performance. At the end of the day though, they may not properly reflect your site’s performance story. Profile each possibility and give it the eye test.
And it’s always worth bearing this in mind:
The best way to prioritize content by building a static site. Ask yourself if the content needs JavaScript.
A profile of Demetrios Matsakis, keeper of time at U.S. Naval Observatory, America’s equivalent to Greenwich in its importance for timekeeping in the modern world.
Vint Cerf announces M-Lab: an excellent resource which will allow people to find out if and how their internet access is being throttled. Viva l'internet!