15 posts
Real world examples of how over-optimizing for metrics can be at odds with performance.
Good cautionary reminder of Goodhart's law. This is why it's so important to choose your primary metrics with care, and to make sure everyone understands why.
Jeremy discussing why performance isn't just about business, but actually has impact across several broad categories:
Naturally, I agree.
Philip takes a trip down memory lane with some fun stories from the early Yahoo! days around performance.
But most importantly, he suggests that the SEO carrot has tipped the focus of performance, and not for the better.
Sites that truly care about performance and the business impact of that performance, worked hard to make their sites faster.
This changed when Google started using speed as a ranking signal.
I made a similar point in a revamped version of my "Performance Budgets that Stick" talk the other week. If we want this burst of performance interest to stick, and to have the impact we want it to have for users, we're going to need to make it easier for folks to connect the dots between business metrics and performance.
I've been super keen on getting some sort of way to measure when the accessibility tree is ready ever since first chatting about it with Marcy Sutton 5 years ago or so. Scott has a great post here about why it's important. He's also filed issues on WebPageTest and Lighthouse to get something added. Hopefully we'll see something soon!
The folks at Chrome, talking about the business impact of hitting their Core Web Vitals thresholds:
We analyzed millions of page impressions to understand how these metrics and thresholds affect users. We found that when a site meets the above thresholds, users are 24% less likely to abandon page loads (by leaving the page before it finishes loading).
We also looked specifically at news and shopping sites, sites whose businesses depend on traffic and task completion, and found similar numbers: 22% less abandonment for news sites and 24% less abandonment for shopping sites.
Super interesting insight into how the folks over at Google came up with their new Core Web Vitals—including everything from how they figured out what "good" or "poor" looked like, how they chose which percentiles to look at, and more.
A rather clever way of looking at performance data by breaking it down into histogram "regions".