I’m going to explain a bit about the choices I’ve made with this current setup to hopefully answer some questions you might currently have. I also add two blocking triggers to the tag in order to maintain a bit of a cleaner data set one that only allows positive numbers (someone smarter than me will hopefully and a blog comment below explaining how the amount of time captured in the browser could be negative) and the other that limits the upper bound for page load speed to 120,000 milliseconds so that outliers don’t completely wreck averages to the extent that we will averages in analysis. You need the page to have fully loaded before you can capture a value for the “Load End”. I use a lookup table in GTM to map page load speed numbers to strings, which I pad with a leading zero so that alphanumeric sorting works within the GA user interface.Īs far as the GTM triggers are concerned, you’ll need to fire this tag on gtm.load so that you all of the timing fields presenting in the performance.timing object. ( Again, credit to Dan whose inspiration lead to much of this post). The variables you’ll use in GTM are custom javascript variables like the following example:Īlso, the values provided to the Event Tag itself will provide useful data for analysis. Simply create a tag that fires a non-interactive event on every gtm.load event (or a subset of pages, if desired) including the calculated values above as custom metrics. Sending the values to Google Analytics is quite simple via GTM. Since the majority of Analytics Ninja’s clients are using Google Analytics, we use custom metrics to capture timing values. These values are tremendously useful as they allow you to calculate the amount of time it took for the browser to reach each stage of page render. You’ll notice that each value returned is measured as a unix timestamp. For the unfamiliar readers out there, you can see the values that are returned by the API in the browser by typing performance.timing into your browser console. The Performance Timing API allows you to access a very large amount of information with regards to exactly how long it takes for basically everything in the page load process to happen. However, when we want to describe the impact of page load on user experience, the picture (and related measures) becomes much more complex and nuanced.Įnter the browser’s Performance Timing API. Page Load Time” is the default page speed measurement metric in Google Analytics, I totally understand why someone would come to the conclusion that this default metric is THE one to use when describing page load speed. Based upon the metric’s name, and amplified by the fact that “Avg. One of the big mistakes that I’ve seen many people make is that they focus on a metric called “Page Load Time” to evaluate how long it takes a page to load. However, there were two posts that really caught my attention that have served as inspiration for client based work that I’m going to share in this article, namely Dan Wilkerson’s 2016 post on increasing GA Page Speed Hit Limit and Jenn Kunz’s 2019 post about improving Adobe’s performanceTiming plugin. This issue has been addressed by members of the digital analytics community over the years. That means that for medium to large sites, there is a pretty significant data sampling that is happening on your page load metrics.
JBIDWATCHER PAGE LOAD TIME CODE
This can be increased to 100% via a small change in code (setting siteSpeedSampleRate to 100%), but GA still limits the processing of the timing hits to approximately 10,000 per day. The main reason is because Google Analytics samples page timing hits to a default of 1% of all pageviews. So you may be asking yourself, “why is that not sufficient?” Inspiration and Initial Thoughtsįirst of all, most readers of this blog already know that Google Analytics has a whole set of Site Speed metrics. But before you can get to a place where you’re able to answer, “does page load speed matter, and if so, how much?”, we first need to have the capability to measure page load speed effectively. I plan to write a follow up to this blog post exploring to what extent page speed impacts business success based upon our own client data. There is a widely held belief, supported by numerous studies, that having a fast site will put your business on the fast track to making money. The marketing industry seems to be very heavily focused on the topic with a wide range of articles and presentations at conferences focusing on the need for a fast site. One of the most common needs that have come up for Analytics Ninja’s clients in 2019 has been measuring page speed.
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