Archive for the ‘Google Tag Manager’ Category
Perhaps this sounds familiar: Your team has just decided to start transitioning to Google Tag Manager. However, you’re stuck on where you need to place the container code.
Traditionally, you’ve placed the Google Analytics immediately before the closing head tag, or perhaps it’s even still in the footer. (gasp!)
With Google Tag Manager the placement is now a little different. Instead of placing it in the head section, Google recommends putting the container code immediately after the opening body tag. (more…)
If you’re evaluating the performance of your site content, it can help tremendously to segment that content into a variety of cohorts. Unfortunately, many website owners have trouble getting enough information about their content into Google Analytics to help them with their analysis.
Some information may already be available on your website, like information about your page or extra information that gives context to the page.
Ultimately we want to bring these additional dimensions about your content into Google Analytics to help with your analysis. One way to do this is by leveraging Schema and Google Tag Manager.
If you’re still unaware of Schema, it’s a way of marking up your content so that it is recognized by Google and other search providers. This helps search engines to better understand your content, and hopefully deliver it in a more relevant way to people searching on their systems.
Ultimately, it’s about driving more organic visitors to your website. (more…)
Do you know how people are completing forms on your site? Are there certain fields that get skipped frequently or that cause users to drop off?
Almost two years ago, I wrote a post showing how to use a simple script to track form abandonment in Google Analytics with event tracking. I’ve gotten a lot of great user feedback (and requests) about that script, and wanted to share an updated version that is a little more elegant.
This new version more effectively handles fields that are completed or skipped. I’ve also modified this script and included instructions for how to add it to your site through Google Tag Manager.
Use this script to see which fields get the most completions, but also use it to compare to the amount of forms that get submitted successfully. If you find that people are starting to complete the form but failing to submit it, you may need to look into ways to improve the user experience.
Recently, I was working with a client to integrate Visual Website Optimizer (VWO) with Google Tag Manager. I started by following the integration guide on VWO’s support pages, but ran into a few issues that required a creative workaround.
Not only did the timing of the VWO loading present issues, but I found that the specific data that is supposed to made available on the dataLayer wasn’t being made available.
Follow the instructions below to fix both of the problems! (more…)
While Google Tag Manager touts itself as a code free alternative to website development, sometimes a knowledge of basic web mechanics (and a little bit of code!) can help make your setup go much easier!
Whether or not you’ve started using the new version of GTM, this post will help explain how to target clicks on specific html elements like links, images, or buttons. (more…)
As you may have heard, Google Tag Manager has a new interface. You can read the official announcement, but we’ve got 6 of the most important takeaways below. (more…)
Troubleshoot ecommerce analytics by taking a “data snapshot” the moment tracking occurs. Use our code and handy checklist to detect hard-to-find issues.
Something is wrong with your ecommerce analytics data, but you’re not sure exactly what. You’ve checked the tracking code and it looks fine. That means the problem is probably coming from the server side.
Server side code takes transaction details — like products, quantity, and price — and places them where your analytics tracking code can read them.
If the transaction details don’t make sense, you’ll have problems such as transaction revenue with no products, or products with no revenue. If some details are malformed or missing, the tracking code may fail entirely. (more…)
It happens all the time. One day you notice a big, ugly surprise at the top of your top Site Content > All Pages reports: “This report includes a high-cardinality dimension, and some data has been grouped into (other).”
The dreaded (other), also known as the high-cardinality limit.
(other) appears in your content reports when you have more than 50,000 unique pages (75,000 for Premium) that are viewed in any given day. The 50,000th unique page that day will appear as “(other)”, and any other unique pages will be consolidated there.
Stop. It may look like you have 50,000 pages in your reports. But ask yourself: do I really have 50,000 (or 75,000) totally unique pages? That is, do I have 50,000 pages with content exclusive and separate from any other page?
(If so, prepare to have your mind blown a bit further down the page.)
UTM campaign parameters. We love them. We hate them.
They make it easy to track both online and offline marketing efforts. But they aren’t very pretty to look at, and they’re difficult to implement reliably, especially for a layperson (i.e. non-technical person).
Often, there’s a situation where we want to track a number of different approaches or people contributing to a campaign. Imagine the pushback you’ll get when you suggest each person modifies their UTM parameters to personally identify themselves or the approach they’re using.
Fortunately, there’s an easier way to track certain types of activities without having to resort to including all those UTM parameters. We can use a simple URL hash and some Google Tag Manager magic to uniquely identify each person.
Google Analytics export to BigQuery is great for getting at the raw session-level data of Google Analytics. But, it’s only for GA Premium (GAP) subscribers. If you have other reasons to need GAP – like increased sampling limits, DoubleClick integration, or additional custom dimensions — and you have the money to spend, GAP is a great option.
Raw GA data?
But what if you’re not a GAP subscriber? Can you still get the raw, session-level data?
In a word: no (at least not from GA). All of the data in GA reports and in its associated reporting APIs is aggregated data. You can create and export reports full of dimensions and metrics, but there’s no report that can give you all of the information for each session the way BigQuery can. (more…)