Analytics

There are a lot of posts on the internet about cross-domain analytics tracking, subdomain tracking, and even cross-frame (X-frame) tracking. Some are pretty solid, yet many aren’t current and/or accurate, a fair number are just confusing, and only a few of them cover all three topics well. While this does not purport to be “the one post to rule them all”, it can absolutely serve as the definitive answer to how I think about cross-X-tracking and walk most people through implementing a good solution.  

I’m a firm believer in the guideline that, “if you find yourself answering the same question three times or more, you need to make it into a blog post.” By that thinking, this post is years too late, but better late than never! 

Skip To:

• How to Configure Subdomain Tracking
• How to Configure Cross-Domain tracking
• How to Set Up Cross-Frame Tracking

Before we get into “how”, let’s discuss “why”.

If you’re like me, you long ago committed to using Google Tag Manager as your one and only platform for configuring your site’s event tracking (and — though it perhaps isn’t the optimal way to go about it — maybe your structured data markup too). To set this up in GTM, you had to create Custom JavaScript variables that used CSS or jQuery selectors to identify the correct elements on the page, and then create tags that arranged those variables into scripts that would pass data to Google Analytics, and/or to Google Search, in a comprehensible format. Those CSS or jQuery selectors were precisely tuned to the unique HTML of the pages where the tags needed to fire, relying on the signature attributes —like id or class — of the key elements in order to identify them. You might have had to go through a frustrating trial and error process to configure some of those variables so that they would populate correctly every time, on every page, but you got there.

And now you hear that the site is going to be redesigned from top to bottom, and the HTML signatures you relied on are all going to vanish or change. PANIC!

Lead scoring is an imperfect science, and one that will depend on where and who you work with. To “score” a lead means to assign it some value that can be used for evaluation of the lead’s quality later. For example, you could qualify or score a lead based on the lead’s budget. If you are a design agency whose projects typically cost $5K-$10K and the client has a budget of over $10k, then that would be a great lead, while if they had less than $1k, they wouldn’t be able to afford to work with you, and it would be a bad lead. You could also evaluate the lead based on the lead’s title. For example, if you’ve determined that leads close at a higher rate if you’re dealing with a VP or C-Level executive, you could score the lead based on who submitted it. If it’s a VP of Sales, then it’s a Hot lead, while a Junior Analyst might be a Cold lead.

Lead scoring is hard because every business is different and there are no industry standards to score your leads against; you have to use your own in-house data and experience to determine what is a Cold, Warm, or Hot (or Bad, Medium, or Good, or whatever other scoring rubric you’d like to use) lead. Before you start scoring leads, talk to as many people on your sales team as you can to determine what makes a lead Hot or Cold, and what information they use to qualify leads. This type of intel is what will help you decide which metrics to use to score a lead.

With that in mind, if you have the data and can qualify your past leads by a particular characteristic of that lead, then you can significantly improve your analysis of your leads using Google Tag Manager (GTM)  and Google Analytics.