At Slice Communications, we’re constantly working to set new standards for measuring the business impact of public relations and social media. Part of that is tagging links and reviewing Google Analytics. We asked our friends at RJ Metrics to give us some insight into how companies can make sure their Analytics are set up correctly so we all have good data to review. We thank Anita Andrews and the rest of the team for their time and expertise putting together this guest blog.
Guest Blogger: Anita Andrews, RJ Metrics
Getting the implementation of Google Analytics right is step one in measuring the ROI of your marketing and PR efforts. In spite of the Google Analytics’ prevalence and importance, I’ve seen many businesses get the basics wrong. In this post I’m going to share six of the most common mistakes I’ve seen people make with their implementations, and provide some guidance on how to fix them.
Mistake #1: Putting tracking code in the wrong place on the page
It’s been over four years ago since Google Analytics made the update from synchronous to asynchronous tracking code. But I continue to encounter companies that haven’t made the switch.
The synchronous code (old version) was placed at the bottom of a page. This was because the code would prevent other web page content from loading. Analytics are important, but most website owners weren’t willing to lose a visitor while that person waited for the tracking code to load.
Google’s asynchronous code allows the tracking code to work its magic in the background while the rest of your site loads uninterrupted.
How to fix this: Update your tracking code. Learn how to do this here.
Mistake #2: Treating external link clicks as bounces
Your bounce rate is a key metric in spotting where your site is losing the interest of readers. The problem is that if you did your implementation wrong, Google Analytics will be tracking clicks on external links as bounces.
Here’s an example from our own blog. We wrote a post about our new customer lifetime value calculator. In the third paragraph is a link to the calculator, hosted on one of our microsites. We want visitors to click that link even though it means leaving our site. In this scenario, a visitor leaving your site isn’t a bounce that says “your site isn’t working for me” — it’s a visitor engaging with the content. Make sure your Google Analytics are set up to recognize that difference.
How to fix this: Google Analytics allows you choose whether outbound links are considered interaction events or not. To keep your bounce rates a clean metric on site underperformance, exclude external clicks from being counted. Learn how to do this here.
Mistake #3: Ignoring pop-up modals
Modals are a great way to collect sign-ups. Here’s an example of a modal pop-up on the Conversion XL blog:
You should be tracking the conversion rate on these modals using event tracking. This is something that requires an extra step to set up, but without it, you’ll have no insight on how the pop-up is impacting customer behavior.
How to fix this: Google provides information on how to set up event tracking here.
Mistake #4: Missing out on UTM parameters
Technically speaking, UTM tagging has nothing to do with the implementation of your Google Analytics, but it has plenty to do with the amount of value you can get out of your Google Analytics.
UTM parameters give you insight on what your placed links (i.e., ad links, press releases, YouTube videos) are driving traffic to your site. But if you’re not tagging links consistently, you won’t have access to this data. This is huge missed opportunity to gain valuable insight on what marketing efforts are paying off.
How to fix this: Use Google’s URL builder and follow these tips on UTM tagging.
Editor’s Note: Slice also blogged about this recently.
Mistake #5: Counting logged in users the same as non-logged in users
If you have a website that visitors can log into (I’m looking at you, SaaS companies) than this might be a mistake that you’re making. Users with login information will behave very differently than other users. Failing to treat them as their own segment will muddy your insights on bounce rates, pageviews, and time on site.
This is another mistake that can be solved with event tracking. Once these events are tracked you can set up a custom segment in your reports to filter out visits that include a login.
How to fix this: Google provides information on how to set up event tracking here.
Mistake #6: Failing to implement cross-domain tracking
There are several scenarios where you might want to track multiple domains. The most common example is if you utilize multiple subdomains of your primary domain: blog, support, and www are some very common ones. And if you’re a SaaS company, your product will likely live at a domain like app (ours is dashboard). When this happens, you want to be sure that you’re properly tracking how visitors move across those subdomains.
Google gives you two options on how to do this. You can set up a single property and use views (formerly known as profiles) to filter out separate subdomains. The other option is to set up separate properties for each subdomain. It doesn’t particularly matter which route you choose, but if you go with the latter you need to set up cross-domain tracking.
Cross-domain tracking will ensure that traffic moving between your different domains is not viewed as “new” traffic. You want your analytics to recognize a cross-domain visitor as being part of the same visit, otherwise you’ll deflate your time on site and inflate your new visitors.
How to fix this: Learn how to set up cross-domain tracking here.
The Future of Web Analytics
The world of web analytics is evolving. We just covered some of the things that are important to be doing today, but it’s never too early to start thinking about tomorrow. The wave of Universal Analytics adoption is growing and with it, deeper insight on how your customers behave across both web and mobile.
Author Bio: Anita Garimella Andrews
Anita Garimella Andrews, VP of Client Analytics Services at RJMetrics, has analyzed the data of over 300 companies throughout the course of her career. She’s forgotten more about data than most have ever learned. Anita gets annoyed with big data hype, preferring to focus on helping clients with what matters: making better decisions with the data they have.