• Joseph Lowe

Cleaner Data and Conversions for SEOs

Updated: Apr 26, 2020

As an SEO/SEM strategist formally for agencies and currently in-house, I’ve learned a great deal about the different insights that internal vs. external stakeholders seek from data.

For small business owners, the questions often revolve around the high-level insights. Why is my bounce rate throughout the site so high? Is there an optimal average session duration time? Am I getting an adequate amount of traffic to the site in comparison to other clients, and what’s my best-performing referral traffic?

For company executives and internal stakeholders, the questions often dive into more granular subjects around return on investment (ROI). Which Analytics campaign produced the most leads? Of my referral traffic, where did most of my webinar registrants come from? What percentage of leads from CPC make it down the funnel to become a “marketing-qualified lead”?

There is one question that remains consistent across agency clients and company executives though. How many conversions did we get this month, quarter, year? Just substitute the timeframe for whatever matters most to your organization.

In this post, we’re going to detail a few tips on the conversion types (goals as Google Analytics likes to call them) and dive into how to make your data a bit easier to read with Google Analytics filters and urchin tracking module (UTMs). Mercer had some great inside takeaways this week, so let’s get into it!

Google Analytics Filters

Google Analytics is an excellent tool to gain a breadth of insights into your website traffic, but it’s not always set up for success right out of the box. Filters serve as a way to ensure the data is displayed in a manner that best suits the SEO and that he or she doesn’t miss out on data points simply because Analytics renders it differently. For example, some businesses operate their online dealings over multiple domains to attract different customers – like Getty Images.

Getty Images operates the following domains:,,, and Imagine for a moment, that you’re the search engine optimizer behind Getty Images. With all of those domains to track, how would you go about doing so in an out-of-the-box Analytics account? When viewing specific pages in Analytics, initially data is rendered with just the request URI (for example /home or /request-more-info) and does not include the domain. In this case, you would not be able to tell which domain that /plans-and-pricing belongs to – making your data much more difficult to actually decipher as brought up by Mercer. This is where filters come into play. By creating an advanced filter, you can request that Analytics show data differently, in this case showing the domain ( and request URI (/plans-and-pricing) together for a more complete dataset.

Actually, building the filter requires a bit of regular expression (Regex) know-how, which we will explain in later blog posts. But in the meantime, Regex is a sequence of patterns that define a search pattern, involving different syntaxes to shorten a search request and make it easier for Analytics to understand your exact need. There’s virtually no other way to include domain names across all of your data unless you’re using syntax to get there because it would take way too many filters to make it happen. While I realize how complicated that must sound at this point, we will go into further detail in another post.

One other use case I want to bring up is filtering data to fix fractured data. In layman’s terms, fractured data is data that is split among numerous categories or URLs. Two quick examples of this could be your social traffic or non complete URLs. As brought up by Mercer, sites like Facebook and Pinterest often split their referral traffic into mobile traffic ( and desktop traffic (, which simply splits up traffic in an inefficient manner. Traffic is often fractured by incomplete URLs as well, or URLs that do not have the ending “/” to complete the URL. When URLs such as /request-a-demo and /request-a-demo/ are separated, this leaves critical metrics incomplete, hiding the full picture behind that data. Filters are an excellent way to clear things up. A few pro-tips on filters though, it is critical to test a filter in your testing view before implementing it into production. It’s very easy to filter out data that is important to the greater success of your business in lieu of short-term gain. And the one thing about Google Analytics that is unforgiving is that once that data is filtered out, it’s gone for good!

If you’d like to learn more on Regex before our upcoming Regex post, check out a great resource from Mercer on Regex for more info.

Who, What, Where and Why of Urchin Tracking Module (UTMs)

Urchin tracking module. What in the world is that? UTMs are the easiest way to track your URLs and play a huge role in deciphering the success of SEO and SEM campaigns in general. Imagine for a moment that you’re creating a long-term advertising campaign that encompasses three or four different product lines. Once those ads are deployed to their dedicated landing pages, all marketers must know how to track the ROI for those ads and specifically learn insights based on terms and ad copy. UTMs are the key to that.

The five sources of traffic that can be displayed via UTMs are source, medium, campaign, term and content. Each of these can serve as an identifier in Google Analytics to determine where your traffic came from and how it relates back to your campaigns. If you were to UTM one of our URLs for example, you could put:

Each UTM parameter shine lights on the purpose of the URL. The source – which should detail the referrer of the traffic – states this traffic was referred to by Google. The medium – which will explain what medium this came through – states this was an advertisement or cpc. The campaign – which explains the specific marketing campaign this ad relates to is a CXL test. The term used for this ad to generate was CXL campaign, and the content simply details the ad type, which is a 300x600 sized banner ad. So what’s the story here? A Google 300x600 banner ad about our CXL test campaign has been engaged with. A few other examples of traffic sources for per UTM parameter are listed below.

Source – google, youtube, facebook, bing

Medium – referral, podcast, webinar, cpc, share

Campaign – cxl-institute, bogo-campaign, certification, consulting-discount

Term – video-cameras-for-sale, used-cameras, free-istock-photos

Content – 300x600, v2-160x600, img-survey-ad

Introduction to Google Analytics Goals

Google Analytics goals (or conversions) portrays how a company defines success across their digital campaign efforts. A goal can be defined as an action that the business wants prospects or customers to take on the website. This can be anything from requesting a demo, ordering an item or simply watching a video. In the grand scheme of things, those goals are relatively simple. Google Analytics allows its users to dive a little deeper into the data and set up goals that not only track time on the site but also custom-build a marketing funnel. Let’s take a deeper look into destination goals, duration goals and pages per session goals.

Destination Goals

Destination goals are probably the simplest goal to set up and comprehend. If a user visits a predefined URL on the website then voila the user has completed a particular goal. Most often this is used for one-off form fills and is why you’re served a thank you for registering or thank you for requesting a demo page. Those pages most often both serve as destination URLs for a Google Analytics goal. Additionally, destination goals allow for SEOs to track users who enter unique marketing funnels. When creating a destination goal, there is a switch tab where you can create a funnel destination goal. Upon switching the tab, you gain the ability to enter multiple URLs that would follow the funnel (stream of URLs) you’d like to track. A super simplistic example of this would be a demo request from your homepage. You could set the URLs as follows: / à /request-a-demo à /demo-thank-you-page to track how often individuals request a demo straight from the homepage.

Learning on Google Analytics’ Time

Before we talk about duration goals and pages per session goals, let’s take a moment to talk about how Analytic measures time. Mercer brought up an incredibly important point about Google Analytics and time measurement, and that is that Analytics cannot measure time unless two consecutive actions occur on different pages. What that means is if I’m on the homepage for 20 minutes and don’t visit another page on the website, then technically that is recorded as 0 seconds on page by Analytics standards because I didn’t engage with enough pages for it to actually measure a time on page. With that in mind, it’s important to understand that not everyone is going to go deeper than the one page, and that’s okay. You just have to understand that the metrics surrounding average time on page or the duration and pages per session goals are heavily determined by this factor and could affect your data in the long run. Okay – now back to some fun goals.

Duration Goals & Pages per Session Goals

Both duration goals and pages per session goals weigh heavily on time. Duration goals allow SEOs to measure how long a user has been on a certain page. But keep in mind, this goal will not register if there is not a timestamp from another page to clarify that they’ve been on the duration goal page for a specific period of time. For example, if your duration goal is to have someone watch a video on your site for 5 minutes and they never move to another page on the site, it will not count as an actual duration goal.

Pages per session goals simply measure the amount of pages an individual visits. The caveat with pages per session goals are the session duration time. Let’s say that your session time is the default 30 minutes on the site. Is that conducive to your prospects? Are there several videos that timeout at the 30 minute mark? If so, they’re going to time out of the session and the pages per session goal will be inaccurate. Just something to keep in mind!

So how are you creating goals? Are you using filters in your Google Analytics account? We’d love to hear how you’re optimizing your Analytics account to better suit your business. Otherwise, thanks for reading and I look forward to sharing more insights next week!

This post was created in an effort to complete my CXL Institute Mindegree Scholarship obligation and speak to the materials reviewed in the course. The information is a combination of my previous knowledge and excellent insights from a phenomenal program.

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