June 26, 2019
‘We feel pretty good about tracking accuracy’. This is the most common response we get from clients before diving into the analytics audits. This is rarely the case, however. Even with this high level of confidence, we encounter tracking issues which significantly distort data analysis and reporting.
Implementing Google Analytics and conversions tracking can be tricky, and errors are easily overlooked, which is why continuous measurement evaluation is vital to ensure data integrity.
Before we do analysis, we need clean data, so let’s take a look at most common Google Analytics data collection mistakes and how to fix them.
Universal Analytics is the latest version of Google Analytics code. Surprisingly, many organizations still use the old version of the code, the Global Site Tag (gtag.js). If that’s the case, you need to migrate the account to Universal Analytics, which offers more tracking capabilities and integrations.
If your bounce rate is too good to be true, it probably is. A bounce rate that’s lower than 10% is a sure indicator of tracking issues. The first thing to check is if there’s double tracking on the website.
Internal links are hyperlinks that point to another webpage on the same domain. Using UTM parameters (such as utm_source, utm_medium, ect.) to tag internal links will trigger a new session every time a link is clicked on.
For example, a user who found your website through organic search clicks on a tagged contact link (e.g. www.example.com/contact/?utm_source=website&utm_medium=aboutpage&utm_campaign=contactus). The initial organic session ends, and a new session is triggered. This will not only inflate your session count in GA but also lose original source and medium attribution for any conversion that might result in a session.
Use UTM parameters to tag external links only, and use events for tracking internal links.
Are URLs with never-ending strings of characters showing up in your page reports?
Query parameters can break up URLs in Google Analytics and make analysis very problematic. A single webpage can be broken up in numerous different pages as parameter values are appended to the URL. If the appended information is not useful for further analysis, you want to exclude the URL query parameter from the reporting view in GA. In Admin/View settings specify the query that should be excluded.
Internal traffic refers to website traffic coming from your offices, employees, marketing partner, etc. This traffic has no value to your bottom line and inflates not only the number of sessions, but other website usage metrics as well (e.g. bounce rate, time on page, page views). Internal traffic can be easily filtered from reporting views with IP address exclusions in View Settings. Before implementing IP exclusion, a couple of things to consider:
Your UTM strategy needs to span different sources and mediums. If you decide to use source and medium that differs from Google defined rules, you must either redefine the Default Channel Groupings or create new Custom Channel Groupings. Run a test of tagged mediums and make sure they’re bucketed in the correct channel. Check out our UTM best practices.
Good, a UTM strategy is in place, we’re good to go, right? Well, not really. In case of linking Google Ads account with Google Analytics, make sure both platforms are set up for tagging. Whether using auto-tagging or manual setting, there are settings in both Google Ads and Google Analytics that should be checked to avoid any data discrepancy.
If left unchecked, Google will auto-tag your URL with default source/medium, and the manual tagging will be ignored.
|google / cpc||https://example.com/?utm_source=google&utm_medium=display&utm_campaign=Prospect|
Google Analytics and Google Ads are linked but no sessions are attributed to Google campaigns? Besides incorrect linking, a common mistake we see happen is using the incorrect URL. The final ad destination URL in Google Ads must match the exact URL that populates in the browser URL. For example, if your ad destination is using https://www.teamlewis.com/our-work, but the actual URL should also include a backslash at the end, https://www.teamlewis.com/our-work/, all traffic will be miscategorized as Direct traffic. Always use the link populated in the browser URL to be sure.
Bot traffic can inflate your traffic data, so it is important to filter this traffic out. Google allows you to filter known bots and spiders from your reports. Enabling this feature in View settings will ensure known bots are filtered, but what about new bots? It is important to notice bot traffic before it drastically distorts your data.
One way to keep track of any weird website behavior is to set up Custom Alerts, which will notify you when there is an increase in new sessions or conversions. After you’ve been altered of unusual behavior, you can deep dive into most common bot traffic identifiers. Bot traffic usually lingers in direct or referral traffic, has a high bounce rate and spends little to zero time on site. Further analysis looks at information such as location, service providers and browser versions in efforts to isolate the culprit. Identifying the exact origin of bot traffic will help in building a segment with clean data.
Often overlooked in Google Analytics are duplicate transactions. These usually happen when a user returns to the order confirmation page either via a bookmark, a page refresh, a confirmation email, etc. In the example below, a single transaction is recorded 14, inflating a $36 purchase to $515. This will significantly skew your revenue and transaction reports. Best way to fix the issue would be from the server-side where a request to refresh/return to the order confirmation page would be blocked or ensuring that the e-commerce data is only sent the first-time user makes a transaction.
If you notice any of these issues or need help identifying tracking issues, we’ve got you covered, check-out our analytics consulting services.