A marketer’s arsenal in 2020 has never been more plentiful. There’s an embarrassment of riches and we’re spoilt for choice when trying to determine which tools best fit our marketing stack and which of those tools offer us the insights that we need.
When we talk about trend analysis or any form of monitoring on your marketing activity or website performance, we’re talking about analytics. There are many analytics packages available to you but you’ll most likely be familiar with Google Analytics. Furthermore, analytics tools come built in with many marketing technology platforms. Six & Flow are a Diamond HubSpot Partner, and as such, we’re often asked why data from Google Analytics and HubSpot’s Analytics package don’t match up.
‘Are we doing something wrong?’
And the answer is invariably ‘no’. Now, we don’t want to get too technical in this article, but before replying to your stakeholder, you should probably do a quick test to ensure that both tracking codes are placed correctly.
Our quick guide below will show you the optimal positions in your code to place each code.
After you’ve ensured your tags are in the correct positions in your site’s code, you should also be aware of your site’s page speed. This shouldn’t be a common occurrence, but because of the positioning of the tags, Google Analytics (if placed without GTM) will always load prior to the HubSpot tag, therefore if a page view occurs and the user leaves before the site completes loading, there can be a disparity in traffic here.
Once you’ve performed the health checks and ensured all is well and you’re still concerned about the variances of data between Google Analytics and HubSpot Analytics we can begin to diagnose why. The differences between data normally falls into two categories, nominal traffic differences and marked traffic differences.
For the former, it’s important to underpin this article by stating that it’s almost next to impossible to have the two analytics platforms recording absolute parity when it comes to data. We’ll delve into the technical ‘whys’ in the next section, but you should be aiming for around a 10% variance between the reporting, and if you sit within this boundary on any given reporting sample, your tracking is in a healthy place.
If you’re still concerned about your data differences, we need to understand how each Analytics platform works. The confusion between platform users comes when falsely believing that metrics named in both platforms are defined the same.
Let’s take the metric ‘session’ as an example, this both appears in HubSpot and Google Analytics as a very important statistic to monitor;
Google Analytics: views a session as occurring when a visitor reaches your site from a referring source with a given time frame (this is defaulted at 30 minutes). The user within this session can be unique or a repeat visitor. They can perform any number of actions on your site within the same session such as page views, download guides or get a quote. This will always remain the same session until they reach the site through a different source.
HubSpot: view a session as a series of actions taken by a user on your site. These activities include page views, CTA clicks, events etc. and sessions will expire after 30 minutes. A new session will occur when a visitor returns to your site with 30 minutes of inactivity prior or via a new session campaign (a different campaign source for example).
With the definition of sessions out of the way, there’s other differences to how each of the tools will track the source of a session. HubSpot analyses and counts sessions based upon the referral domain a user came from (google.com, facebook.com, linkedin.com etc) whereas Google Analytics measures sessions from referral source/medium (google/organic, google/ppc, facebook/paid-social) - therefore, this will ultimately mean you have occasions where a visit to your site will count as one session in one tool but multiple sessions in the other and vice versa.
There are finer differences between the platforms which result in slight anomalies when performing traffic comparison but this should fall within the expected 10% variance. The reason the question "why analytics tools don’t match up" is becoming more prevalent is due, in part, to the introduction of the data protection and cookie banner disclosing policy. This has impacted tools’ ability to track anonymous users.
If you’re still concerned about data disparity between your platforms, why not reach out to us and let us help you by looking at the issue in a little more detail. Don’t forget to stay tuned for the next articles in this mini-series on how to best make use of both tools in your analysis stack.