Author: Mike O’Connor
Reading Time: 10 minutes
What is the attribution model used for Google Analytics’ standard Acquisition reports? Last Interaction, right? Not exactly.
The default model in Google Analytics is, in fact, Last Non-Direct Click. While this may seem to be a subtle difference, it can have a big impact on how you attribute conversions, and how you interpret your data. According to Google, this is when this model is useful:
If you consider direct traffic to be from customers who have already been won through a different channel, then you may wish to filter out direct traffic and focus on the last marketing activity before conversion.
But this makes some big assumptions, especially to make it the default model in all standard acquisition reports. It can be very useful to know if a considerable percentage of your traffic is coming directly to your website to make a purchase - And this can help inform your overall acquisition strategy.
As you can see, there can be considerable differences between the two models:
While you can’t change the attribution method used for the standard acquisition reports, you can change or compare models in the Attribution Model Comparison Tool, and we fully encourage you to do just that; and not just for Last Interaction vs. Last Non-Direct Click, but take a look at how all of the models compare. This, along with utilizing the Multi-Channel Funnels Top Conversion Paths report, provides essential insight into the roles that your various channels play.
Also, be wary of how Google Analytics defines and counts sessions. As we all (should) know, a session (or visit) is a group of page views and interactions associated with a single user (on a single web browser) within a certain timeframe, typically the industry standard of 30 minutes (i.e. a session will end after 30 minutes of inactivity).
However, in addition to time-based expiration Google Analytics also uses campaign-based expiration. That is, any time a new traffic source value (e.g. utm_source, utm_medium, utm_campaign, utm_term) is recorded for a particular user Google Analytics will consider it a new session, regardless if this activity occurred within the 30-minute session window. This can inflate the total session count for your website as well as diminish visibility into the traffic source that initially drove the visit and can also mis-credit conversions.
As an example, consider this scenario experienced by one of our e-commerce clients. After migrating to a new e-commerce platform there was a requirement for all existing users to update their passwords the first time they logged into the new platform. This triggered a password reset email for which the inbound links were tagged with campaign values to indicate that the user clicked through from that password reset email. Now, if a user initially came to the website from a Paid Search listing, added an item to the cart, and then attempted to log in to complete their purchase then they would be prompted to reset their password. After receiving the email and resetting their password then that would now be counted as a new session and the eventual purchase would be credited to the password reset email and not to Paid Search.
Now, I know what you may be thinking - just remove the campaign parameters from the links in the email, right? But it can still be useful to identify how many users are clicking through from those emails, we just don’t want to consider it a new session, nor do we want it to receive credit for the conversion. What we’d want instead is to have Entry source dimensions (i.e. the source/campaign that initiated the visit) but also general source dimensions to provide insight into in-session activity. One option here would be to use Internal Promotions tracking or a unique query parameter mapped to a custom dimension as an alternative to utilizing the utm parameters in these types of scenarios.
These are just a couple of examples of Google Analytics methodologies that differ from industry standards (or at least from the methodologies followed by other major web analytics providers), and yet they are not clearly called out in the Google Analytics user interface. Rather they require a bit of digging through the documentation to find.
So be sure to keep these things in mind and do your due diligence into analytics platform methodologies before undertaking an analysis and presenting your data. It is incredibly important to ensure that your insights are framed in a way that accurately represents what is actually happening so that you maintain trust in the data throughout the organization, and are making the correct decisions to optimize the business.