Use the Data Available to You to See the Whole Picture
Guest Contributor:
Corey Morris, Digital Marketing Director
Lead attribution and the customer journey. Yes, these are two of the most commonly used buzzwords in digital marketing right now. This is not a lazy blog post to latch onto what others are saying and to give you a fluffy, rosy version of how you should be considering both the customer journey and lead attribution to make your digital marketing drive results 10x over what you got last year. This blog is to make sure we’re all on the same page and using the data available to us to help make these topics attainable and realistic before we get too “pie in the sky” with our conceptual thinking.
But first, we must answer this question: what is lead attribution? Lead attribution is the practice of giving credit to the source who provided the lead. For example, if you are running a PPC campaign in Google AdWords and that person comes to a landing page on our site and completes the form, then they are a conversion—consequently, that lead gets attributed to PPC via AdWords.
This example sounds like typical and solid tracking; however, it could also be short-sighted when we’re talking about “last-click attribution.” By counting this lead as a lead specifically for AdWords PPC, we’re potentially not considering the other potential ways the user might have found us—and the other ways they interacted with our content before coming back. In this case, PPC is getting the credit.
The customer journey can be defined as the process a user takes to go from their initial step in researching, all the way to the point of conversion. If we’re using the Google AdWords PPC landing page form completion example noted above, then we’re also talking about how that same individual (yes, they’re a person, despite all of our “persona talk” about site visitors and users) ultimately decides to fill out a form, which is recorded as a conversion.
The challenge in all of this is that we don’t often work to connect the dots to attribute a lead to all the channels that had a role in the conversion— not just the one that received the last click. It can be tricky as it often isn’t linear or very trackable; however, that doesn’t let us off the hook. We have some data at our fingertips that helps us start the process of working toward building a system. If you have Google Analytics, then you have a tool that has two reports you should start looking at as your first step.
The first report in Google Analytics to get familiar with is the Multi-Channel Funnels Overview under the Conversions section. If you have conversion goals set up in your account, then you’ll have data in this report by default.
You can use the checkboxes to update the Venn diagram to mix and match, so you can understand how the different channels were involved in user journeys that ultimately led to a conversion. You can also see how many total assisted conversions there were.
The second report to take a look at is the Assisted Conversions report (also under the Conversions section in Google Analytics).
There’s a lot more you can do in this report. At a basic level, it shows a breakdown of assisted conversions, which are channels that were part of a user journey but didn’t get the last click or direct conversion at the end of the journey. If you have values set for your conversion goals or have eCommerce tracking on in Google Analytics then you also can see dollar values for each channel, which can be incredibly helpful in measuring the cost of your efforts against revenue generated. You can customize the data in this report by changing the number of days in the window prior to conversion as well as look at the value of first interaction versus last click.
Bonus: If you want to take another step and get into more advanced territory, take a look at the Attribution Model Comparison report in Google Analytics. There are some fun ways to compare models and see how the data and your perspective on conversions might change. We’ll get into this and go deeper with the next post in this series.
