Lately there is a lot of talk about how “last-click is dead” and people are discussing other models, like first-click attribution models. However, there is an important distinction to be made between rules-based attribution models and algorithmic attribution modeling.
When you agree to apportion credit based on frequency, recency, perceived strength of the interaction, etc. you are using rule-based attribution. The default attributon model in most tools, last-click, is a rule-based model. By today’s standards, this rules-based approach seems crude. The althernative is the algorithmic approach (statistical algorithms) to attribution in order to the determine the weights. The algorithmic approach is similar in that it aims to weight things like frequency, recency and the quality of the interaction in coming up with a composite credit for each conversion.
I hear you thinking sure, got it. But that’s not all there is to it – because if you use last-click attitribution, that means that you can only award credit to campaigns that get you clicks. And that might be nice for Search, but where does that not work out at all? Right, in Display and even more so in video, where clicks are far from the right metric to judge effectiveness by. In Display banners and video, the impressions and views drive your prospects to conversion. Furthermore, your clickers might not be your converters (source: Quantcast PDF). I hear you thinking “sure, I get that, but how to measure all that?” – Well, make a first move into a better, or less wrong, attribution model by including impression impact in your model and assigning some credit to it.
So, are you rushing into your Google Analytics at this very moment to include impression interactions into your custom attribution model (see here how). Let us know how this works for your campaigns!