Let me start this section by saying: there is no ‘right’ attribution model. There are no off-the-shelf solutions out there that you can buy, drop on your data, and hey presto: instant, accurate campaign evaluations.
In order to get to a view of campaign performance there are concessions you must make, the biggest being whether to split your views of the online and offline world in to independent, mutually exclusive existences, or whether to try and integrate them. No: I take that back. The biggest concession is how much time (or money) you’re willing to invest in improving your understanding of how your advertising works. More advanced techniques may take weeks to generate, and then need ongoing support and tweaks.
The underlying complexity driving this decision is a result of the vastly different data sets that arise from the offline and online worlds. The former are generally traditional media channels such as radio and TV, to which you may physically be unable respond in kind (at least until touchscreen TV technology and its ilk is widely deployed). Adverts are served at set times in the programming, and are ‘slow’ at typically 10-30seconds in duration. Whether an individual has seen your advert is an unknown.
Contrast this with online advertising where adverts are shown across most pages, at any time, are renewed with each refresh and sometimes with each scroll. A user browsing around may be exposed to tens or hundreds of adverts in a very short space in time, with every viewing and interaction tracked to the nth degree. Advert content (‘creative’) can be generated programmatically on the fly leading to a multitude of different versions and, significantly, there is (with some exceptions) a cookie record tracking the sequence of what you’ve seen and done.
In essence: you have offline data that needs (let’s say) weekly aggregations in order to provide sufficient sample volumes for a robust pattern to emerge. You then have online data that comes thick and fast, but where scale is thinned by high variety and fine detail.
So gives rise to two main techniques, Econometrics and Digital Attribution. And occasionally: an attempted mash of the two.
Econometrics attempts the holistic view, giving up some of the finer detail in order to give an all-encompassing view. Models will vary in complexity (often determined by the amount of time invested in developing the model), but the basic premise is one of linear relationships: do more of advertising type A, get a proportional increase in sales back x weeks later.
Digital Attribution foregoes explaining the offline activity and instead makes use of the online tracking technologies to try and piece together a customers’ journey. Broadly speaking, these techniques fall in to two camps. The first correlates customer interactions with sales activities, and leaves sales unexplained by patterns in the digital data in an unallocated ‘pot’. The second fully apportions the known sales across digital advertising channels. Both may choose (or not) to make sequencing and chronology a factor, but both techniques make use of case-wise customer journeys as opposed to ‘activity volumes’ as used in econometrics.
I’m not an econometrician, and my work falls in to this second set of techniques: digital attribution. In agencies this is often a complementary piece to an econometrics project, and is used to provide further, more granular insight in to the finer detail of online campaign performance.
So, having set up the background, let’s talk more about this latter field of analysis.