The goal behind an attribution model is to understand how all of your marketing touch-points fit together, showing you which dollars are working the hardest, and subsequently allowing you to make smarter decisions next time around.
Sounds simple, right? Actually, it’s very far from simple, and is actually the wrong goal to be aiming for.
Firstly, most of the barriers to building an attribution solution are extremely hard to overcome, and others are simply impossible to accomplish. Technical limitations mean we can’t tie together all of our digital touch-points, human limitations mean we can’t get everyone to agree to how the model should work, and simple life realities (such as real face-to-face communications) mean we can’t account for every day-to-day interaction.
Programmatic Attribution Modeling is a solution that moves us beyond these barriers, and focuses us back on the idea of taking action based on data, not just making sexy graphs. But first, let’s understand why this fresh approach is really needed.
Where do we begin?! To make headway with an attribution model, we must be able to account for as many touch-points as we can, with at least a reasonable sample size of the targeted population.
Some of these are simple. It is not uncommon to bring together all display buys under one cookie by using a buying platform for real-time media, and that same cookie can be served on premium/direct display buys, too. This could be your ad server, or it could be a demand-side platform (DSP), a data-management platform (DMP), or better still, a PMP (Programmatic Marketing Platform) that combines all these technologies.
And, that cookie can also be matched against the clickers from your SEM program, with the same tools, or with on-site analytics. Tag container companies like BrightTag, TagMan, Tealium and others can help further, and some take you to a deeper level still by helping to bridge more vendors that might be on your plans.
The next step would require pure, sneaky “smarts.” Using Pinterest or Facebook in your marketing? Use your PMP to look for incoming traffic (and cookies) from those sites and add that individual’s interaction to their universal profile, knowing they have pinned or friended your brand.
At this stage, we are still missing everyone’s offline exposure to the dataset, as well as interactions with real-life influencers. In the future, perhaps the presence of RFID chips in phones (and in people ) will help overcome that; but, until it becomes widespread and measurable, we need to settle for broad, geographical trends based on the investment of advertising at the DMA level to get a partial solution for this piece.
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