Programmatic: Attribution Model, the most underrated element of the Ad Tech...

Attribution refers to the way we choose to measure the contribution of each touchpoint in a user's journey leading up to a conversion 📏

In programmatic advertising, attribution windows are very often Post-Click (PC) and Post-Impression (PI). Example: "the chosen model is 7-day PC and 1-day PI".

This model has significant flaws, especially regarding PI:

• Did the ad actually appear on screen? (e.g., viewability rate) 🤔
• Was the ad placement truly high-quality? (e.g., Ad Attention) 🤔
• Was it really this ad that motivated the user to take action? 🤔

Classic post-impression attribution is actually quite harmful to campaign efficiency: it incentivizes cookie dropping, i.e., buying at the lowest possible CPM with strict capping to claim credit for a maximum of conversions, without necessarily generating them for real... 🤯

Yet, there are more efficient alternative approaches.

Some attribution tools, like Wizaly for example, base their methodology on Shapley values.

Lloyd Shapley was a mathematician who one day asked himself the following question: "In a team sport, how do you determine the contribution of each player to the team ?"

And then, he had a brilliant idea: "It's simple! We'll play as many matches as there are possible player combinations, and we'll analyze all the scores!"

In the digital world, this means that for EVERY conversion achieved (whether through ads or not), you can trace back ALL touchpoints, to then estimate the value of each one.

And let me tell you, cookie dropping campaigns end up with a value that is... rather low 🤭

Our recommendation at GreyFox Ads is to have an approach that aligns with the conversion funnel 🌪️:
🎯 People who match the typical buyer persona but do not have purchase intent = Branding KPIs
🎯 People with intent but who don't know the advertiser's website = Consideration KPIs (traffic)
🎯 People with intent who already know the website = Conversion KPIs

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