[cosmie]

Attribution is as sufficiently advanced as needed to get approval to give BBDO another blank check for another hairbrained scheme that'll fund yet another award for BBDO. While concurrently being undermined and considered not reliable or advanced enough if the numbers it provides do not lead to another blank check for the creative agency.

I come from the performance side, but currently work with a bunch of CPG clients with products that run the gamut between commodity to luxury.

I specifically work on creating analytics and attribution frameworks, because these companies are fine with fuzzy hand-wavy "lift studies" for tv commercials and stupid in store display stunts. But they hold a double standard and anything digital has to be concretely measured to defend its budget.

It's actually pretty easy to create robust analytics and attribution in the space. But it's mainly a process thing, to be able to sprinkle around enough unique traits or identifiers along the way to measure at an aggregate level what the impact was. It tends to rarely be done though, due to a lack of that level of operational discipline for brand marketers and agencies, or due to the desire to deliberately sabatage the numbers because they don't paint a particularly flattering picture. So more often than not you end up with a botched execution on the small details that were required for proper attribution, then the resulting numbers being full of enough holes to spin the data however is convenient. Or someone slapping on some poorly integrated software that spits out a number that's taken as the holy grail, "cuz AI said so".

... which leads to a terrible cycle of distrust in analytics and attribution on the brand side, leading to fewer initiatives that prioritize it.

[shostack]

Interesting. We share a lot of frustrations and challenges.

What sort of spend levels and data volumes do you typically need to see for the lift studies you do with TV and CTV? Do you typically isolate to specific markets for that?

[cosmie]

It really just depends. In the case of one retail client, we have carte blanche access to all of their data, from marketing systems to POS data to app location data. It makes it incredibly easy to "lazy load" a lift study after the fact, by looking for anomalies in behavior that are correlated with the creative. Rather than a standard test and control, we can essentially tailor the model to a per-store or per-region level and rollup lift from there. It's less about the spend level and data volume, and more about the data completeness.

For CPG clients, it's more of a pain. Those usually involve really complex interagency relationships, with discontinuity in both processes and data access. And in a lot of cases, they may have access to a retail partners POS or loyalty data, but can't share it directly with us as a third party agency, and there's a game of telephone where we have to coach them on what to ask for and provide to us (in whatever form they're allowed), while being blind to the data and data/system structures. So a lot gets lost in translation, with the spend level having to be large enough to compensate for however dysfunction that process is for that program and client.

That said, I'm lucky enough to be a passive observe to that most of the time. Another manager under my boss is responsible for those more traditional lift studies. I have an unusual background in that I've done a lot of process development work, web analytics, and data engineering/management. So I'm only brought into those projects when we have more technically sophisticated needs.