Prohaska Perspectives April 2022April was a busy month—we released The Identity Partner List 5.0, launched our Attribution and Measurement practice, attended events across the US and EMEA, and made exciting plans for Cannes (stay tuned for more on what we have up our sleeve for The Lions). Read on for a glimpse at the new IPL, our POV on A&M, fresh job opportunities with Team Prohaska and with our clients, and where to find us in May!
The Identity Partner List 5.0Team Prohaska is proud to release version 5.0 of The Identity Partner List, our guide to the landscape of deterministic, probabilistic, and contextual solution providers brands need to consider as they make plans for a post-third-party cookie world. Get the list!
Prohaska Global Attribution & Measurement Strategy Lead, Rudy Grahn, talks to CMO Rachel Pasqua about the importance of holistic A&M
This month, we launched Prohaska Consulting’s Attribution & Measurement (A&M) practice, designed to help marketers obtain true ROI from their media investments. In this edition of Prohaska Perspectives, A&M practice lead Rudy Grahn sits down with our CMO, Rachel Pasqua, to talk about the difference between measurement and accounting, the downward pressure of the tech tax, and the next big thing in A&M.
Rachel Pasqua: Rudy, thanks for making the time. I thought we’d start with the basics—how should we define measurement?
Rudy Grahn: That’s not as basic as you might think! Ideally, measurement is the creation of data sets which fuel better decision-making. In marketing, that means measurement is the process of creating and curating the data needed to drive better investment decisions within and across media channels. Our goal is to teach clients how to build those data sets.
Rachel Pasqua: Not everyone perceives measurement as a tool for improving performance—I’ve heard you say many times that most companies treat it more like something post hoc and descriptive, like accounting.
Rudy Grahn: Right, whereas measurement in its proper form should be cyclical and prescriptive, but more often than not, it isn’t approached that way and it’s a dangerous pitfall. When you see how much money is spent on measuring performance compared to the size of the media investment being measured, the importance comes into sharp focus. Non-working media budgets are now commonly the equivalent of 50% of the media budgets they are supposed to optimize. Without proof of ROI, that is likely profligate waste.
Rachel Pasqua: I know this is something we all need to worry about, but do some of us need to worry more than others?
Rudy Grahn: Anyone responsible for outcomes is responsible for measurement. There is a high degree of specialization needed in channel-specific measurement, but for most, where there are multiple channels being measured, there is also responsibility for a holistic measurement strategy across those channels. The degree of investment dictates the degree of need.
Rachel Pasqua: Clearly, marketers need to change their ways, but who is responsible for that change?
Rudy Grahn: Much is made of this question, and for good reason, but it is one that can be greatly mitigated in advance by transparency in methodology and in the standards used to measure. Measurement is often equated to “grading the homework”, and lots of debate goes on around the bias of whoever does the grading. Fair enough, but reducing measurement to “the grading of homework” isn’t always fruitful, especially if it is just a purity test no one in this ecosystem can credibly pass to begin with. If the debate over who does the grading gets in the way of the homework being graded at all—or worse yet, prevents the homework itself from even being attempted—that’s unhelpful, to put it mildly. The idea that there is an ideal, unbiased judge somewhere—on any side of media transactions—is usually a bit of self-deception.
Rachel Pasqua: Things are changing so quickly though—isn’t it dangerous to make too many big decisions around measurement strategy in the midst of so much uncertainty?
Rudy Grahn: Perhaps, but waiting for uncertainty to pass in this industry won’t do. Managing constant change is what most of us do all day, no matter what our job title says, and this shouldn’t be seen as a headwind. The day processes are stable enough for “set it and forget it” is the day the algorithms can likely take over for many of us. The good news for humans is that we aren’t even close to “set it and forget it” yet, especially around the really big decisions that need to be made. Lean in to the uncertainty and wrestle with it to the best effect you can. It is why we are here. It is why you are needed.
Rachel Pasqua: We agree challenges abound—when it comes to A&M, which are the biggest and most pressing?
Rudy Grahn: People deciding that the change is too hard to manage, and giving up is top of my list. I think that deciding that getting measurement and optimization right is “too tough” is the biggest factor driving adoption of “performance” based media, and this is a mistake. There are lots of good reasons to invest in performance media, but avoiding the difficulties of measurement isn’t one of them.
Rachel Pasqua: I think we agree that if there’s one thing this industry is great at, it’s avoiding the inevitable, often in favor of the shiny new object. That said, sometimes the shiny new object plays a part in helping us confront what we’re trying to avoid. So, talk to us about the next big thing in A&M.
Rudy Grahn: At the risk of piling on, I have to say, it is machine learning. We have to keep in mind that much of what people mean when they say “measurement” is really just “post-campaign measurement”. Many say “measurement” when what they really mean is “assessing the value of an impression we bought after we bought it”. In some ways, this is redundant with the bidding process. A bid for an impression is a prediction of the value of an impression before you buy it. If you are getting your value predictions correct before you buy them, the need to do post-hoc assessment is greatly reduced. Many are building bidding processes separate from their measurement processes because neither process on its own is good enough to do all the work. Machine Learning can change that. Building the data sets which make accurate value predictions should not be separate from the other data sets you build to do your measurement. Making a single holistic process out of these things has lots of potential and can eliminate lots of the problems vexing marketers today. We’re ready when you are!
Interested in learning more about Attribution & Measurement strategy from Prohaska Consulting? Let’s talk!
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