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Retail Media Breakfast Club
Retail Media Measurement Is Broken (Here’s What Brands Actually Want Instead)
00:11:06
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- [00:00]
A
Andrew Cavado keeps seeing the same thing happen with retail media networks. A brand asks for data that they can feed into their marketing mix model, but the retailer misunderstands the request and tries to sell them on an attribution dashboard instead. And the brand walks away. Andrew has spent over 15 years building measurement programs at Google, Meta, Netflix, and Snap. And, and he now runs Growth by Science, a consultancy helping brands build custom incrementality testing and media mix models. So he's seen this from every angle. And on a recent episode of the Unlocking Retail Media podcast with host James Avery, he laid out why RMNs keep losing credibility with measurement. It's not that the ads don't work. It's that they can't prove it in the language that, that their most valuable advertisers speak. This is a great episode. I learned a ton. And I'm going to share three things that I took away from this conversation about what retail media networks are getting wrong on measurement. Let's jump in. Number one, the big ad platforms stayed measurement agnostic, but retail media didn't. Andrew says that platforms like Meta, Google and Snap got big in part by, by supporting whatever measurement approach the advertiser wanted to bring. Incrementality, mta, media, mixed modeling. If there was demand, they built for it. But RMNs tend to do the opposite. Let's listen.
- [01:36]
B
Of course, having worked at a bunch of them, I, I, I do have a special place in my heart for the ad platforms. But ad platforms are not necessarily in the business of optimizing advertiser outcomes. They're in the interest of, or they're in the business of optimizing their own revenue. And the way that you do that is you have to be effective, right? And so you can't not optimize for advertiser outcomes and you can't not be a performant platform. But you also are always trying to find an edge to, you know, to showcase a story, to showcase the performance of your ads, and to, you know, create a narrative that attracts advertisers from a different vertical, from various different verticals to your platform. And really the way to do that is with measurement. And so the smart platforms will always take a relatively measurement agnostic perspective and kind of forge partnerships and build first party products that are really born of the demand that they're receiving from the advertiser. So if an advertiser says, hey, I really care about incrementality, or I really care about mta, or I really care about some random other type of measurement approach, if there's enough demand for that measurement approach, the advertising platform is going to support it. And that's why when you do work with some of the bigger platforms, pretty much any measurement approach that you want to bring to the table and that you want to utilize, that will be somehow supported. Now they may try to guide advertisers a certain way and they may try to talk a lot about some of the more sophisticated and advanced measurement approaches, but they're never really, never going to turn down business. And if you know a certain measurement approach they disagree with but drives a lot of business, they're going to support it. So really, I think as an advertiser it's really important that you have your own opinions on measurement and that you kind of lean on your own research and your own analytical expertise to kind of set what you want your measurement approach to look like rather than take that input necessarily from the platform. You can of course listen to what they have to say, but just know that you know their, their measurement programs are, are not 100% altruistic.
- [03:52]
A
The lesson from big ad tech is simple. Don't pick the measurement approach for the advertiser. Support what they need. Takeaway number two. Retail media networks are answering questions that their biggest advertisers have stopped asking. Andrew says that retailers are over investing in things like granular user level path to purchase data while their most valuable prospects actually need something else. They need aggregated feeds for modeling and experimentation.
- [04:23]
B
You know where I have seen measurement lag somewhat from the ad platform side is definitely in the RMN space. And it's just born of the fact RMNs are not typically the primary revenue stream for folks, right? This is retailers or other platforms that have a ton of data and realized in the last few years that hey, there's some great monetization potential. We can help our various constituents, we can provide a great experience to consumers but also help our advertisers and the wholesalers to really promote their products in a good way. And so they're by nature not caught up with what I would say is natives to adtech. And so what I tend to see happen a lot in rmns is this over focus on really granular user level data. Trying to design this path to purchase data set with the idea that hey, we can prove that there was a touch point on some property that preceded a conversion and that's going to be good enough to prove efficacy. And I would say that that is not the case at all. That's kind of the old school way of doing things. Certainly there's demand for that. A lot of advertisers still care about those types of data sets, but the approaches that the most sophisticated advertisers are taking are really more in the modeling, the experimentation and in sort of like that aggregate data approach. So I think retail media needs to consider what does an experiment require and what does an MMM require, And not to say give up on the attribution because as I said, there's a lot of demand still for that. But supplement that with a great API that lets you get data out in a way that's conducive to mmm.
- [06:29]
A
Miracle Ads is the ad tech solution trusted by rakuten and over 50 global enterprise retailers. That's because Miracle Ads was built with both 3Pmarketplace sellers and 1P suppliers in mind. Both advertiser audiences demand a seamless advertising journey from onboarding to reporting. You can offer everything from sponsored products to video ads all in one solution. Learn more@miracle.com that's M I R A K L.com and highlight number three. First party lift is grading your own homework. Just be honest about it. James asked Andrew how he designed incrementality for a retailer and Andrew laid out a graduation framework. Don't force full incrementality on new advertisers. But also don't pretend basic attribution is the ceiling either. Let's listen.
- [07:29]
B
I think there's a couple of approaches, right, you nailed it. Where there's the segmentation of the advertisers. For one, I wouldn't force incrementality on people who are just trying out the platform. What I would do is acknowledge that whatever attribution measurement you might be doing at that level is not going to be perfect, but it can be sort of directional and it can give a hint as to are people that are buying your product seeing the ad. That's the most conservative way of representing that. And you can say in the best case, everybody that saw the ad bought the product. In the worst case, you're at least showing it to people that would have already bought that ad anyway. And so you know that your target audience is here. And then the next step after we prove that out is to tweak the incrementality. But you've got to have a certain minimums in terms of spend to be able to do that. But yeah, first party Lift is always good. It doesn't cost, obviously the development cost, but there's no, you know, having to partner with somebody else. However, it is a bit of grading your own homework. I would look at First Party Lift as kind of a more of a sales tool versus a measurement tool and go into it fully acknowledging like, look, we're doing our best to be objective, but we understand that you may not look at this as purely objective because it isn't, because it's our own platform, blah blah blah. However, as a graduation from attribution, I think this is a great next step.
- [08:55]
A
So the path is this directional attribution for newer, less sophisticated advertisers. First Party Lift as a step up from that, and then third party GEO testing and MMM integration for the biggest spenders. A lot of retailers are stuck at step one, but here is the slightly uncomfortable part. Andrew is describing what sophisticated brands need, but a huge chunk of brands aren't even there yet. I spoke with Liz Roche, who is VP of Media and Measurement at Albertsons Media Collective, a couple of weeks ago about their recent IROAS research and she made the point that many brand partners receive one of these kinds of reports and simply don't have the team to really question or understand what's behind it. Not every brand has a data science team that can evaluate whether that IROAS figure was calculated using propensity score matching or clustering or whatever historical brand sales were included as a feature. That research from Albertsons found that 83% of campaigns can flip from positive to negative IROAS based on the methodology alone. And this means that transparency isn't just a nice to have for the top tier CPGs running MMMs. It's also essential for the mid market brands who are taking the number at face value. So the takeaway here is that retail media has a measurement gap at both ends. The most sophisticated brands can't get the data formats that they need and the mid market brands can't evaluate what they're getting. Andrew's advice RMN's is the same regardless. Be honest about what your measurement does and doesn't prove any and build the APIs and geo targeting that advanced advertisers require. Invest in internal expertise that really understands the why behind the ask. Check out the Unlocking Retail Media podcast for the full conversation. Thanks for listening and I'll catch you tomorrow.