Latest / Retail Media Breakfast Club / Predictive Commerce: Why Life-Stage Data Could Replace Retail Media’s Obsession with Transactions
Transcript
- Education-Powered Commerce Media pitches === [00:00:00] Kiri voiceover: Most commerce media networks sell the past. They know that you bought diapers last Tuesday, so they serve you diaper ads this Tuesday. [00:00:08] The closed loop from exposure to transaction is the foundational value proposition of [00:00:15] every retail media pitch deck in existence. Marco Steinsack thinks there is an opportunity to flip that. Here's what he told me: "Brands wanna know what you're going to purchase, not what you purchased in the [00:00:30] past." And Steinsack says that because he is the general manager of Backpack Media, a new commerce media network built on top of Sallie, an education solutions company focused on helping students plan and [00:00:45] pay for college. [00:00:46] His previous role was running Sephora's media network, where he grew the business to what he says became more than two hundred million dollars in ad revenue within two and a half years, roughly three percent of the retailer's sales. [00:01:00] And Steinsack argues that transaction data is a lagging indicator. [00:01:04] Life stage data is a leading one, and Sallie, through its direct relationships with students, parents, and recent graduates, has life stage [00:01:15] signals that no retailer can match. Let's jump in. [00:01:19] [00:01:20] Kiri voiceover: So it's called Backpack Media, and Sallie calls this the first education-powered media network in commerce media. Through [00:01:30] Sallie's private student loan business and broader education support ecosystem, Steinsack says that the network reaches two-thirds of college-bound freshmen each year And that reach produces a special kind of signal. [00:01:43] Sally knows [00:01:45] when someone is planning for college, enrolling, choosing a major, living on or off campus, graduating, and making their first independent financial decisions. Layering data science on top of those signals, Steinseck's first [00:02:00] five hires were all data scientists, and you can start to see how modeling future behavior comes with some precision. [00:02:08] So this is all based off an interview that I did with Marco Stanczak for my column at The Drum, and I'll link up to the [00:02:15] full a-article in the show notes here. Wanna touch on a couple more things. One is how even though this network is built off the back of a student loans company, also an education [00:02:30] services company, Marco was at pains to point out that they're not monetizing transaction data. [00:02:38] He says, "What we're actually doing is giving brands access to our customer relationships. They have strong and [00:02:45] differentiated customer relationships even without huge transaction volume." And he points back to his time at Sephora that it never would be able to compete with Amazon or Walmart on reach, but Sephora's depth of [00:03:00] relationship with its beauty customers drove that media business to a three percent of sales volume much faster than a lot of networks usually achieve. [00:03:11] And that means building a network based on [00:03:15] really strategic endemic advertisers. [00:03:19] And he's operating Sallie the same way. The customer relationships are deep and differentiated, the data is sensitive, and the network has to be selective about which [00:03:30] advertisers it works with because the moments that it monetizes being a student's first financial decisions are high stakes for customers. [00:03:40] Now I want to turn to a recent interview that Marco did on The [00:03:45] Middlemen podcast, where he talks about what things are like actually building inside of a financial services provider, because it certainly comes with its own quirks. Let's listen [00:03:56] Marco: The steepest learning curve for me has been around [00:04:00] working within a financial institution, right? So we're, we're education finance. And so I have been getting a crash course on what that means and standing up a, a commerce media business within that context. Um, [00:04:15] very different from a retailer and, you know, everything that we do is about privacy. [00:04:21] Everything that we do is around trust. Uh, we never sell personal data ever. Everything's anonymized, everything's aggregated, brands never [00:04:30] see individual level information. So that's been really important. Um, it's also been important for us to be selective about who we partner with and, and which brands that we work with, um, because this is a very high stakes moment for, for college students. [00:04:44] And, you [00:04:45] know, for us, we have to anchor to, is this offer relevant to this person? And if we can anchor to that, then the rest we can solve. Um, in terms of go to market, [00:05:00] I think part of it, and we were just talking about this this morning with the cargo team, um, part of it is in your sales motion, uh, how much are you incorporating your audience set into an [00:05:15] existing RFP where the buyer is looking for something very specific? [00:05:21] tom limongello: Right. [00:05:21] Marco: Versus how much are you, um, getting the opportunity to, like, give them your whole go to market value prop, right? 'Cause we're [00:05:30] something very different. This is, um, you know, it's, it's education media, what does that mean? And it's predictive commerce media, powered by verified life stage data. What does that mean? [00:05:42] You know, so if you're getting an RFP that says, "I [00:05:45] want Gen Z." Okay, great. We have that, but we also wanna be able to tell our story a little bit more broadly. And so we have been, you know, winning moments to tell that story and share how we're different, [00:06:00] um, and, you know, the more that that shows up in performance for brands, you know, the more momentum we gain. Retailers know that a marketplace [00:06:15] model can dramatically boost product assortment, shopper engagement, and total revenue. But to get the most out of your marketplace, you need an ad tech solution that can really engage sellers. Miracle Ads is powering [00:06:30] the future of retail media for leading retailers to activate both three P Sellers and one P brands. [00:06:37] Kiri Masters: Learn more@miracle.com. That's M-I-R-A-K l.com. [00:06:44] [00:06:44] Kiri voiceover: Now, I [00:06:45] did ask Marco a really annoying question in the interview, which is, can we really call this commerce media? I mean, there's no transactions, and when we look at the IAB definition of commerce media, it calls it a [00:07:00] practice where companies use their physical assets, digital properties, first-party data, and connection to the shopper journey to create integrated media opportunities with access to real transaction data. [00:07:12] So Backpack Media fits some of [00:07:15] that, but not all of it. There is first-party data. There is a c- connection to consequential consumer decisions, but there's no point of purchase, and there's no closed loop in the traditional sense. So when I put that to Marco, [00:07:30] he said, "Look, we're not monetizing past transactions. [00:07:33] What we're doing is creating predictive commerce media." And he says, "I guess it depends on how you define commerce. To me, we are a media network." [00:07:45] So perhaps what is being built here in the commerce media landscape is outrunning the categories that the industry created to describe it. You know, we have financial services media like Chase Media Solutions, American [00:08:00] Express, PayPal. [00:08:02] We also have entities like Blue Light Card in the UK, who is building a membership data-led media network targeting verified frontline and public sector workers. [00:08:15] So each of those fits some of the IAB definition and not others. [00:08:22] And the reason why this is important is that all of this discussion around measurement and [00:08:30] standards may need to flex by advertiser type, by network type, by what kind of outcomes they're able to drive. All of these companies are thinking about attribution [00:08:45] differently, [00:08:45] and they might need to bring in different third-party measurement providers. [00:08:51] So I'm gonna wrap it up there. There was a couple more topics that I was able to cover in the column that we can't get to today. [00:08:57] But I think this is interesting because this is [00:09:00] early. This is an unproven concept at scale They haven't announced any partners, uh, yet. Not to say that they don't have them, but they don't have any publicly announced yet. And [00:09:15] this predictive commerce thesis hasn't been validated by the kind of independent research that would, you know, bring in conceivably a raft of advertisers right off the bat. [00:09:29] But if the [00:09:30] thesis holds that life stage signals outperform transaction data as a predictor of future purchases, the implication that the industry's fixation on closed loop transaction data might be optimizing for a signal [00:09:45] that could lose its edge in an AI-mediated shopping world. [00:09:50] And the networks worth watching might be the ones who built on data that the rest of the industry doesn't have access to. Thanks for listening, and I'll catch you [00:10:00] next week [00:10:00]