Latest / Retail Media Breakfast Club / Same Words, Different Forks: Recap of eMarketer & Sensor Tower Commerce Media Executive Briefing at Cannes Lions
Transcript
- Same Words, Different Forks === [00:00:00] Kiri Masters: The Cannes Lion Festival of Creativity is firmly in its retail media era, and that was no more true than on Monday afternoon. eMarketer and Sensor Tower ran an executive briefing on commerce media hosted [00:00:15] by The CPG Guys. I was on the closing analyst panel and sat in a couple of briefing sessions earlier in the afternoon. [00:00:23] A lot of the conversations were about what is holding retail media back from all it could be [00:00:29] And a [00:00:30] few weeks ago, I started a series with Anne Halik of Miracle Ads called "The Demons Inside Retail Media." The argument being that the biggest threats to retail media in twenty twenty six are internal, not the AI and tariffs stuff [00:00:45] that everyone's bracing for. Two sessions at this event were in effect, a room full of analysts arriving at the same place through different doors. [00:00:54] Let's jump in. [00:00:55] [00:00:57] Kiri Masters: Sarah Marzano, who is the VP and principal [00:01:00] analyst covering commerce media at eMarketer, opened with a forecast. US retail media is going to pass a hundred billion dollars in ad revenue by twenty twenty nine, and that is the [00:01:15] same year that growth is gonna start dipping into single digits for the very first time. [00:01:22] Her read is that the channel has the top-line traits of a mature ad medium, but a lot of [00:01:30] unevenness underneath because players hit scale at very different speeds. Another sobering number came from eMarketer's survey of retail media network leaders run with Bane. Leaders feel confident on [00:01:45] strategy and product roadmap, but less so on the foundational stuff, the operating model, team structure, measurement. [00:01:53] And inside the strategy pillar within a retailer, fifty-six percent of retail media network leaders [00:02:00] said that they were very confident that their retail media goals aligned with their organization's objectives. But there was a roughly thirty-point drop when asked whether the broader organization really believed in that [00:02:15] retail media strategy. [00:02:16] Next up, there was a panel talking about the growth engine of retail media, and this one kept snagging on vocabulary. Growth, loyalty, incrementality, everyone is using these words, but perhaps [00:02:30] they don't mean the same thing. [00:02:31] Claudia Johnson, who is the technical advisor to the CEO at Omnicom Flywheel, had the line of the afternoon. She said, "The problem isn't common language, it's a common understanding." [00:02:45] Her analogy was the fork from The Little Mermaid. The media and creative teams are brushing their hair with the fork. The retail team, the rest of the enterprise, is using their fork to eat steak. [00:02:59] She says, [00:03:00] "We both know what a fork is. We both believe in the fork, but we have very different understandings of what the fork should be doing." Shweta Bhardwaj, who is a partner, consumer products at Bain & Company, brought the brand [00:03:15] side version to the table. She says that brand teams have grown suspicious of retail media because they keep getting asked to spend more on it and to starve the top of the funnel so that sales teams can hit next day conversion goals. [00:03:29] [00:03:30] The fix that she's seeing at sophisticated advertisers is an operating model change, pulling the media out of multiple corners of the org into one group that looks at the full funnel. Now, this is the same point that Anne and [00:03:45] I have been making about retail media networks aimed at the brand side of the table. [00:03:49] These silos aren't unique to retailers. ~Now, on the panel that I was part of with Sarah Marzano, Andrew Lipsman~ [00:03:53] ~And Debbie Aho Williamson, we... Ian asked, Ian... Oh. ~Now, on the panel that I was part of, hosted by Ian Simpson from Sensor Tower and featuring me, Andrew Lipsman, [00:04:00] Sarah Marzano, and Debbie Aho Williamson. On this panel, Ian asked me to dig into this topic of dark search, [00:04:08] Which is the term for a purchase decision that is made inside an AI assistant with the shopper landing on the [00:04:15] retailer having already decided what they're going to buy. I've written about it in previous newsletters. I'll link up to it in the show notes. Now, here's the idea. A lot of LLM-referred traffic now arrives on retailer sites, but also [00:04:30] publisher sites, without any referral tag. [00:04:33] So it looks like it is just coming to the retailer as direct traffic. Retailers see a surge in direct traffic and don't always clock that it might [00:04:45] be coming from AI assistance, and a growing share of LLM-referred users are landing straight on a product page. Shopify's Q1 twenty twenty-six read is that fifty-five percent of sessions that are [00:05:00] referred from AI start on a PDP at a retailer, compared to just twenty percent of traffic starting on a PDP for organic search. [00:05:12] And what this means is that the decision got [00:05:15] made somewhere that the retailer and brand never touched or influenced. Now, Andrew Lipsman from Media Ads and Commerce pushed on this framing- [00:05:25] His point, elaborated on a blog post that I'll share in the show notes as well, [00:05:30] is that it might be dark from an analytics view, but it's not truly dark. Panel-based data like sensor towers can show you the visit that preceded the visit. You might lose the UTM tagging granularity, but not necessarily the [00:05:45] whole picture. [00:05:45] His argument is that the core gap is in attribution, not in whether the behavior can be observed at all, and those are different problems with different fixes. And Sarah jumped in with a longer view. Retailers [00:06:00] have navigated imprecise purchase journeys forever: word of mouth, the physical store, the social swipe up, TV ads. [00:06:09] Her case for giving them some credit is that they're used to customers changing how they decide and [00:06:15] showing up anyway, and that retailers were among the first advertisers into ChatGPT ads. That kind of supports this notion. It's fuzzy. We know it's fuzzy. We've figured out some things along the way. How different could it really [00:06:30] be? Miracle Ads is the Ad Tech solution trusted by Rakuten and over 50 global enterprise retailers. That's because Miracle Ads was [00:06:45] built with both three P Marketplace sellers and one P suppliers in mind. Both advertiser audiences demand a seamless advertising journey from onboarding to reporting. [00:06:56] You can offer everything from sponsored products to [00:07:00] video ads all in one solution. Learn more@miracle.com. That's M-I-R-A-K l.com. [00:07:08] [00:07:09] Kiri Masters: Now on AI, of course, this group of analysts didn't agree. I think that was kind of the point of bringing us all [00:07:15] together. Andrew and I have been running this debate in public for months. His reality check that he most recently posted is the most recent volley back to me. His position is that most of what gets called agentic commerce isn't agentic at [00:07:30] all. [00:07:30] We could call it advanced search or AI-assisted shopping. Personally, I'm fine with that. Agentic means autonomous decision-making, and he doesn't see humans wanting to hand over the middle of the funnel where we build conviction in a [00:07:45] purchase. He cites data about Amazon's Rufus assistant, saying that this is evidence of the case. [00:07:52] Amazon sessions convert at a twenty-one percent baseline, which rises to fifty-eight [00:08:00] percent for sessions including eleven or more Rufus queries. There is a link here, more AI querying, more conversion, not less, and he calls this an evolution, but not a revolution. Now, Debbie Aho [00:08:15] Williamson, who writes an excellent blog called The AI Ad Economy, sat in the other chair, the self-described vegan at a barbecue, the AI person in a room full of retail media people. [00:08:28] And her example that she shared [00:08:30] this morning was she was in France. She woke up this morning with a red, swollen eye. She asked ChatGPT for help. She got pointed to a specific French pharmacy because ChatGPT knew where she was in France [00:08:45] with a translated sentence to hand to the pharmacist, who then found two products for her. [00:08:50] This is the kind of pathway which ends up with a retailer, ends up with a physical transaction, but the decision-making all happened much earlier. Debbie [00:09:00] also shared some Sensor Tower numbers on early ChatGPT advertising. She says that shopping brands, as in retailers, consumer brands, were nearly 40% of ad impressions in the first few months, with Best Buy the [00:09:15] top advertiser over the full period. [00:09:17] Now, the detail underneath this is the interesting part, because Best Buy went really big out of the gate. About 28% of all impressions in the first week [00:09:30] belonged to Best Buy. But then by mid-May, Best Buy had vanished from the data entirely, and every Best Buy ad that she could find was a product ad, not a brand ad, not a [00:09:45] reminder to come in to Best Buy and check out the deals. [00:09:47] Her framing, which is the uncomfortable one for most people in the room, is that the categories which are investing first in ChatGPT ads are among the most exposed to the discovery shift that [00:10:00] AI is creating. So four analysts and on a panel reading the same moment very differently. Is AI a distraction from billions of dollars in low-hanging onsite advertising fruit, or is [00:10:15] it the thing that's already reshaping discovery? [00:10:18] We didn't settle it this time. Thanks for listening. I'll catch you soon [00:10:23]