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Index/Marketing/Retail Media Breakfast Club
Retail Media Breakfast Club artwork

Same Words, Different Forks: Recap of eMarketer & Sensor Tower Commerce Media Executive Briefing at Cannes Lions

Retail Media Breakfast Club · 2026-06-26 · 10 min

0:00--:--

Key moments - from our scoring

Substance score

67 / 100

Five dimensions, 20 points each

Insight Density14 / 20
Originality13 / 20
Guest Caliber15 / 20
Specificity & Evidence13 / 20
Conversational Craft12 / 20

At Cannes Lions, eMarketer and Sensor Tower convened retail media leaders to diagnose what's constraining the channel's growth. Sarah Marzano projected US retail media reaching $100 billion by 2029 - the same year growth enters single digits - signaling maturation alongside significant unevenness in how retailers execute. The real problem, though, isn't terminology: it's conflicting mental models. Claudia Johnson (Omnicom Flywheel) crystallized this with her fork analogy - media teams and retail teams both believe in retail media but use it differently, like brushing hair versus eating steak. Shweta Bhardwaj (Bain & Company) flagged that brands have grown wary of retail media because they're pressured to defund top-of-funnel spending for short-term conversion wins. The fix is organizational: pulling media decisions into one team managing the full funnel. The panel also wrestled with dark search - AI-driven purchase decisions made before the retailer touches the customer. Andrew Lipsman argued the real gap is attribution, not invisibility, while Debbie Aho Williamson pointed out that early ChatGPT advertisers (heavily retailers like Best Buy) are among the most exposed to AI-driven discovery shifts. The analysts left unsettled whether AI is a distraction from onsite opportunity or already reshaping retail fundamentally.

Key takeaways

  • →US retail media will reach $100 billion by 2029, the same year growth enters single digits, indicating the channel is maturing with significant operational unevenness underneath.
  • →Retail media networks suffer from silos where media teams, sales teams, and the broader organization define success differently - Claudia Johnson's 'different forks' analogy captured how common language masks conflicting mental models.
  • →Brands are increasingly suspicious of retail media because they're pressured to starve top-of-funnel investment to hit next-day conversion goals, requiring operating model changes that consolidate media decisions across the organization.
  • →Dark search - AI-driven purchases decided before landing on retailer sites - is primarily an attribution problem, not an invisibility problem; panel data can show the behavior even without UTM tagging granularity.
  • →Early ChatGPT advertisers (retailers and brands) may be among the most exposed to discovery shifts from AI, making their heavy early investment potentially shortsighted if conversion models don't adapt.

In this episode

  1. 1Retail Media at Scale: eMarketer's $100 Billion Forecast and Maturity Signals
  2. 2The Fork Problem: How Teams Use the Same Words but Mean Different Things
  3. 3Brand Skepticism and Operating Model Solutions for Retail Media
  4. 4Dark Search and AI-Assisted Discovery: Attribution Challenges and Panel Data
  5. 5The Great AI Debate: Evolution vs. Revolution in Commerce
  6. 6Best Buy's ChatGPT Strategy Shift: Early Dominance to Product-Only Focus

Mentioned

eMarketerSensor TowerMiracle AdsBain & CompanyOmnicom FlywheelThe CPG GuysShopifyRakutenAmazonBest BuyChatGPTAmazon Rufus

Guests

Andrew LipsmanSarah MarzanoClaudia JohnsonShweta BhardwajDebbie Aho WilliamsonIan Simpson

Topics in this episode

ChatGPT advertisingAmazon RufusRakutenCannes LionseMarketerSensor TowerBane & CompanyOmnicom FlywheelMiracle Adsdark search

Questions this episode answers

What growth forecast did eMarketer project for US retail media?

Sarah Marzano projected US retail media will pass $100 billion in ad revenue by 2029, the same year growth is projected to dip into single digits for the first time, signaling channel maturation.

What is 'dark search' and why is it a problem for retailers?

Dark search refers to purchase decisions made inside AI assistants before the shopper lands on a retailer site, appearing as direct traffic without referral tags; Shopify data shows 55% of AI-referred sessions land on product pages versus 20% for organic search, meaning retailers never influenced the decision.

Why are brand teams becoming skeptical of retail media?

Brands are suspicious because they're repeatedly asked to increase retail media spending while cutting top-of-funnel investment to help sales teams hit next-day conversion goals, creating unsustainable budget pressure.

What was Claudia Johnson's main critique of retail media alignment?

She argued the problem isn't common language but common understanding - media teams and retail teams use the same words but have fundamentally different mental models of what retail media should accomplish, like using a fork to brush hair versus eat steak.

Is AI a distraction or transformation for retail media?

Analysts disagreed: Andrew Lipsman framed AI as an evolution in search and shopping, while Debbie Aho Williamson suggested early AI adopters (retailers and brands) are among the most exposed to discovery shifts, leaving the question unsettled.

What our scoring noted

Our reviewer’s read on each dimension, with quotes from the episode.

Insight Density

14 / 20

The episode delivers several substantive analytical points - retail media maturation forecasts, the vocabulary/understanding gap in growth metrics, the dark search/dark funnel concept, and differing views on agentic commerce - but much of the substance is framed as recap rather than new argumentation, and there's a sizable ad read that dilutes density. The best insights (fork analogy, panel disagreement on AI) are valuable but not packed with novel operational detail.

The problem isn't common language, it's a common understanding. 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.
A lot of LLM-referred traffic now arrives on retailer sites, without any referral tag. So it looks like it is just coming to the retailer as direct traffic.

Originality

13 / 20

The episode presents fresh framing on existing problems - the fork metaphor is creative, the dark search concept is non-obvious, and the Best Buy/ChatGPT case study (vanishing from paid search) is an interesting data point. However, the core ideas (silos between teams, attribution challenges, AI reshaping commerce discovery) are not particularly contrarian or first-principles; they're thoughtful recaps of nascent industry conversation rather than original analysis.

The problem isn't common language, it's a common understanding.
dark search. Which is the term for a purchase decision that is made inside an AI assistant with the shopper landing on the retailer having already decided what they're going to buy.

Guest Caliber

15 / 20

Strong practitioner lineup: Sarah Marzano (VP analyst, eMarketer), Claudia Johnson (technical advisor to CEO at Omnicom Flywheel), Shweta Bhardwaj (partner at Bain), Andrew Lipsman (Media Ads and Commerce), and Debbie Aho Williamson (AI Ad Economy). These are senior practitioners and analysts with real operating leverage, though the episode is mostly Kiri Masters recapping their insights rather than letting them speak extensively. No lightweight celebrity guests.

Sarah Marzano, who is the VP and principal analyst covering commerce media at eMarketer, opened with a forecast.
Claudia Johnson, who is the technical advisor to the CEO at Omnicom Flywheel, had the line of the afternoon.

Specificity & Evidence

13 / 20

The episode includes specific data points (US retail media hitting $100B by 2029, 56% confidence in strategy alignment vs. 30-point drop in org belief, 55% of AI-referred sessions start on PDPs vs. 20% for organic, Amazon Rufus conversion lift from 21% to 58%, Best Buy's 28% of ChatGPT ad impressions in week one), but lacks depth on how these numbers were derived, sample sizes, methodologies, and dollar impact. Many claims are attributed to data sources without granular evidence presented in-episode.

US retail media is going to pass a hundred billion dollars in ad revenue by twenty twenty nine, and that is the same year that growth is gonna start dipping into single digits.
fifty-six percent of retail media network leaders 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 retail media strategy.

Conversational Craft

12 / 20

This is a recap episode where Kiri Masters largely paraphrases and narrates panel content rather than conducting direct interviews. The best moment - the disagreement between Andrew Lipsman and Kiri on agentic commerce - shows intellectual engagement, but the episode lacks real-time follow-ups, clarifying questions, or productive pushback on claims. The host is competent but passive; she's summarizing rather than probing.

Two sessions at this event were in effect, a room full of analysts arriving at the same place through different doors.
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.

Conversation analysis

Computed from the transcript - who did the talking, and the words that came up most.

Most-used words

media18retail14twenty8different7panel7retailer7fork6retailers6best6sessions5first5percent5brand5chatgpt5sensor4commerce4

Episode notes

Fresh from Cannes Lions, I'm unpacking one of the biggest themes that kept surfacing across analyst discussions: retail media's greatest obstacles aren't external - they're internal. While everyone is focused on AI, tariffs, and the next wave of disruption, the conversations I heard pointed somewhere much closer to home. Misaligned organizations, inconsistent measurement, and teams speaking the same language but meaning completely different things may be holding retail media back more than any emerging technology. I also share highlights from an analyst panel featuring Sarah Marzano, Andrew Lipsman, Debbie Aho Williamson and myself. We debated "dark search," AI-assisted shopping, attribution, and whether generative AI is fundamentally changing commerce, or simply accelerating trends that were already underway. If you're trying to understand where retail media is headed next, this episode connects the dots. This episode is sponsored by Mirakl Ads Timeline [01:00] eMarketer's forecast: Retail media surpasses $100 billion, and why slower growth could actually signal maturity.

Full transcript

10 min

Transcribed and scored by The B2B Podcast Index.

Same Words, Different Forks === 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 by The CPG Guys. I was on the closing analyst panel and sat in a couple of briefing sessions earlier in the afternoon. A lot of the conversations were about what is holding retail media back from all it could be And a 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 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. Let's jump in. Kiri Masters: Sarah Marzano, who is the VP and principal 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 same year that growth is gonna start dipping into single digits for the very first time. Her read is that the channel has the top-line traits of a mature ad medium, but a lot of 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 strategy and product roadmap, but less so on the foundational stuff, the operating model, team structure, measurement.

And inside the strategy pillar within a retailer, fifty-six percent of retail media network leaders 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 retail media strategy. 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 they don't mean the same thing.

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." 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. She says, "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 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. 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 I have been making about retail media networks aimed at the brand side of the table.

These silos aren't unique to retailers. ~Now, on the panel that I was part of with Sarah Marzano, Andrew Lipsman~ ~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, Sarah Marzano, and Debbie Aho Williamson. On this panel, Ian asked me to dig into this topic of dark search, Which is the term for a purchase decision that is made inside an AI assistant with the shopper landing on the 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 publisher sites, without any referral tag. 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 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 referred from AI start on a PDP at a retailer, compared to just twenty percent of traffic starting on a PDP for organic search. And what this means is that the decision got made somewhere that the retailer and brand never touched or influenced. Now, Andrew Lipsman from Media Ads and Commerce pushed on this framing- His point, elaborated on a blog post that I'll share in the show notes as well, 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 whole picture. 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 have navigated imprecise purchase journeys forever: word of mouth, the physical store, the social swipe up, TV ads. Her case for giving them some credit is that they're used to customers changing how they decide and 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 be? Miracle Ads is the Ad Tech solution trusted by Rakuten and over 50 global enterprise retailers.

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com. That's M-I-R-A-K l.com. 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 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 all.

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 purchase. He cites data about Amazon's Rufus assistant, saying that this is evidence of the case.

Amazon sessions convert at a twenty-one percent baseline, which rises to fifty-eight 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 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. And her example that she shared 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 with a translated sentence to hand to the pharmacist, who then found two products for her. 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 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 top advertiser over the full period. 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 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 reminder to come in to Best Buy and check out the deals. 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 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 it the thing that's already reshaping discovery? We didn't settle it this time. Thanks for listening.

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