Eric Franchi

Excited to share that Aperiam has invested in Bedrock Platform. Obvious statement of the century: programmatic advertising has a complexity problem. The infrastructure that was built to give media buyers control has, over time, done the opposite. Burying teams under layers of operational overhead, legacy tooling, and opaque supply chains. Bedrock is built on a simple but powerful thesis: what if you stripped away the bloat and rebuilt the stack around what actually matters... precision, transparency, and speed to execution. Their platform combines AI-driven campaign automation with curated, premium inventory across video, CTV, DOOH, and audio, and aims to be flexible and get smarter over time. Plus, the team (Shane, Austin, Ryan, James) has deep roots in the infrastructure layer of ad tech and understands the plumbing better than almost anyone. We believe the next generation of programmatic will be leaner, faster and AI-first. Bedrock is building that.

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Sooo... how can the same product, from the same company, with the same experience; suddenly grab market share from the market leader. No flashy launch, no innovation, no nothing. Change your brand positioning. How about if you are Anthropic (Claude), and you go up against a powerful government department like the DoW? Your intent was not brand position change; that'd be too pedestrian. There is of course, a lot of nuances in what happened; but the market does not care about the nuance - most of which I am most likely missing. The market cares about what they see and hear and feel. The Brand Positioning Deck on Amodei's laptop on never changed. Perceptions of the market changed of what Anthropic was. What it stood for, how far it will go for something as abstract, yet important as principles. As an aside: Amodei has re-engaged DoW and opened negotiations again as of late last week. Anthropic is still a business; and sometimes the market make a lot of assumptions, so you never know what is going on behind the scenes. But the users have made up their minds of what OpenAI and Anthropic stands for - rightly or wrongly. Both CEOs played a big role in this evolution. Speaking of CEOs and big companies and their 'products,'.... Nah, that'd be too easy. https://lnkd.in/gyfapkyF

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My First AI Post @2025 (and thank you to the Aperiam AI Fellows and to Dawn West and Alex Netelkos for making that happen!): If viewability was the training wheels of digital measurement, attention is the carbon-fiber racing bike we’ve just learned to ride. Erica kicked us off by pointing out the obvious: “viewable” doesn’t mean “seen,” and certainly doesn’t mean “engaged.” McKinsey’s neat formula — time × focus × intent — resonated as a starting point, but the real magic is in using that across channels and across the funnel. Why does this matter now? Because omni-channel is no longer a buzzword — it’s the messy, lived reality. CTV, digital out-of-home, mobile gaming, social feeds — each has wildly different engagement norms. An attention metric gives marketers and publishers a lingua franca, a Rosetta Stone to compare value and outcomes without pretending a click on a banner is the same as leaning into a big-screen spot. Publishers want credit for premium environments. Marketers want to normalize buys across hundreds of brands, countries, and media types. Attention-weighted CPMs (ACPMs) start to solve both. Even a global CPG can look across Meta, YouTube, stadium signage, and print — and make sense of it. Is it perfect? No. Is it better than a stack of inconsistent KPIs and gut feel? Absolutely. And yes, AI is in the wings — not as the protagonist, but as the stagehand making the hard stuff easier. It can normalize messy data, connect attention signals to outcomes, and embolden teams to try bolder experiments. Which brings us to the closing provocation: What if a few brave brands and publishers agreed to really transact on attention — rough edges, no gaming, shared responsibility for outcomes? The moment feels right to test whether attention can become not just a metric, but a currency. Marc Guldimann Adelaide David Zapata AB InBev Michael Shields Cory Treffiletti Rembrand McKinsey & Company

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Advanced measurement is moving from a “nice to have” to a requirement for brands that want to prove how marketing drives real business outcomes.✅ Nowhere is this more visible than in Retail Media Networks. RMNs like Instacart give brands access to closed-loop performance data tied to actual purchases. That data is powerful, but it also raises an important question for marketing leaders: How do we validate results beyond platform-reported metrics and understand true incremental impact? This is why third-party measurement is becoming a critical part of the RMN conversation. Circana has emerged as a leading independent measurement partner for retail media performance, including Instacart. By combining retailer point-of-sale data with broader market and category intelligence, Circana helps brands understand not just what sold, but what actually changed because of advertising. https://lnkd.in/gp4CghWf For food and beverage brands, this matters because RMN performance rarely exists in isolation. Retail media influences household penetration, repeat rates, brand switching, and category growth, not just attributed sales within a single platform. Third-party measurement helps separate signal from noise by providing a consistent view across retailers, channels, and time. The broader industry trend is clear. As RMN budgets grow, marketers are being asked to answer harder questions around incrementality, brand impact, and long-term growth. Platform dashboards alone are no longer enough. At Effective, we see advanced media performance reporting as a layered system. Platform data shows what happened. Third-party measurement helps explain why it happened and whether it was truly incremental. Together, they create a clearer connection between media investment and business results. This is how performance reporting evolves from tracking activity to proving impact. Jeff O'Shaughnessy Joelle Kaplan #MarketingMeasurement #RetailMedia #MediaPerformance #Incrementality #F&B #MarketingEffectiveness

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Here's a great example of AI-first adtech creating real business value for media companies. Aperiam portco Gray Swan is partnering with Operative to bring AI-powered observability to media companies managing converged linear, streaming, and digital advertising operations. GraySwan's agentic AI layer continuously monitors performance signals across supply, demand, pricing, and delivery. It automatically surfaces anomalies, inventory and pricing optimizations, and incremental revenue opportunities in real time. No more hours spent pulling reports or missing critical signals. As Zeev Neumeier, GraySwan's Founder and CEO, put it: media is now a data-driven business, and the power comes from turning that data into action. Proud to support the team GraySwan as they bring this vision to the world's leading media brands.

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For years, we've watched retail media continue to fragment often operating on the promise of "closed-loop data" rather than verifiable, standardized results. In 2025, with IAB's new Commerce Media Standards going live, we're seeing the inevitable fallout: brand satisfaction with RMN transparency has plummeted from 66% to 44%. The market is demanding accountability. Brands are done asking for proprietary dashboards; they're demanding shared, IAB-aligned proof. The old JBP (Joint Business Plan) is evolving into something more like the JVP (Joint Value Partnership), where outcomes replace obligations and standardization is the currency. 👉 Read the full perspective : https://lnkd.in/giDQc9jY #RetailMedia #JBP #JVP #CommerceMedia #Transparency #MediaAMP

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On paper Boost Mobile x Liquid Death have no business doing a collab. In culture it makes perfect sense. There is no overlap of products, distribution, or consumption occasions. So why would they team up if there is no category adjacency. What matters here is shared positioning. Both brands define themselves as disruptors in industries where consumers have long felt taken advantage of. → Liquid Death takes bottled water and flips it with irreverence.  → Boost takes on the Big Three carriers with a promise of transparency. The common ground is not the product, it’s the attitude. That is why the Cellphone Bill character feels so natural. It is grotesque, absurd, and culturally resonant. It looks and feels like a Liquid Death idea, but it dramatizes the pain points of the wireless industry. In that move, Boost borrows credibility and attention from an audience already primed to enjoy it. Partnerships like this don't need to make sense in a traditional way.  They are about association and shared codes. → Associating with Liquid Death transfers its entertainment-first equity to Boost. → Associating with Boost gives Liquid Death a new canvas to extend its creative IP. For Boost, the strategy is clear. They cannot outspend Verizon or AT&T, but they can out-entertain them and dramatize consumer frustration in ways incumbents never will. For Liquid Death, it is proof that the brand is not just a canned drink, but a cultural IP factory that can collaborate with other disruptors. Together, this is less about selling phone plans or water and more about building relevance through shared cultural disruption. That association is enough.

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Retail Media – What’s Driving Growth? A new Digiday piece highlights how US CPG advertisers are moving away from third‑party cookies and doubling down on CTV, contextual, and retail media as alternatives. That mirrors what we found in our Ascentinel survey of the UK retail media market. While the survey wasn’t solely FMCG focused, there were strong parallels when asking the buy side what’s driving growth. The top three ranked drivers were: 1) First‑/second‑party data access 2) Closed‑loop measurement / sales attribution 3) Proximity to purchase / the digital shelf Is it a surprise that cookie decline ranked much further down the list? Advertisers are actively seeking more accountable, closer‑to‑purchase signals. For anyone building or scaling a retail media network - or supporting those who do - the direction of travel is clear: >Standardised data & measurement >Quality of data and the ability to measure ROAS >The cookie fading into the background If you’d like to explore the insights from the study - from competitor rankings to market growth - let’s connect. https://lnkd.in/eFFPqZV5

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The end cap is the prime location for in-store retail media, so it makes sense as a space ripe with innovation potential. Retailers such as Kroger and CVS Pharmacy are beginning to install digital screens on end caps to expand their in-store advertising networks, opening up new opportunities to appeal to consumers. Along with product advertising, these screens can be used to highlight seasonal promotions, sales events, and other time sensitive events. 🔗https://lnkd.in/e4tRfiVE

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The Senate is tightening teen ad targeting; the House will vote on a bill that bars behavior-based ads for under-17s, pushing brands toward contextual plays.

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If you don’t verify the audiences your ads actually reach, how often and where, you’re never going to see investments and outcomes clearly. It's that simple. (Anyone who says differently isn’t being honest.) Props to our partners at Walmart Connect for backing up their awesome platform with iSpot's ability to prove it against hundreds of streaming and linear channels. Transparency FTW. I won't jump on the nose joke bandwagon. This is serious stuff! Brings a happy tear to my eye, however!😊 #unifiedmeasurement #integritymatters #transparency

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Meta’s Agentic Commerce signals a bigger shift than shopping features. eComm brand owners take note. There was an interesting moment in Meta’s latest earnings call that didn’t get a lot of mainstream attention. Zuckerberg and CFO Chad Heaton discussed “agentic shopping tools”. AI agents that help people find very specific products, remember preferences over time, and eventually make decisions on a user’s behalf. They described these agents as sitting between shoppers and merchants, blending subscriptions, advertising, and commerce into one experience. On the surface, it sounds like another product roadmap update. But if you’ve spent time in eCommerce, it’s hard not to read this as something more structural. Most online shopping today still relies on the same basic behaviour: people browse, compare, get influenced, and then decide. Agentic shopping flips that sequence. Instead of someone scrolling through options, they tell an AI what they want and the AI does the filtering. Not just by keywords, but by memory. Past purchases. Returns. Preferences they’ve never formally written down but have shown through behaviour. Think less “recommendations” and more delegation. “Find me something like the jacket I bought last winter, but lighter, under $250, and not from a brand that took three weeks to ship.” That’s not search. That’s outsourcing the decision. Meta’s interest here makes sense when you zoom out. If an AI agent becomes the place where people decide what to buy, then Meta isn’t just hosting ads anymore. It’s shaping the decision environment itself. Ads, subscriptions, product discovery, and checkout start to blur together. The platform isn’t trying to send traffic somewhere else, it’s trying to mediate the entire journey. In that setup, the value isn’t attention. It’s trust and relevance at the exact moment a decision is being made. This is where things change for eCommerce brands. When the “buyer” is an AI agent, a lot of the tactics we’ve relied on for years start to matter less. Hooks don’t impress an algorithm. Brand storytelling doesn’t override poor fulfilment. A polished funnel doesn’t fix inconsistent sizing or high return rates. Agents evaluate patterns. They learn which brands deliver on time, which ones cause friction, which products get kept. Over time, that becomes the filter through which brands are surfaced. What’s interesting is that this rewards the unglamorous stuff. It rewards brands that are clear on who they’re for, realistic with pricing, operationally sound, and consistent over time. In a world where people stop browsing and start delegating, it’s no longer: “How do we get more people to click?” It becomes: “Would an AI confidently recommend us without needing persuasion?” That’s a harder question. But it’s also a more honest one. And it suggests that the next phase of eCommerce growth will come from being genuinely easy to choose.

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Brands pay for eyeballs. Sponsored search, hero banners, featured shelf positions - all designed to catch a shopper mid-browse. Now ask yourself: what happens when the shopper isn't browsing at all? Agentic AI is quietly reshaping how purchasing decisions get made. AI systems that don't just recommend - they retrieve, evaluate and execute. Autonomously. The implications for retail media are significant. Not in five years. Now, in the way the infrastructure and data strategies are being set. The retailers who expose clean, structured, machine-readable data will be discoverable. The ones whose product information is locked in formats built for human screens may simply be invisible. Same economics - brands paying for advantage - but a fundamentally different delivery mechanism. I've broken this down in a short carousel. Would genuinely welcome thoughts from anyone working in this space - is this on your radar yet? #RetailMedia #AgenticAI #DataStrategy #Loyalty #id8retail

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Two common scenarios: 1) You're a CPG. You have 50+ RMNs chasing you for commitments. Or 2) You're an RMN, competing with 50+ others, many of whom are much larger than you. How do you allocate as a brand? How do you compete as a mid-size retailer? Angela Myers gives fantastic advice. https://lnkd.in/gCjggFd4

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we've posted a couple of reports in the past - but I think that this one is the BEST and most interesting one we published (...so far...) this is a true comparison of "attribution" vs. incrementality - comparing the actual aggregated, weighted, and indexed results based for the clients who share attribution data with INCRMNTAL Super interesting - get it below!

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Nearly three out of four shoppers now point to Retail Media Networks as a key influence on what they buy. That momentum is changing everything for brands. Caroline Tolbert and Jeremy Rothschild will be at Ad Age’s Business of Brands conference to share the results from our recent survey of top brand executives. The survey reveals how leaders are thinking about today’s shopper journey and the strategies that create real engagement. They are excited to spotlight the creativity and leadership making waves at the 2025 𝗕𝗿𝗲𝗮𝗸𝗼𝘂𝘁 𝗕𝗿𝗮𝗻𝗱𝘀 𝗔𝘄𝗮𝗿𝗱𝘀, honoring the work that is moving the industry forward. If you want to hear what is driving results for brands right now, be sure to connect with Caroline and Jeremy at Business of Brands. The future of content and commerce is happening here.

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Meta just confirmed what I've been arguing for months. In their Q4 earnings call, they explicitly stated that investments in GEM (Generative Engagement Models) are improving ad performance and attracting more ad dollars. [FULL QUOTE BELOW] Most marketers will hear this and think: "More AI hype." They're wrong. Meta has fundamentally re-engineered its ad platform at the technical level. It's invisible to most people: -Consumers just see better ads at the right time -Advertisers see the same campaign manager interface -Unless you've hit a scaling wall, you might not notice anything I wrote about this shift a few weeks ago (link in comments): Meta moved from interest-based targeting to predictive intent. From "show ads to people who like yoga" to "show ads to people our system predicts will buy yoga mats in the next 72 hours, regardless of their stated interests." That's not an incremental improvement. That's a different product. Here's what frustrates me about all the AI hype: everyone's trying to predict how this technology will change the world in 10 years. Meanwhile, the companies that invented this stuff—Google, Meta—are already using it. Not in some flashy consumer app. In their ad stacks. Because that's what drives their stock price. If you're still running Meta ads the same way you did two years ago—optimizing around interests, testing endless creative variations, obsessing over attribution—you're putting the same gas you use for your Corolla into a rocket ship. The platform has changed. Your strategy needs to change with it. What does that mean practically? Feed the algorithm more signals. Give it conversion data. Give it the creative diversity it needs to learn what works. Let it find your customers instead of you trying to define them. Trust the predictive engine you're paying to access. The marketers who adapt to this shift will scale. The ones who don't will keep hitting walls they can't explain. --- Susan Li, CFO "In Q4, we doubled the number of GPUs we used to train our GEM model for ads ranking. We also adopted a new sequence learning model architecture, which is capable of using longer sequences of user behavior and processing much richer information about each piece of content. The GEM and sequence learning improvements together drove a 3.5% lift in ad clicks on Facebook and a more than 1% gain in conversions on Instagram in Q4. This new sequence learning architecture is significantly more efficient than our prior architectures, which should enable us to further scale up the data, complexity and compute we use in our future ranking models to deliver performance gains. As we scale up our foundational ads models like GEM, we are also developing more advanced models to use downstream of them at run-time for ads inference. In Q4, we launched a new runtime model across Instagram Feed, Stories, and Reels, resulting in a 3% increase in conversion rates in Q4."

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Love this - AI is the Oppenheimer moment for marketing. It fundamentally collapses the time and cost of production. A $2.5 million campaign that used to take four months can now be delivered for $500,000 in four weeks. Legacy firms that charge based on hours with the 'time-driven' model are existentially threatened by this.

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