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10 minutes of expert insights every weekday. Your morning ritual for staying ahead in retail media.
Editorial
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[00:00:00] Kiri: Retail media teams want to fill every ad slot on their website. Merchant teams want to sell product, and at most retailers, these goals are kind of at war with each other, and shoppers [00:00:15] are caught in the crossfire. Show me the incentive and I'll show you the outcome.
[00:00:20] That's something that Charlie Munger once said. When one team gets paid to fill ad slots and another gets paid to sell products, you get [00:00:30] exactly what you'd expect
[00:00:31] Irrelevant sponsored product ads clogging up search results while relevant products get buried. Today's episode features some commentary from a live stream that I did last [00:00:45] week on LinkedIn with Andreasen, CEO and co-founder of Penta Leap, who just recently released their second half, 20, 25 sponsored products Benchmark Report, and it shows cracks [00:01:00] forming in that old model of the organic product engine versus the ad product engine and a path forward that might finally end the merchant versus media civil war.
[00:01:13] Let's jump in.
[00:01:14]
[00:01:15] Kiri: Andreas made an argument that I hadn't really considered before, which is that the technology that's used to rank products by relevance on a website already exists, and it's existed for a long time. Retail [00:01:30] media just was never brought in to align with it.
[00:01:33] Andreas: . We think of retail media ad serving. Um, versus let's say an Algolia or Coveo or, um, [00:01:45] whoever runs onsite search as two totally different things. They are not. They're both about understanding queries and relevant products and sorting things in a way that truly makes [00:02:00] sense, hence brings money in.
[00:02:02] Um, so we are never talking about keywords and how to fix that problem when we. Um, onboard those, um, onsite search products like, um, I [00:02:15] Goia and, um, they're like 5, 6, 7 bigger players in that space. Bloom Ridge for example, also, and those products are specialized. That's what they have been doing for 10 plus years.
[00:02:29] In many [00:02:30] cases. They are specialized. Their core task is figure out which products are relevant for which query. So this problem got solved in the last 10 years. Now we have retail media [00:02:45] and we are operating in a silo trying to replicate that. Um, that's why, um, search organic and sponsored will merge into one.
[00:02:56] There's a centralized algorithm that defines relevance. [00:03:00] There are two revenue streams. And you, you sort products in a, in a comprehensive way. That makes sense. So there is, this isn't, uh, like a revolutionary problem to solve. Um, there, there is tech that [00:03:15] does this. It just hasn't been the. Number one priority for any retail media company.
[00:03:23] Reason why we have two types of ad servers today. The one ad server operates in a silo and tries to [00:03:30] reinvent the wheel, solve a problem that has been solved for ages already. And the other methodology is, and that's the thing we did, we you let the website preload, you check which products have bits, and [00:03:45] you reshuffled this in a comprehensive way.
[00:03:48] And you don't then deal with solving a problem that the search engine has solved already. And that's why there's such a big discrepancy between organic relevance and [00:04:00] paid relevance because it's very simple. Basic algorithms and regional media and high-end ai. Um, on the organic side, um, what retail media vendors do they claim their amazing, [00:04:15] um, their.
[00:04:15] Basic algorithms are high-end ai, which oftentimes they aren't. And when you test it, you will see that.
[00:04:21]
[00:04:23] Kiri: This disconnect explains so much of what we see in that benchmark data from Penta Leap. [00:04:30] Retailers using fixed placement strategies show low overall coverage with sporadic placement patterns. For example, office Depot and Lowe's both sit at about 24 to 36% coverage of [00:04:45] sponsored products on a search query.
[00:04:47] These networks have demand, but according to Andreas, they just can't match it to shoppers effectively because they're running to different systems that don't talk to each other. [00:05:00] Did you know that leading retail media networks drive 85% of their ads through mid and long tail advertisers?
[00:05:12] Kiri Masters: Miracle Ads provides full [00:05:15] funnel ad formats tailored to both one P and three P advertisers leveraging unique AI capabilities that provide unprecedented levels of relevance and engagement. Retailers who want to capture ad spend from [00:05:30] the long tail of three P Marketplace sellers use miracle ads in their tech stack.
[00:05:35] Learn more@miracle.com. That's M-I-R-A-K l.com.
[00:05:43] Kiri: Andreas used an analogy [00:05:45] that really stuck with me, which is that running fixed ad placements is like putting your slow sellers in the window of a Fifth Avenue store. Let's listen.
[00:05:56] Andreas: In the old world, you would say, [00:06:00] I. Reserve a carousel and certain tiles in the grid exclusively for ads. And then you try to put whatever demand you have into those tiles, [00:06:15] and then you, of course have diminishing returns. Um, actually you're even causing damage sometimes. Let's assume you have 10 tiles, you have one truly relevant product ad that can go in.
[00:06:29] And then you [00:06:30] have nine that are somewhat relevant. This is precious real estate. You waste people, first of all, will not click. The few that click will not buy. So it's like you running a [00:06:45] store on Fifth Avenue and putting your slow sellers into the store window. Correct. And it, of course doesn't work that way in the now.
[00:06:55] Fast forward today, the way, um, we solve [00:07:00] this is that you don't reserve tiles and fill them with ads. You, you see, do I have products that are super relevant and if so, I [00:07:15] dynamically rank them alongside organic products. So a, a search engine that, um, runs onsite search for retailers would usually sort products based upon an [00:07:30] expected margin per impression.
[00:07:32] So AI determines what do we expect, how much money will we make actually when we list a product? And the higher the margin per impression, the higher the product will rank. But [00:07:45] now, mm-hmm. You have two revenue streams. You sell products, so you get a retail margin in. But if a product has a bid and CPC money comes in.
[00:07:56] That same product may generate a [00:08:00] higher margin per click, which is not necessarily from retail, but partially from ad money that comes in, but doesn't matter. It brings in more money. So the product deserves to rank higher if you have a [00:08:15] shitty product that doesn't really match the context. Well, the imp um, the margin per impression is very low, so we just take it out.
[00:08:24] We would not show that. Mm-hmm. And in this case, you see there's a. [00:08:30] Brilliant way of managing the trade off between. I run a retail business, wanna sell product, but it should also be relevant. And the, the trade off is managed better in a dynamic way. And that's what we call fluid. And this is [00:08:45] where you see, um, many retailers, home Depots on this, Mac sees on it.
[00:08:50] Um. CVS Solando in Europe. Many others have this, let's say, organic, um, integration of sponsored [00:09:00] products. In the end, it's products. People don't want divided websites. Yeah, this is paid, this is organic. This is not how humans think, and this just makes no sense to run it that way.
[00:09:12]
[00:09:13] Kiri: Penta LEAP's Data shows [00:09:15] this shift happening in real time. Macy's move from heavily front loading ads in positions one to three on the page to spreading out sponsored products fluidly across the entire grid. [00:09:30] Home Depot shows a similar evolution.
[00:09:32] Their distribution has shifted from concentrated top of page placements to dynamic positioning that suggests that their algorithm is making real time decisions about where sponsored products. [00:09:45] Deliver the most value.
[00:09:47] So wrapping up here, what strikes me about this solution is that it addresses this friction point within retailers. In a fixed placement system, one team [00:10:00] wins at the other's expense. The retail media team celebrates filling all 10 slots on a page. The merchant team watches their carefully curated category pages get hijacked.
[00:10:13] Buy I relevant [00:10:15] ads. Fluid integration promises to change that equation when sponsored products only appear if they're relevant enough to justify their position based on a combined margin, both teams win. The [00:10:30] conflict disappears when the system optimizes for total value instead of forcing a choice between product sales and ad revenue.
[00:10:38] And just a note for brands, because you talked about this a lot from a retailer's perspective. When you're [00:10:45] evaluating where to spend, you might want to ask your retailers a question. Does your sponsored product algorithm talk to your organic search algorithm, or are they running in parallel? And that might help you understand.
[00:10:58] And of course, be sure to [00:11:00] check out the sponsored product benchmark report from Penta. Penta Leap has been a client of mine in 2025, and I have really gotten a lot out of their benchmark reports that now bring together multiple [00:11:15] years of. Information about how retailers are changing the coverage of their sponsored products from fixed to fluid for long tail queries.
[00:11:26] And there's also some goodies in there for brands when [00:11:30] they look at how brands across different categories are showing up in sponsored product slots on retailer websites. You can find that report@pentaleave.com. Thanks for listening, and I'll catch you tomorrow.
[00:11:41]