Differentiating Retail Media Networks with AdTech and Predictive Modeling

Matthew VanBuren

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The Retail Media Boom and the Differentiation Imperative

Retail media networks (RMNs) have exploded onto the advertising scene, emerging as the “third big wave” of digital advertising after search and social​. Major retailers like Amazon, Walmart Connect, Target Roundel, and Kroger Precision Marketing have built sizable ad businesses on the back of their shopping platforms. In 2024, RMNs were expected to account for roughly one-fifth of worldwide digital ad spend (~$140 billion, up from $115 billion in 2023)​, and forecasts peg 2025 spend at around $166 billion​. More than 200 companies, from grocery chains to convenience stores and even banks, have launched their own media networks to get a piece of this high-margin revenue​. The appeal is obvious: Boston Consulting Group estimates retail media can generate 70–90% profit margins​, and retailers are keen to monetize their customer traffic.

However, as the gold rush intensifies, a critical reality has set in: simply having a retail media network is no longer enough to guarantee success​. Not all RMNs will thrive, and many are already struggling to maintain high growth rates despite the overall boom​. The space is dominated by early movers (Amazon now commands about 75% of U.S. retail media spending​) and brands are becoming choosier about where to invest their ad dollars. As one analyst put it, many retailers have launched networks that “simply list advertising space” without unique offerings, and they will need to evolve their models to truly compete​. In practice, that means differentiating through superior technology, data, and insights. Retailers must leverage their rich first-party data, deploy AI-driven personalization, and build predictive models of shopper behavior to offer value that standard media companies can’t match. Those that do will position themselves as indispensable partners for brands; those that don’t risk fading out in a crowded field​.

The First-Party Data Advantage

At the core of every retail media network’s value proposition is its first-party shopper data. Unlike traditional media companies, retailers have direct visibility into customers’ purchase histories, browsing behaviors, and loyalty program activity across online and offline channels. This treasure trove of data is “clean, reliable, and enables the kind of personalized advertising that customers will still want, even after third-party cookies go away”​. In other words, retail media runs on authenticated, privacy-compliant data that most standard publishers simply don’t have.

This first-party data advantage lets retail media networks target ads with a precision and relevancy difficult to achieve elsewhere. For example, a brand can use Kroger’s shopper data to target households that buy organic snacks, or leverage Target’s past purchase data to reach customers likely to be interested in a new baby product. Retailers effectively know what shoppers bought, when, and how often, allowing them to build rich shopper profiles and segments. Traditional media platforms, by contrast, often rely on proxies like cookies or general content affinity; they can’t match the granularity of “real purchase” insights. Moreover, as the industry shifts toward a post-cookie world, this first-party data is emerging as a critical asset. Advertisers are pouring budgets into RMNs specifically to gain access to these insights at the digital point of sale​. In fact, many mid-tier and smaller retailers see a chance to carve out a niche precisely by “capturing traffic during moments of purchase intent”, which has become the holy grail for advertisers as third-party targeting falters​. By integrating shopper data across e-commerce sites, mobile apps, and loyalty programs, retail media networks can offer highly bespoke audience segments (e.g. lapsed buyers of a certain brand, or enthusiasts of a category) that standard media companies simply cannot replicate with the same accuracy or scale.

Another key differentiator tied to first-party data is closed-loop measurement. Because the retailer owns the point of sale, they can tie ad exposures directly to actual purchases, closing the loop from impression to conversion. A retail media network can show a brand that a sponsored product ad on its site led to, say, 500 online orders and 200 in-store sales within a week. This ability to directly attribute sales to ads is a huge contrast to traditional media, where advertisers often rely on modeled assumptions or surveys to gauge impact. For instance, Target’s Roundel network can use its data collaboration platform to share SKU-level sales data back to advertisers and measure the lift from campaigns. Closed-loop reporting not only proves ROI but also provides feedback to refine targeting quickly​. In sum, first-party data gives retail media networks a twin advantage: better targeting upfront and better measurement on the backend. This is a foundation on which they’re building even more advanced capabilities using AdTech and AI.

AI-Driven Personalization at Scale

Access to rich data is only the beginning – the real differentiator comes from how retail media networks use that data with advanced AdTech and AI. The leading RMNs are investing heavily in artificial intelligence (AI) and machine learning to drive personalization and optimize ad performance in real time. This goes far beyond what a traditional media outlet could do with basic segmentation. AI allows retailers to turn millions of data points into tailored experiences for each shopper, at scale​.

Consider the on-site experience on a retail media network versus a standard publisher site. On a retailer’s e-commerce site, the ads can effectively become personalized product recommendations embedded in the shopping journey. With AI algorithms analyzing behavior, each ad impression can be dynamically generated to feature products or offers that align with an individual shopper’s interests. As one industry expert explains,

"AI uses first-party data – like purchase history, browsing behavior, and demographics – to predict consumer intent with remarkable precision. Rather than relying on broad segments, AI can create dynamic profiles of individual shoppers and target them with tailored messages based on what they’re most likely to buy, and when.”​

In practice, that means if you’ve been browsing running shoes on a retailer’s site but haven’t purchased, the network’s AI can detect this intent and serve you a highly relevant ad (perhaps a limited-time discount on the exact shoes you viewed or a complementary product like running socks) at the perfect moment to convert the sale​.

This level of one-to-one personalization is something traditional media networks struggle to achieve. A TV network or a news website might optimize ads by broad audience demographics or context, but they generally can’t swap out ad creative on the fly for each user based on real-time behavior. Retail media networks, on the other hand, are increasingly leveraging dynamic creative optimization and AI-driven recommendation engines to make every ad feel like a natural extension of the shopping experience. For example, AI can adjust an ad’s visuals, copy, or product mix based on a shopper’s recent actions. If a Target.com visitor has been searching for winter coats, the next ad they see might showcase a personalized selection of coats in their size or style preference, possibly even drawn from brands they’ve bought before, making the ad feel more like a recommendation than a generic pitch​. This kind of AI-driven creative personalization at scale boosts engagement and conversion rates, and it’s a capability born from the fusion of retailer data and modern AdTech.

Crucially, AI isn’t just improving on-site ads; it’s also optimizing off-site and cross-channel retail media campaigns. Many retail media networks now run campaigns on external inventory (display ads on other websites, social media, CTV/streaming platforms, etc.) using their shopper data to define the audience. AI plays a key role in these off-site campaigns by finding the right customer at the right time across the web. Walmart Connect, for instance, can take its in-store and online purchase data and use it to target Walmart shoppers on streaming TV through its partnership with Roku, or on social media via TikTok​.

The heavy lifting of matching Walmart’s audience data to those external ad opportunities happens through advanced AdTech integrations (i.e. identity matching, programmatic bidding) often enhanced by machine learning algorithms that optimize which ad to show to which user. The result is that retail media networks can extend personalized, intent-based advertising well beyond their own sites. This full-funnel capability, reaching a shopper on any channel with relevant messaging, is being realized with the help of AI models that learn where each advertising dollar yields the best outcome. As the Ventura Growth analysts note, this shift to data-driven advertising means brands are no longer limited to static placements; they can “leverage the power of AI to create highly personalized, real-time advertising experiences that deliver measurable results” across channels​.

Predicting Shopper Intent and Behavior

A distinguishing strength of retail media networks is their ability to anticipate what shoppers will likely do next, effectively predicting intent to stay one step ahead. By analyzing vast first-party datasets, retailers can employ predictive modeling to forecast customer needs and behaviors with uncanny accuracy. This is an area where retail media truly sets itself apart from standard media companies. Traditional media might infer broad intent (e.g. someone reading a travel article might be interested in luggage ads), but retail media networks can often explicitly tell which customers are “in-market” for which products based on real, recent signals.

Using machine learning, a retailer can build propensity models that answer questions like: Who is likely to purchase product X in the next 30 days? Which dormant customers are likely to re-engage with a particular brand if exposed to a specific offer? What complementary product might a shopper want based on what’s already in their cart? Armed with such models, retail media networks can proactively target shoppers with the right message before the shopper even explicitly searches or asks. For example, Kroger’s data science unit 84.51° applies predictive machine learning to segment shoppers and anticipate their needs, enabling highly tailored marketing outreach through Kroger Precision Marketing​.

These models might reveal that a customer who buys baby diapers is statistically likely to buy baby food next, prompting the RMN to serve an offer for baby food to that shopper. In essence, predictive analytics allow the retailer to leverage patterns in past behavior to predict future intent, then deliver ads that intercept that intent.

Leading retail media networks are already weaving predictive insights into their ad platforms. Target’s Roundel has explicitly integrated predictive AI into its offsite advertising. By sharing SKU-level sales data with its ad tech partners, Roundel can “unlock enhanced optimization capabilities with predictive AI”, meaning their system can predict which ad placements are likely to drive a purchase and adjust bids and targeting accordingly​. This kind of predictive optimization improved campaign results significantly – in one test, applying AI-driven optimization in Target’s Bullseye Marketplace (Roundel’s offsite inventory network) led to an average 10% increase in return on ad spend (ROAS) and 9% more online orders for participating brands​. Those are tangible gains delivered by predicting and acting on shopper intent signals more intelligently.

Moreover, predictive models help retail media networks enhance the customer experience, not just ad performance. If a retailer’s AI predicts a certain customer is likely running low on a frequent purchase (say, dog food), the network can serve a timely reminder or coupon before the customer even realizes they need to restock. This predictive relevancy is something consumers actually appreciate, it feels like the retailer is being helpful rather than intrusive. Industry research shows that over 80% of brands see increased consumer spending thanks to personalized experiences​, and nearly 90% of consumers prefer ads tailored to their interests or current shopping intent​.

By modeling shopper behavior, retail media networks aim to hit that sweet spot where ads genuinely add value. Traditional media, lacking direct insight into purchase intent, are hard-pressed to deliver this level of timely relevance. A network TV ad or a magazine spread might build awareness, but it won’t know you’re in your last week of dog food supply and nudge you to buy... retail media can.

In summary, first-party data, AI-driven personalization, and predictive modeling work in concert to make retail media networks extraordinarily powerful. They ensure that advertising is not only highly targeted and personalized, but also well-timed and contextually integrated into the shopping journey. This trifecta creates a unique value proposition for brands: campaigns that reach consumers at the moment of intent, with the right message, and a clear line of sight to resulting sales.

Beyond What Traditional Media Can Offer

It’s worth underscoring how these capabilities contrast with those of traditional media companies. A standard media company (e.g. a television network, a news publisher, or even a big social platform) might have scale and quality content, but it lacks the direct commerce context and granular shopper data that retail media networks possess. Traditional ad channels often rely on demographic targeting or content-based targeting (show the car ad on a car review site, for instance). They don’t operate at the actual point of purchase, so they capture consumers higher up the funnel, with less certainty about immediate intent.

By contrast, retail media lives at the nexus of media and commerce. When a consumer is on Amazon searching for “4K TV”, or browsing recipes on Kroger’s app, their intent is explicit and the network can serve a relevant ad (for a specific TV model or a discount on ingredients) right then and there. Traditional media might reach that same consumer a day earlier while reading news, but it’s a guessing game whether an ad will resonate at that moment. As one report noted, retail media networks can “reach high-intent shoppers” in ways walled-garden publishers cannot, all while respecting privacy regulations​. In an era where third-party tracking is restricted, media owners without their own customer login data are at a disadvantage. Retailers have the logged-in, purchase-hungry audiences that advertisers covet.

Another major difference is in collaboration and transparency. Retail media networks are increasingly partnering with brands to share insights and data that go beyond what a standard media vendor would provide. For example, Roundel works closely with advertisers via data clean rooms and APIs to share conversion data, optimize campaigns, and even co-create audiences​.

This level of data collaboration means a brand and a retail network can jointly analyze what’s driving sales and refine their approach almost in real time. Traditional media companies historically have been more siloed. They deliver impressions, but rarely hand over rich data signals to advertisers due to privacy or competitive reasons. Retailers, on the other hand, are using collaboration (in privacy-safe ways) as a selling point, effectively saying: “We’ll use our data to help your brand grow, and show you the proof.”  The depth of audience insight and post-campaign sales analysis that a retailer can offer (often through self-serve dashboards or regular reporting) is a strong differentiator. As Boston Consulting Group found, brand advertisers working with RMNs want exactly these kinds of data-driven insights and full-funnel reporting that they “can’t get from [traditional] merchant partners”​.

Finally, the technology platforms underpinning retail media networks are increasingly sophisticated, rivaling those of the biggest ad tech companies. Many RMNs have built or licensed self-serve programmatic platforms that let brands bid on audiences and placements in real time, similar to how one would buy ads on Google or Facebook. For instance, Walmart Connect partnered with The Trade Desk to launch a DSP that uses Walmart’s first-party data for off-site targeting​, and Target’s Bullseye Marketplace similarly lets brands programmatically buy inventory across hundreds of publishers using Target data. This means that retail media is not stuck in a silo of on-site banner ads; it’s plugged into the wider digital ecosystem with real-time bidding, audience extension, and multi-channel reach. Traditional media companies that don’t control logged-in audiences typically can’t offer this kind of one-to-one retargeting or deterministic audience buying at scale.

In short, retail media networks are blending the capabilities of an ad tech platform with the richness of retail data, whereas a standard media company might have content and eyeballs but fewer tools to exploit data for predictive targeting. The end result is that RMNs can deliver higher ROI and better shopper experiences, raising the bar for what advertisers expect from “media” partnerships​.

Case Studies: How Leading Retailers Leverage AdTech and AI

To illustrate these differences, let’s look at how some of the major retail media networks are applying first-party data, AI, and predictive modeling in practice:

Amazon Advertising (Amazon Ads)

Amazon was the pioneer of retail media and remains the juggernaut in the space. It launched sponsored product ads on its site back in 2012 and quickly proved how lucrative retail media could be​. By using its massive trove of first-party e-commerce data, Amazon built sophisticated recommendation and ad placement algorithms that surface products a shopper is likely to buy. Every Amazon search or product page is an opportunity for a personalized ad – and Amazon’s AdTech ensures that the ads (sponsored products, display banners, etc.) are relevant to the shopper’s intent. This early embrace of data-driven, in-search advertising gave Amazon a huge head start, and today it controls roughly 75% of the U.S. retail media market​.

Advertisers on Amazon can target customers by purchase history (e.g. advertise a new cereal to someone who buys organic groceries) and even reach Amazon audiences off-site through Amazon DSP. Amazon’s AI and predictive analytics constantly refine who sees which ads; for example, Amazon’s system might predict that a customer who bought a camera will soon need a tripod, and thus show tripod ads to that customer across Amazon’s properties. The scale of Amazon’s data (millions of daily transactions) allows its models to learn fast and drive high conversion rates.

Case in point: many brands have shifted significant ad budgets to Amazon because its closed-loop data proves the ads drive sales, advertising now contributes an estimated $38 billion+ annually to Amazon’s revenues. By acting as both a retailer and a cutting-edge ad platform, Amazon exemplifies how an RMN can eclipse traditional media in targeting and performance.

Walmart Connect

Walmart Connect (the retail media arm of Walmart) has rapidly emerged as a strong #2 player by capitalizing on Walmart’s sprawling in-store and online presence. Walmart uses its first-party data from 150 million weekly shoppers to help brands target and measure campaigns. A key differentiator for Walmart Connect is its focus on omnichannel integration, using data from physical stores, online shopping, and even partners to create a unified targeting and measurement framework.

For instance, Walmart knows what’s selling in-store in real time via POS data, and it can adjust digital promotions accordingly (a capability standard media firms lack). Walmart has also been innovating through partnerships: it teamed up with The Trade Desk to allow programmatic buying of Walmart audience segments on the open web, and with media companies like Roku and TikTok to extend Walmart-powered ads into streaming TV and social feeds. This means an advertiser can use Walmart’s shopper insights to buy a video ad on Roku and later see if viewers of that ad bought the product at Walmart, a closed-loop scenario combining online advertising with offline sales data. Walmart Connect’s investments in AdTech and data science are paying off, as advertising contributed nearly a third of Walmart’s $6.7 billion operating income recently​. The division saw ~28% YoY growth in Q3 2023, outpacing many traditional ad businesses​.

A practical example of Walmart’s predictive prowess is how it localizes ad content: Walmart can analyze local store purchasing trends (say a spike in gardening supplies in one region) and prompt brands to serve region-specific ads for related products​. This level of granular, data-informed marketing is something a generic media company simply wouldn’t be equipped to do. Walmart’s blend of scale, data, and AI-driven execution has firmly positioned Walmart Connect as a differentiated contender in retail media.

Target Roundel

Target’s Roundel is often cited as an innovator in how retail media networks collaborate with advertisers. Roundel leverages Target’s rich guest data (from in-store purchases, Target.com activity, and the popular Target Circle loyalty program) to create precise audience segments and actionable insights. One of Roundel’s standout strategies is its emphasis on data collaboration and predictive analytics. Target was among the first to set up a clean-room environment where advertisers can match their customer data with Target’s to find overlapping audiences or glean new insights (all in a privacy-safe way). This collaborative approach means brands advertising through Roundel get more than just ad placements – they get shareable analytics about what drives performance.

Technically, Roundel has invested in off-site AdTech via its Bullseye Marketplace, an invite-only network of over 150 million publisher placements where Target data can be used to buy ads​. Through Bullseye, Target’s first-party data is deployed to reach guests “where they enjoy content,” with closed-loop measurement back to in-store or online sales​. Target has demonstrated how predictive modeling boosts these off-site campaigns: by sharing conversion signals with The Trade Desk, Roundel unlocked AI-based optimizations that increased ROAS by ~10% in test campaigns​.

In practice, this might involve predictive algorithms deciding which ad inventory is most likely to convert a specific Target guest and adjusting bids in real time. Roundel’s success is reflected in industry accolades. In a 2024 industry study, marketers ranked Target Roundel highly for its data sharing, targeting effectiveness, and ability to drive traffic​. The takeaway is that Target is differentiating not just by having data, but by actively using AI and partnerships to make that data work harder for advertisers than any traditional media partner could.

The Future of Retail Media Belongs to the Innovators

The rapid rise of retail media networks is reshaping the advertising and retail industries simultaneously. But as growth accelerates, it’s clear that survival and long-term success will favor those networks that truly leverage their inherent advantages and invest in continuous innovation. First-party data, AI-driven personalization, and predictive behavior modeling are not just buzzwords, they are the pillars upon which retail media networks can build unique value propositions that outshine what standard media companies offer. The major players like Amazon, Walmart, Target, and Kroger have demonstrated how combining retail savvy with AdTech prowess can unlock exceptional advertising performance. They turn customer data into actionable intelligence, serve consumers in the moments that matter, and prove advertising effectiveness with a rigor that legacy media can’t match.

Meanwhile, the flood of new retail media entrants faces a crucial choice: differentiate or fade away. As one Forbes analysis warned, having a retail media network in name only won’t be enough, retailers must “demonstrate the value of their platform offerings” through superior capabilities​. This likely means more AI investment, better self-serve technology for advertisers, deeper partnerships (e.g. data sharing arrangements, joint business planning with brands), and innovative uses of their physical stores and digital touch-points to create an omnichannel media experience. We may soon see a shakeout where brands consolidate spend to the RMNs delivering the best results and insights. Those networks that can predict consumer needs, personalize at scale, and provide closed-loop proof of performance will stand above the rest.

For tech professionals in advertising and retail media, the implication is clear: building a differentiated retail media network requires both the right data assets and the tech muscle to unlock their potential. It’s about bridging retail and media through technology, turning stores and sites into intelligent ad platforms. Retailers that succeed in this endeavor will not only secure a new revenue stream, but also deepen their customer relationships by making every interaction (even ads) more relevant. In contrast, traditional media companies will need to reinvent how they use data and partnerships to keep up, as the bar for targeted, accountable advertising is raised.

In the end, the rise of retail media underscores a broader trend: the future of advertising belongs to those who can marry context with data. Retailers inherently have the shopping context and now are learning to wield data and AI like tech giants. This is leveling the playing field with (and in some ways surpassing) the Googles and Facebooks of the world by bringing advertising closer to the point of purchase than ever. The opportunity is enormous, but so is the competition. The retail media networks that differentiate through superior AdTech and predictive analytics will be the ones left standing when the dust settles and they’ll define a new era of personalized, high-performance marketing.