In this week's guest member blog post, we hear from Iman Nahvi, Co-Founder, Chief Executive Officer at Advertima as he explores how can AI unify fragmented online and in-store audiences, enabling true omnichannel Retail Media with real-time targeting, activation, and measurement.
In 2026, we will look back and say: Retail Media is stuck. Except we ought to face and solve the next big challenge right now: unifying addressable audience segments across networks as well as across online and in-store channels. Despite the rapid growth of Retail Media, most brands are forced to operate in silos, with in-store and digital campaigns managed separately, limiting the effectiveness of Retail Media investments.
I recently read an article by Greg Deacon on InternetRetailing about the organizational hurdles of breaking down silos in Retail Media. He makes a strong case for how internal brand structures are often the biggest blockers to true cross-channel integration. That’s the organizational side of the problem. Here, I’m focusing on the tech side of the problem - how unifying audience segments across online and in-store is the only way to truly break these silos.
Even when we look at In-store Retail Media alone, the industry is still deploying legacy DOOH technology without questioning whether it can actually deliver the value, precision, and scalability that made online Retail Media so successful. For a deeper dive into why in-store Retail Media cannot simply replicate DOOH, check out my blog post from 2023.
Since then, a lot has happened: the CAPEX for sensor-based hardware kits has significantly decreased (by more than 70%), and major leading Retail Media organizations have started deploying technologies enabling real-time audience segmentation, activation, and measurement in-store. The most recent example is MAF’s Precision Media in their Carrefour UAE stores. 2024 was a year of progress because first-movers initiated this wave of innovation. And as we know, every innovation needs bold trailblazers, and the early majority will follow the first-movers soon after.
Now, building on the progress made on real-time in-store audience segmentation and activation, it’s time to look ahead and focus on the next big challenge: unifying audience segments across online and in-store to create a truly seamless omnichannel media buying experience.
Currently, the industry’s biggest attempt to bridge the gap between in-store and online shopping is linking loyalty card data to purchases at physical checkout and linking back to online shopping history. That’s a good step and, fortunately, from a tech perspective, not a big challenge. However, it misses a crucial component of the bigger picture: real-time addressability and activation of the in-store shopper during the shopping session based on unified cross-channel audience segments.
To really bring digital advertising’s power into the physical store, we need more than just checkout-based attribution. Checkout data only captures completed purchases, failing to account for in-store browsing behavior, intent signals, and real-time engagement - elements that have been critical to Online Retail Media's success.
Right now, In-store Retail Media is defined by DOOH technology, not by what actually works for omnichannel advertising. The industry assumes that because digital screens are needed for In-store Retail Media, the related DOOH technologies are the only option to invest in without any changes or add-ons. That’s like saying banner ads targeted based on zip codes are the future of ecommerce advertising. It ignores the core mechanisms that made Online Retail Media dominant: real-time, retail/shopper-data-driven, audience-based activation.
The problem? DOOH technology was built for a different purpose. It wasn’t designed for real-time audience activation. It wasn’t designed to unify audience segments across online and in-store environments, and it certainly wasn’t designed to integrate with the sophisticated data-driven buying experiences that brands and agencies now expect. The result is that in-store advertising remains disconnected from the precision, measurement, and flexibility that define modern Retail Media.
Let’s examine how the industry is approaching audience segmentation in Retail Media Networks (RMNs). Right now, the primary focus is on isolated first-party data within each retailer’s walled garden. Every RMN operates its own unique audience segmentation, bidding system, and measurement framework. As a result, media buyers face a fragmented landscape where they must manually reconcile audience definitions across hundreds of different RMNs, most built on different tech stacks.
The ad tech industry has recognized this problem and is already working on mapping audience segments across these walled gardens. The challenge for demand aggregators is that isolating and matching user IDs doesn’t work across Retail Media walled gardens due to privacy restrictions, retailers' lack of willingness to open up, and fragmentation between networks. Isolating and matching IDs might still be viable for offsite activation but not for unifying audiences across Retail Media Networks. Even for offsite activation, the majority of targeting still relies on lookalike modeling rather than one-to-one ID matching.
The only remaining path forward for cross-RMN unification is using AI to match audience segments across networks. AI-driven generative modeling enables intelligent segment predictions, while AI-driven probabilistic modeling facilitates audience matching without requiring deterministic identifiers. Online innovators in our space will use AI-driven lookalike modeling to map audience segments across different Retail Media Networks, addressing the fragmentation issue. These efforts mark an important step toward solving the online part of the problem.
A similar challenge applies to in-store: there is no real-time access to a shopper’s profile during the in-store journey. So, if we are mapping audiences across walled gardens using AI, why aren’t we extending this logic to the physical world, where ID matching is neither feasible nor scalable?
The answer is simple: it’s possible. Using AI to predict in-store shopper segments in real-time and match them with already offered and monetized online segments is the only path to truly unified omnichannel Retail Media.
The good news is that, considering in-store as an isolated channel, the technological solution is already developed, deployed in major global store networks, and applied to hundreds of campaigns daily. This is the tech I explained in my introduction. The same approach can be applied to predicting to which online audience segments individual in-store shoppers belong and making them addressable based on those.
And this is how the four-step process works:
This is the key to unifying audience segments across in-store, online, and different Retail Media Networks. It enables a seamless, standardized, and unified audience structure that allows media buyers to execute true omnichannel campaigns without fragmentation.
To solve this challenge, computer vision leverages 3D space perception, walking path predictions, and Predictive AI to forecast audience composition for the next spot. Then, approximately half to one second before the ad plays, the AI informs the digital signage CMS, ad server, and SSPs about the audience composition for the next spot and recommends what available segment to target, ensuring maximum relevant impressions.
This ensures comparability between online and in-store media measurement and delivers deeper audience insights than online. Additionally, because audience segmentation is unified across online and in-store, consistency is maintained even at the segmentation level. You can read about how Advertima solves the measurement gap in our In-store Retail Media Standards post.
Retail Media won’t reach its full potential until it embraces audience unification across online and in-store. Capturing, segmenting, activating, and measuring audiences seamlessly based on unified segments across both environments will create a genuine omnichannel media buying experience and unlock new revenue streams. This shift will ultimately define the next phase of Retail Media’s evolution.
The technology is here. The buying experience is evolving. But adoption is not a given. It requires innovators and first-mover Retail Media executives to break down existing silos and embrace AI-driven audience matching across online, in-store, and RMNs.
The key challenge now is whether retailers and tech providers will take the necessary steps. Media buyers are certainly ready to benefit from such advancements.
Retailers are sitting on one of the most powerful monetization opportunities in media – In-store Retail Media. This practical guide outlines 4 actionable strategies to:
Discover how to turn in-store audiences into measurable media assets – and elevate the true value of your retail media network.
