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For years, on site retail media has operated on a simple and effective idea: brands pay to appear in search results the moment a shopper is looking for something. But that era is changing fast. We're now entering a world where AI agents, not search bars, can drive a significant part of the buying journey.
This isn't just a tech upgrade, it's a fundamental shift in how people shop. With AI agents that can anticipate needs, research products, and even complete purchases on their own, retail media has to move past a keyword-focused model. It needs to become more about understanding people, their intent, and their journey.
Walmart’s recent announcement made this perfectly clear. The company is betting on a future where people use the search bar less and AI agents more to shop. When a retail giant like Walmart makes a bet like that, it's a sign for all of us to pay attention. This change will have huge implications for everyone in the retail media space, from the platforms themselves to the brands that advertise on them.
The Retailer's Side: A New Way to Target
To see where retail media is headed, just look at Google's journey. Google, once the undisputed king of keyword advertising, has steadily shifted its focus to audience and intent. For example, its Performance Max campaigns use AI to deliver ads across Google's entire network, on Search, YouTube, and beyond, without needing a single keyword from advertisers. Instead, the campaigns are optimized for outcomes, like a purchase or a lead, based on a mix of user behavior and signals. This evolution from "what they search" to "who they are and what they're doing" can be the roadmap for retail media networks.
Amazon is already laying the groundwork for this future with its Amazon Marketing Cloud (. While its ad platform still relies heavily on sponsored products and search, AMC is a powerful step toward audience-based advertising. Think of it as a secure data playground where brands can analyze their own customer data, like email lists and website behavior, alongside Amazon’s data. This lets them create sophisticated audience segments, see the full customer journey, and measure their ad effectiveness in a more complete way, looking past just a simple return on ad spend. It's a clear signal that winning on Amazon won't just be about bidding on a keyword, but about truly understanding the customer.
The Brand Side: Proactive Advertising
The new retail landscape demands a big change in strategy from brands. They need to become experts on their customers and move past a keyword mindset. It's time to think in terms of audience segments, intent signals, and the entire customer journey.
This evolution is all about shifting from reactive advertising (bidding on keywords after someone searches) to proactive advertising, which anticipates what a customer needs even before they search.
Building Product Pages for AI
Brands should start by optimizing their product pages not just for shoppers, but for AI agents. This means structuring content around a product’s use cases and context. For instance, a running shoe's product page shouldn't just list technical specs. It should also include content for different scenarios, like "ideal for marathon training" or "best for light jogging and walks." By giving an AI clear, structured information about how and when a product is used, brands help the AI match the product to a customer’s specific needs, even without a direct search query.
Mastering Basket Affinity
Another powerful proactive tactic is using basket affinity data, which shows what products are often purchased together. This goes beyond simple "customers also bought" suggestions. Brands can use this data to target shoppers right on the verge of a purchase.
To do this well, brands need to combine two types of data:
- Quantitative Data from Retailers: This is the "what." Retailer platforms provide the data showing which products are purchased together. For example, a brand selling air fryer liners might see that customers who buy a certain air fryer also tend to buy their liners just after.
- Qualitative Insights from Customers: This is the "why." Through interviews and surveys, brands can understand the motivations behind a purchase. For the air fryer, an interview might reveal that people buy the liners because they're tired of scrubbing the basket. This insight lets the brand create a much more compelling ad with a message like, "Tired of scrubbing, our liners make cleanup a breeze." This speaks directly to a customer's pain point, turning a simple ad into a helpful solution.
Leveraging Cross-Channel Data
A brand's biggest advantage comes from connecting its different data sets. This is the heart of a cross-channel data strategy. For many brands, a lot of their first-party data comes from their direct-to-consumer channels, like their own website, email lists, and loyalty programs. This knowledge is a powerful tool that can inform and improve retail media campaigns, and it works both ways. For instance, a brand could use insights from its own site, like what a customer has in their cart, to create a more effective ad on a retailer’s site. Conversely, data from a retail platform, such as which products are trending in a specific region, can inform the brand’s DTC marketing strategy for that area. This constant flow of information ensures a brand can provide a consistent, personalized experience based on the most complete picture of its customer.
Conclusion
As consumer behavior changes, both retail media networks and the brands that advertise on them must evolve to stay relevant. The future of retail media isn't a game of keyword matching, but a sophisticated ecosystem where success is determined by a brand's ability to anticipate customer needs and deliver proactive, personalized experiences. Brands that invest in understanding their customers and leveraging data across all channels will be best positioned to thrive in this new age of AI-driven commerce.