Making Retail Media More Personalized and Efficient With AI

Retail media is an incredibly powerful tool for influencing purchasing behavior at or near the point of sale (POS), which is why it’s such a valuable asset for retailers and a sought-after marketing channel for brands. However, retail media also faces a consistent challenge: attribution. This difficulty linking sales to ad spend has led CPG brands to invest billions in campaigns with limited visibility into their actual effectiveness. It has also made it harder for retailers to prove the value of their retail media networks (RMNs). Today, artificial intelligence, connected data solutions and advanced loyalty programs are changing this equation by enabling precise personalization, accurate attribution, and measurable results that benefit retailers, brands and customers alike.  

From Guesswork to Precision Personalization 

Historically, retail media offered only incremental improvement on traditional advertising models, wherein brands allocate budgets to broad campaigns hoping to reach the right audiences. This approach frequently results in waste, with advertisements displayed to uninterested consumers who aren’t likely to buy a product. This “broadcast” model makes it nearly impossible to optimize campaigns. 

AI is eliminating this inefficiency by analyzing vast amounts of consumer data to predict purchasing behavior and deliver highly targeted advertisements and promotions. Machine learning algorithms process customer transaction histories, browsing patterns, demographics, and seasonal trends to identify the most promising prospects for specific products. This level of personalization means advertising dollars are spent on consumers genuinely likely to convert, dramatically reducing waste and improving return on investment. 

The technology goes beyond simple demographic targeting or segmentation by understanding individual consumer journeys. AI can identify when a customer who regularly purchases organic produce might be interested in a new plant-based protein brand, or when shopping patterns suggest they're planning a large gathering. Personalization, of course, ensures advertisements feel relevant and helpful rather than intrusive. 

This level of personalization, however, creates a scalability challenge. How can retailers generate millions of personalized offers or “challenges” with different stretch goals or point awards? The only viable solution is through the use of AI. The technology enables retailers to scale millions of personalized offers in real time by applying certain parameters around the promotion. It also gives CPG brands the flexibility to run tailored promotions, whether for an entire brand or specific SKUs, while ensuring that each offer still feels relevant to the consumer. 

Breaking Down Data Silos for Better Attribution 

If your definition of retail media is limited to digital ads or onsite placements, you might question how retailers can achieve this level of personalization within their RMNs. However, retail media is more than a video display on an endcap or a pop-up ad on an e-commerce site; it’s an omnichannel strategy focused on delivering incremental value to retailers, CPG brands, and customers. This comprehensive approach recognizes that modern consumers interact with brands across multiple touchpoints, from mobile apps and websites to physical stores and social media platforms. 

Attribution remains challenging in retail media because data is often siloed across disconnected channels, making it difficult for CPG brands to link ad spend to sales and for retailers to scale their networks effectively. Many retailers have separate systems for loyalty programs, POS, and advertising platforms. This fragmentation makes it nearly impossible to get a complete picture of campaign performance. 

AI addresses these attribution challenges by creating sophisticated models that track customer interactions across multiple touchpoints and time periods. Advanced algorithms account for the complex, nonlinear nature of modern shopping behavior, where a customer might get a promotion through their loyalty app, research the product online, visit a physical store, and make the final purchase days later. 

Leveraging Loyalty Data for Revenue Growth 

Loyalty data is often the key to delivering targeted ads across digital and physical channels. Fully utilizing the powerful data asset generated by loyalty programs enables closed-loop attribution that supports revenue growth. Leveraging that data for digital personalization initiatives also creates more digitally-engaged customers, who tend to spend more as they see increased value from their engagement. Loyalty programs provide a goldmine of first-party data that, when properly analyzed (a process AI is particularly adept at accelerating), can reveal deep insights into customer preferences, purchasing patterns, and price sensitivity. 

Smart retailers are leveraging this data to create dynamic advertising experiences that adapt in real time. AI can automatically adjust promotional offers based on inventory levels, competitor pricing, local events or weather conditions while ensuring each customer sees offers tailored to their individual preferences. AI is already helping loyalty-aligned initiatives like Tesco’s Clubcard Challenges deliver hyperpersonalized incentives to participating customers based on preferences, purchase history and other factors; replicating this experience in digital advertising is the next evolutionary step.  

The financial impact of these AI-driven improvements is substantial. Retailers report increases in advertising revenue as CPG brands see better results and invest more heavily in proven channels. Meanwhile, more digitally-engaged customers increase revenue potential, and improved targeting and personalization drive higher conversion rates and larger basket sizes. 

The future of retail media lies in this intelligent, data-driven approach that treats every advertising dollar as an investment in building stronger customer relationships. As AI continues to advance, successful retailers will be those that turn data into actionable insights that drive profitable, personalized experiences 

Jeff Baskin is the chief revenue officer of Eagle Eye, a leading SaaS and AI technology company that delivers loyalty, personalized promotions, and omnichannel marketing solutions for retail, travel, and hospitality brands.