How AI Powers the Next Generation of Retail Media Success
By Jeff Baskin, Chief Revenue Officer, Eagle Eye
U.S. advertisers will spend more than $62 billion on retail media in 2025, an increase of more than $10 billion year-over-year, with three-quarters of advertisers planning to increase their retail media ad spending. But raw spending growth tells only part of the story. The real transformation centers on artificial intelligence’s ability to solve retail media’s most persistent challenges while creating new opportunities for precision targeting, true personalization, and measurable campaign profitability.
As brands navigate an increasingly complex ecosystem where 85% of CPG brands now spend on four or more retail media networks, the companies winning this space leverage AI to move beyond basic audience segments toward individualized customer experiences. Smart retailers are creating dynamic advertising ecosystems that adapt in real-time, automatically adjusting promotional offers based on inventory levels, competitor pricing, and local conditions while ensuring each customer sees offers tailored to their individual preferences.
AI’s Attribution Advantage
Traditional attribution models break down when customers engage with loyalty programs, advertising platforms, and digital and in-person storefronts before finally transacting at the point of sale. This fragmentation makes it nearly impossible for CPG brands to link ad spend to sales and for retailers to scale their networks effectively. This disjointed customer journey creates blind spots that make it difficult to optimize campaigns or prove return on investment.
AI is solving this challenge by analyzing patterns across vast datasets to identify conversion signals that human analysts might miss. Advanced analytics platforms use probabilistic models that can confidently attribute sales back to promotional initiatives. When someone views a product advertisement on their mobile device and purchases that item in-store shortly afterward, AI algorithms connect these seemingly disconnected events by analyzing location data, timing patterns, and behavioral signals.
Machine learning models build comprehensive customer profiles that track engagement across email marketing, social media interactions, search behavior, and in-store visits. By mapping these complex touchpoints, AI provides brands with clear visibility into which advertising investments drive actual sales rather than just clicks or impressions. The result is attribution accuracy that enables smarter budget allocation and campaign optimization in real-time.
Pushing Personalization Beyond Segments
Traditional retail media personalization has operated within the constraints of predefined audience segments, creating experiences that feel generic despite being “targeted.” Seventy-one percent of consumers expect companies to deliver personalized interactions, and 76% get frustrated when it doesn’t happen, according to a McKinsey report. The segment-based approach means that thousands of customers receive identical offers simply because they share certain demographic characteristics.
AI is helping overcome these limitations by enabling true one-to-one personalization at scale. Machine learning algorithms analyze individual purchase histories, browsing patterns, seasonal preferences, and real-time behavioral signals to create bespoke experiences for each shopper. Early trials have shown a 10% to 25% increase in return on ad spend for targeted campaigns using AI-powered personalization.
This approach goes far beyond product recommendations. AI systems can dynamically adjust messaging tone, visual elements, promotional formats, and even pricing strategies based on individual customer profiles. A price-sensitive customer might receive discount-focused communications, while a premium shopper sees messages emphasizing quality and exclusivity.
Marketers now allocate roughly 40% of their budgets to personalization, nearly double the 22% allocated in 2023. Companies implementing AI-driven personalization report that 80% of businesses see increased consumer spending, averaging 38% more when experiences are personalized.
Getting the Most Out of Loyalty Data
Loyalty programs generate treasure troves of first-party data, but most retailers have barely scratched the surface of this asset’s potential. Traditional loyalty approaches relied on points accumulation and generic rewards that failed to create emotional connections or drive meaningful behavior change.
AI transforms loyalty data into a dynamic personalization engine that creates immediate value for both customers and retailers. Real-time AI processing enables loyalty programs to respond instantly to customer behavior. When a frequent customer deviates from their normal shopping pattern or shows interest in a new category, the system can immediately present relevant offers or product suggestions. This responsiveness creates moments of genuine surprise and delight while also reinforcing the value exchange the consumer has with the retailer, both of which strengthen customer relationships beyond simple transactional exchanges.
The sophistication extends to understanding customer lifetime value and optimizing reward structures accordingly. AI identifies high-value customers who merit premium experiences while recognizing price-sensitive segments that respond better to discount-based incentives.
Generating Insights from Cross-Channel Data
Data fragmentation remains one of retail media’s biggest obstacles to optimization. Customer information sits scattered across loyalty platforms, point-of-sale systems, e-commerce databases, and advertising networks, creating incomplete pictures that limit campaign effectiveness.
AI-powered data integration platforms are breaking down these silos by automatically matching customer identities across systems and creating comprehensive behavioral profiles. These platforms use matching algorithms to connect online and offline interactions, even when customers use different email addresses or devices across touchpoints.
The unified data enables closed-loop attribution that tracks customers from initial advertisement exposure through final purchase and beyond. Retailers can measure not just immediate conversions but also long-term customer value, repeat purchase rates, and cross-selling success. This holistic view reveals which advertising strategies build lasting customer relationships rather than just driving one-time transactions.
Machine learning can continuously analyze cross-channel data to identify optimization opportunities that human analysts would miss. These systems might discover that customers who engage with email campaigns are more likely to respond to in-store promotions, or that social media advertising works best when combined with search marketing for certain customer segments.
An AI-Powered Retail Media Strategy That Works
Success in AI-driven retail media requires more than just implementing advanced technology. The most effective strategies align artificial intelligence capabilities with clear customer experience objectives and business goals. Three in five consumers would like to use AI applications as they shop, but only when these tools genuinely improve their experience.
The foundation starts with data quality and integration. AI systems perform best when fed comprehensive, clean datasets that provide essential pieces of the puzzle to form a complete picture of customer behavior. Retailers must invest in data infrastructure that connects all customer touchpoints and maintains consistent identity resolution across channels.
Campaign strategy should focus on customer value creation rather than just conversion optimization. The most successful AI implementations balance immediate sales goals with long-term relationship building. This means using artificial intelligence to identify moments when customers are genuinely receptive to offers rather than constantly pushing promotional messages.
The winning approach treats AI as an enabler of better customer interactions rather than just an efficiency tool, creating shopping experiences that customers genuinely appreciate and actively seek out. When retailers apply these principles and best practices to their retail media strategies, they will make their retail media network more appealing to CPG brands and create deeper, more fruitful relationships with their customers.
About the Author
Jeff Baskin is a seasoned senior executive leader with over 20 years of experience in the technology sector, specializing in grocery, convenience, restaurant, and big-box retail industries. Jeff’s expertise lies in omni-channel strategies and the full spectrum of digital retail ecosystems, including eCommerce, loyalty programs, mobile platforms, digital marketing, and marketplaces. He has created partnerships with some of the world’s largest retailers to optimize the customer experience, in-store operations, digital programs, and streamline supply chain solutions.
