Moving Beyond Segmentation: How Retailers Execute True Personalization

Personalization may be the promise, but the retail experience that customers get from their favorite brands often tells a different story. Despite claims of sophisticated, advanced and "personalized" engagement, many retailers still group customers by generic segments that look similar on paper, such as demographics or purchase history. This approach overlooks the fundamental requirement of personalization: precise, real-time understanding of an individual shopper's unique needs.

By putting technology to work in smarter, more intentional ways, forward-looking retail leaders are building a new approach that makes true personalization not only realistic but attainable, and it’s changing how they communicate, serve, and build relationships with their customers.

Meeting Customers Where They Are, When it Matters

True personalization demands real-time adaptation to each customer's preferences and history, as well as to the totality of their specific situation. When a shopper browses winter coats on a Tuesday evening, their need differs from someone checking the same products during a Saturday morning snowstorm warning. Similarly, age brackets, income levels and purchase histories cannot predict whether our hypothetical coat-shopper needs a gift, a replacement, or a last-minute stopgap option.

More retailers are beginning to recognize this limitation and are building systems that respond to actual behavior rather than assumed preferences. Artificial intelligence now enables retailers to deliver tailored offers at the precise moment of decision, accounting for timing, local conditions, promotional sensitivity and current behavior in addition to preferences and purchase patterns. These systems generate individualized offers instantaneously rather than relying on predetermined rules.

They also facilitate deeper, more connected customer-brand relationships. As Petco's (now former) Senior Group Product Manager Tara Dalrymple explains in our recent Navigating the Future of Retail e-book, AI can also support complete customer journeys. "Our goal is to make customers feel like we know them and help them do what's best for their pet at every step," says Dalrymple. For example, recognizing when a puppy becomes an adult dog and adjusting recommendations accordingly or noticing when preventive care reminders become relevant.

Breaking Down Data Silos for Unified Engagement

The ability to personalize effectively depends on unified data systems that break down silos between loyalty, marketing, and commerce. Without clean, connected customer data, even the best AI tools cannot meaningfully scale personalization or improve outcomes. Most retailers maintain separate systems for transactions (point of sale), loyalty interactions (rewards platforms) and marketing engagement (CRM systems), preventing the holistic view necessary for meaningful personalization.

Giant Eagle, a regional supermarket chain that has served U.S. customers for over 90 years, takes a different approach, as described by Executive Vice President and Chief Merchandising and Marketing Officer Justin Weinstein: "With a unified digital platform, we craft simple journeys for customers, though they're complicated behind the scenes." The platform itself is comprised of backend systems that connect purchase histories, browsing patterns, and engagement metrics across channels to create a comprehensive customer understanding. In this way, an integrated data infrastructure utilizes every customer interaction to inform future engagements.

Paul Tepfenhart, global director of retail and consumer strategy at Google Cloud, highlights the shift from descriptive to predictive analytics, including demand forecasting, churn prediction, and out-of-stock prevention as a beneficial outcome to unifying data systems. These applications directly impact profitability by reducing waste, retaining customers, and ensuring product availability. Predictive models anticipate demand spikes days or weeks in advance, allowing proactive inventory management while simultaneously creating personalized promotions that match individual purchase cycles.

The Human Work Behind Personalization

Purpose-built AI, effective data management systems, and a strong loyalty program are all important elements of achieving advanced personalization. However, the biggest barrier to success isn't technological capability, it's organizational readiness. Personalization requires marketing, merchandising, product, and data teams working together toward a shared vision. Without cross-functional alignment, initiatives stall before reaching execution, leading to fragmented customer experiences.

Retailers need to be committed to maintaining data quality, establishing clear governance, and consistent coordination across departments to get the most out of their AI personalization strategies.

Marketing teams must collaborate with merchandising to define offer parameters that AI can optimize around. Technology teams need input from customer service to understand pain points. Data scientists require business context to build relevant models. When these groups operate independently, personalization efforts produce disconnected experiences that confuse rather than delight customers.

Measuring Impact Through Connected Systems

Once organizational alignment is achieved, data silos are broken down, and contextual personalization initiatives are implemented, their impact must be assessed. Retailers that excel in personalization measure effectiveness through comprehensive attribution models that track complete customer journeys across channels. Giant Eagle, for example, uses AI to analyze engagement points and redeploy resources where customers find genuine value. This measurement extends beyond simple conversion rates to include customer lifetime value, basket composition changes, and category discovery patterns.

Success requires continuous refinement based on customer feedback. Each interaction provides learning opportunities that improve future recommendations, and declined offers inform assessment metrics as much as accepted ones. Over time, these platforms develop increasingly accurate models of individual preferences and behaviors, creating genuinely helpful experiences rather than generic promotions.

Navigating the future of retail through more advanced, customer-centric personalization requires practical implementation rather than perfect planning. Start with clean data foundations, build connected systems, implement AI where it solves specific problems, and maintain relentless focus on customer value. With this approach, new technology serves a human purpose: creating better shopping experiences that save time, deliver value, and build genuine connections between retailers and customers. And that’s a retail future we can all aspire to.

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.