How Do You Personalize the Buying Experience for Someone You Don’t Know?

Retailers spend massive sums optimizing personalization for known users, those who have logged in, shared their email, or enrolled in loyalty programs. But what about the 85% to 98% of shoppers who remain anonymous? These users visit your site, browse products, filter by size or price, maybe even linger on a product page, and then vanish without a trace. No cart, no form fill, no login. And yet, their intent is real. The challenge is, how do you personalize their experience without violating privacy or requiring identity? 

As data regulations tighten and opt-out rates climb, brands must learn to engage visitors who offer no personally identifiable information (PII). And yet, modern AI models have evolved to do just that, using behavioral signals, live session patterns, and predictive segmentation to build contextual relevance in real time. The key is to shift from identity-based marketing to behavior-driven engagement. 

Let’s look at a typical experience. Mia, a DIY homeowner, visits the site of a large home improvement store. Let’s call it HandyBuild. She’s searching for backyard lighting ideas. She browses six solar light products, applies a filter for "warm white" and price under $40, but doesn’t add anything to her cart. After 90 seconds of scrolling and hesitating, she leaves. In most cases, the visit ends there. HandyBuild has no way to follow up, no knowledge of who Mia is, and no insight into how close she came to purchasing. 

Now contrast that with an optimized journey powered by real-time personalization for unknown users. As Mia browses, the system silently observes key behavior signals: product dwell time, scroll velocity, bounce probability, and feature filters applied. After 45 seconds of inactivity, a dynamic panel appears: “Still looking? Here are the top-rated warm white solar lights under $40.” These suggestions matched her behavioral signals and session flow. Mia sees two relevant options with strong reviews and limited-time pricing. She clicks through, adds one to her cart, and completes the purchase. No email was needed. No login was required. The system adapted in that moment. 

This is the kind of experience that separates high-performing retailers from the rest. TCS Customer Intelligence & Insights™ for retail supports this approach by unifying real-time signals through an advanced customer data analysis and applying AI to trigger the right nudge at the right time, even for users like Mia. Through behavior-based personas, inferred affinities, and contextual prompts, TCS Customer Intelligence & Insights™ for retail helps retailers engage unknown users in ways that feel personal, timely, and valuable. For known users, the system can go deeper, but for the majority who remain anonymous, this capability becomes the most critical growth lever in the funnel. 

Retailers need to address a fundamental question: Are we only personalizing for the few customers we know, or are we optimizing for the majority we don’t know? More than ever, shoppers expect tailored experiences but at the same time they resist oversharing data. The power to act on anonymous intent is a competitive necessity. 

If this resonates with your current digital strategy, or if you’re seeing high traffic with low conversion from anonymous users, you might want to check out TCS Customer Intelligence & Insights™ for retail. There are proven ways to make unknown customers feel seen, guided, and more likely to return. And that’s where real digital loyalty begins.