The anonymous customer: the great challenge of modern retail

The importance of understanding the anonymous customer

Optimizing the PDP without identification
In retail, the “typical customer” no longer starts on the homepage or arrives logged in. More and more visits land directly on a product detail page (PDP) from AI search, comparison sites, marketplaces, or social media, with an intent that changes quickly and few persistent signals.
That forces a shift in the goal: instead of waiting for identification to personalize, you have to interpret session signals and reduce friction in the moment.

• Most retail traffic already arrives without login, without reliable cookies, and from AI searches, comparison sites, marketplaces, or social media.
• Personalization still boosts results: tailored offers and experiences can increase conversion and cross-selling by 30% to 40%.
• “Anonymous” does not mean “invisible”: real-time behavioral signals reveal intent and friction points.
• The challenge is no longer “recommending better,” but removing blockers that prevent reaching checkout.
• With economic pressure, high acquisition costs, and privacy changes, waiting for the user to identify themselves is no longer a viable strategy.

This analysis is based on the article “Retail’s Biggest Blind Spot: The Anonymous Customer”, published in Retail TouchPoints by Alix de Sagazan (CEO and co-founder of AB Tasty).

The importance of the anonymous customer in retail

Personalization for anonymous traffic
– The source piece states that personalization (for example, tailored offers) can increase conversion and cross-selling by around 30%–40%.
– It also emphasizes that today “most traffic” in retail is anonymous: it arrives without login and with weakly persistent signals (less reliable cookies, switching between devices/tabs).
– Practical implication: if the site is designed primarily for identified users, it is optimized for a minority and leaves value on the table across the bulk of traffic.

Retail is entering a new era of discovery: AI-powered search is changing how consumers find products and, with it, which part of the journey each brand controls. For years, the sector optimized for the “known customer”: loyalty programs, CRM, personalized emails, and in-app experiences designed for those who log in, return frequently, and leave a consistent data trail.

That assumption is breaking down. Today, most traffic is anonymous. Many shoppers land directly on product pages from AI search results, price comparison sites, marketplaces, or social media.

They arrive without identifying themselves, often without cookies that tell a reliable story and without a prior relationship with the retailer. In addition, their context is hard to reconstruct: they jump between tabs, browse on multiple devices, and change intent within the same session.

Treating that visitor as “lower value” is a strategic mistake. In many categories, they represent the biggest revenue opportunity: they are usually a high-intent shopper, short on time, with a comparison mindset. They’re not “browsing”; they’re trying to decide quickly.

Real-time behavioral signals and their relevance

The good news is that anonymity does not equal opacity. Even if historical data is missing—previous purchases, lifetime value, established affinities—the retailer does have something immediate and actionable: real-time behavioral signals.

Every click, filter, or repeated visit to a specific section provides context about intent:

• Someone who checks delivery dates several times may be worried about arriving on time for a birthday or event.
• Someone who sorts by “lowest price” or sets a price filter usually indicates that budget is the deciding factor.
• Someone who switches between the size guide and the returns policy may be looking for reassurance about fit and post-purchase friction.

The problem is that many experiences are still static: they show the same content and force the same purchase flow, regardless of what the user is “saying” with their behavior. That’s where the opportunity to respond when intent is hottest gets lost.

Challenges of personalization for anonymous users

Personalization works: tailoring offers and content can lift conversion and cross-sell by 30% to 40%. But the challenge is obvious: how do you personalize if you don’t know who the person is?

The answer requires changing the approach. Instead of relying on historical segments or persistent profiles, personalization for anonymous users must be based on contextual intent: what the user is doing now, in this session, at this point in the funnel.

Key trade-offs in personalization
– Personalization vs. privacy: the more “aggressive” the inference, the more important it is that the experience feels useful (not invasive) and that the user understands why they’re seeing certain information.
– Speed vs. accuracy: responding in real time often beats “getting it perfectly right” late; a simple help (delivery times, returns, stock) can unlock more than a sophisticated recommender.
– Session signals vs. history: with anonymous users, the session rules; the risk is overinterpreting a single action. Better to combine 2–3 consistent signals before changing messages or offers.

On top of that comes a “cocktail” of external pressure: privacy changes, the decline of third-party cookies, and the rise of AI search. The era of “easy tracking” is over. Waiting for the user to identify themselves—only then offering a relevant experience—no longer fits how people discover and buy in 2026.

Revenue opportunities with anonymous shoppers

The anonymous shopper often arrives “mid-funnel” with a short list in mind. They’ve already compared, already read, already asked an AI or checked reviews on another site. In that scenario, the retailer’s job isn’t to educate from scratch, but to reduce friction quickly to get them to checkout.

From intent to fast conversion
Practical flow to turn anonymous intent into progress (without relying on login):
1) Mid-funnel entry (PDP/PLP) → detect 1–2 dominant signals (price, delivery, trust, fit).
2) Identify friction → what’s holding back “Add to cart” or “Pay” (timeline, total cost, policy, stock, payment method)?
3) Minimal intervention → show the answer where the doubt appears (not on another page): ETA, returns, promo/bundle, reviews, availability.
4) Checkpoint → if after the intervention not

there is progress (cart/checkout), test an alternative (e.g., click-and-collect vs. shipping; warranty vs. reviews; bundle vs. one-off coupon).

When an anonymous user leaves without buying, it’s often not due to lack of interest, but because something stopped them: doubts about delivery, price, trust, returns, or payment complexity. Removing those blockers in the moment can turn “hard-to-retain” traffic into immediate revenue.

Also, in a context of economic pressure, more deliberate consumers, high acquisition costs, and margins under strain, improving conversion of existing traffic—especially the majority, which is anonymous—becomes a critical lever.

How to remove blockers in the purchase process

For years, retail equated personalization with recommendations. For the anonymous user, that obsession may be secondary. The priority is to identify what prevents them from buying and clear the path.

Common blockers and quick solutions
Quick checklist of frequent blockers (and immediate fixes):
– Delivery: ETA not very visible → show estimated date and options (urgent/click-and-collect) on the PDP and in the cart.
– Price: surprise total cost (shipping/taxes) → clarify the total as early as possible and avoid “surprises” at the end.
– Trust: lack of social proof → surface reviews, ratings, and warranties near the CTA.
– Fit/compatibility: technical or sizing doubts → clear guide, comparisons, and “what’s included” without leaving the page.
– Stock: uncertainty about availability → stock by variant and restock times if applicable.
– Checkout: too many steps/fields → reduce friction (autocomplete, guest checkout, popular payment methods).
– Returns: hidden policy → simple summary and link to details next to size/CTA.

Examples of responses based on real-time signals:

• If delivery is the concern: show fast delivery options, “next-day,” or click-and-collect earlier, and make timelines visible at the point where the doubt arises.
• If price is the brake: highlight promotions, bundles, or a one-off incentive that reduces hesitation, without forcing navigation across multiple pages.
• If trust is lacking: elevate reviews, warranties, and easy returns; reduce uncertainty with clear, accessible information.

The key is to move from rigid experiences to experiences that react. It’s not about guessing “who” the customer is, but understanding “what” they need to move forward.

The evolution of retail in the face of AI search

PDPs for mid-funnel intent
What changed: AI search and comparison environments are pushing many users to land directly on PDPswith a mental preselection (they arrive “mid-funnel”).
What this implies: the first seconds on the PDP (clarity of total price, delivery, returns, trust) matter more than classic navigation by categories or the home. The site must be prepared to respond to intent, not to “present the brand” from scratch.

AI search is reshaping the discovery map. Instead of starting on the home, the consumer lands on a product page from a result already “curated” by an assistant, a comparison engine, or a social feed. That reduces the retailer’s control over the start of the journey and increases the importance of what happens in the first seconds on the site.

In this new pattern, designing primarily for logged-in customers means optimizing for a minority. The risk is clear: losing the majority by not recognizing them, not reading their signals, and not responding to their intent.

Customer expectations in a changing environment

Personalization: urgency and current expectations
In the base piece, two data points are cited that explain the urgency:
– 71% of customers still expect personalized experiences throughout the purchasing process.
– 61% say they feel “like a number” when the experience isn’t tailored.
Operational takeaway: even if there are fewer persistent identifiers, the expectation of relevance remains; that’s why real-time signals (delivery, price, trust, fit) become the most direct bridge.

Even if the data ecosystem changes, consumer expectations don’t drop. 71% of customers still expect personalized experiences throughout the purchasing process, and 61% say they feel “like a number” when the experience isn’t tailored.

The tension is obvious: fewer persistent identifiers, more pressure for relevance. The way out requires a shift in mindset and operations: collaboration among merchandising, UX, logistics, and pricing to act on real-time behavioral signals. It’s harder than launching another loyalty tier, but its impact can be greater because it targets the bulk of traffic.

From Suricata Cx, that same logic of contextual intent and automation with human control translates into faster, clearer omnichannel experiences, where each interaction provides actionable context even when the customer remains “anonymous.”

This approach starts by designing operations around real-time signals (intent, friction, and urgency) and orchestrating consistent responses across channels, prioritizing resolution and user progress over relying on persistent identifiers.

The figures and examples cited are based on publicly available information as of the publication date. The magnitude of the impact may vary depending on the category, the market, and thedigital maturity of the retailer. Discovery patterns (AI, comparison sites, social) evolve rapidly, so some details could change and be updated with new information.