Retailers are investing millions to regain cultural relevance — bold campaigns, edgy collabs, viral moments. But behind the front-end flash, the back-end infrastructure remains stuck in time. And it’s quietly killing the customer journey.   

Take Gap, for example. Under new CEO Richard Dickson, the brand is pouring energy into its revitalization. “If you’re relevant enough,” he told The New York Times, “it eventually drives revenue.” That’s the theory. But here’s the problem: What happens when a consumer sees a TikTok they love, clicks through to the retailer’s site… and the search bar can’t understand what they want?   

This is where the reinvention effort breaks down. Culture moves fast. Pre-LLM search engines? Static chatbots? Not so much.   

Sadly, most retail sites still rely on keyword-based search technology built for a different, pre-LLM era — one where consumers typed in “red dress” or “leather boots” and scrolled through ten pages of thumbnails. Today’s shoppers are more likely to ask full, conversational questions — “What’s a comfortable yet stylish outfit for Coachella?”  Traditional search doesn’t just fail to deliver. It actively undermines the experience.   

The result is a massive gap — no pun intended — between cultural momentum and conversion. You can capture attention with a killer campaign. But if your customer experience, and specifically your site search, dead-ends or misfires, the moment (and the customer) is lost. You need an exceptional experience that continues the conversation with relevance. 

Relevance Isn’t Just a Vibe — It’s Now Infrastructure 

In today’s retail landscape, “relevance” can’t just live in brand campaigns and social feeds. It has to show up in the digital experience itself. That means moving beyond surface-level branding into the nuts and bolts of how customers discover and buy products.   

If a brand positions itself as modern, expressive, and customer-obsessed, its online experience should reflect that — especially at the critical point of search and discovery. Instead, many shoppers are greeted with clunky interfaces, irrelevant results, or worse, the dreaded “no products found.”   

This isn’t just an inconvenience — it’s a business problem. Countless studies show that shoppers won’t return to a site after a poor search experience, and eCommerce search failure rates on major retail sites remain a significant issue. In an industry where margins are tight and acquisition costs are high; that’s a leak no brand can afford.   

The Rise of LLM-powered Conversational Commerce 

What’s the fix? Retailers need to reimagine product discovery as a conversation — not a database lookup.  

Think about your best in-store associate. They understand nuance. They ask clarifying questions. They read your vibe. Online search should do the same.   

Thanks to advances in Generative AI and large language models (LLMs), that vision is now possible. These models can understand natural language, recognize product context, and even reflect brand voice. That means they can help shoppers find what they’re looking for — even if they ask in slang, in full sentences, or with imperfect phrasing.   

This is the beginning of conversational commerce — not just chatbots or voice assistants, but a full rethinking how digital product discovery works. When done right, it’s not just more accurate. It’s more personal. More brand aligned. More human. And ultimately, more effective.  

Why Conversations Must Connect to Product Discovery   

Conversational commerce isn’t just about personality or tone — it’s about performance. It only works when the dialogue leads somewhere meaningful. If a shopper asks, “What’s that hoodie from your spring campaign?” and the system can’t find it or recommend a relevant alternative, the experience falls apart. That’s why relevant product search results are critical. They create a better shopping experience for consumers and drive real conversion for retailers.   

The next generation of digital commerce needs to seamlessly connect natural, conversational inputs with precise, context-aware product discovery results. It’s not enough to sound human; the system must understand humans. That means combining the feel of an in-store associate with the intelligence of large language models (LLMs) trained on your product catalog, content, and customer behavior.  
 
This new conversational infrastructure should deliver answers that are accurate, relevant, and brand aligned. It must also be proactive, offering personalized, assumptive recommendations that anticipate what the shopper really wants.   

Reframing Retail Relevance for Today’s Generation 

Retailers aren’t wrong to chase cultural relevance. In fact, it’s more important than ever. But without a digital experience to match, those investments can fall flat.   

If a campaign drives a million clicks, but search only converts a fraction of the audience, the math doesn’t work. The story doesn’t hold. The brand promise breaks.   

Retailers need to treat product discovery not as a technical afterthought, but as a strategic pillar. That means moving beyond pre-LLM, keyword or vector-based search. Embracing conversational interfaces. Infusing brand personality into every layer of the site. And meeting customers where they are — not just culturally, but contextually.   

The brands that do this won’t just be relevant. They’ll be ready. The time is now.