Understanding Agentic Search: A New Paradigm for Retail Discovery

The Rise of AI-Powered Search in Retail

Over the past year, we’ve seen the rapid adoption of AI-based chatbots to support a wide range of everyday activities. In the retail sector, this shift is starting to reshape how customers search for and discover brands and products, particularly through the emergence of agentic search powered by large language models (LLMs).

It’s estimated that platforms like ChatGPT now process over a billion searches per week, with forecasts suggesting that LLM-driven search could rival traditional search engine usage within just a few years. While current referral traffic from LLMs only accounts for around 0.05% of total web traffic, click-through rates (CTR) are already approaching those of mature platforms like Google.

Early Signals: What the Data Tells Us

This tells us two key things:

• Agentic search is still in its infancy, but it has enormous growth potential.
• Even in these early stages, the ability of LLMs to deliver contextually relevant results is already prompting meaningful user engagement.
  

As these systems evolve and consumers become more comfortable with conversational product discovery, agentic search is poised to become a major channel for retail merchandising and customer acquisition.

Retailers should begin thinking seriously about how their content, products, and brand are represented - and discovered - within this new ecosystem.

How Agentic Search Differs from Traditional SEO

To prepare for this shift, it’s critical to understand how agentic search works differently from traditional search engines.

Traditional Search Engines:

• Rank content based on keywords, backlinks, site speed, and paid promotion.
• Match query terms to indexed pages.
• Prioritise volume, structure, and technical SEO best practices.
  

Agentic Search:

• Prioritises semantic relevance, context, and content quality.
• Aims to understand the underlying intent behind a query, not just match keywords.
• Surfaces content that meaningfully addresses user needs through natural language understanding.

The Retail Analogy: A Digital Sales Assistant

Imagine walking into a physical retail store and speaking to a knowledgeable sales assistant. You explain what you’re looking for - preferences, constraints, budget - and they respond with curated recommendations, backed by product insights and social proof.

That’s the experience agentic search is trying to replicate.

These systems are designed to:

• Understand nuanced user intent through natural language.
• Surface only highly relevant, context-aware content.
• Offer reasoning based on product specs, use cases, or third-party reviews.
• Avoid promotional bias, as there are currently no paid placements influencing results.

What This Means for Your SEO Strategy

The good news: traditional SEO isn’t dead. Foundational best practices - like clean site structure, page speed, and keyword clarity - still matter for visibility.

However, agentic search prioritises depth and intent over volume. That means:

• Tailored, high-quality content becomes essential - think how-to guides, product explainers, FAQs, and blog posts.
• Third-party content like reviews, Reddit threads, and Quora responses grow in value due to their authenticity and specificity.
• Multimodal content (text, images, video, audio) helps LLMs better interpret, contextualise, and present your offerings.
  

Retailers should shift from evaluating content based solely on traffic numbers to assessing how well it:

• Answers real customer questions
• Aligns with user intent
• Reflects the brand’s core value proposition

Looking Ahead: What’s Next

In the next article, we’ll share how we’ve been implementing these strategies - including updates to support agentic systems and AI-enhanced search. We’ll also explore how reporting and analytics need to evolve to accurately track performance in this new landscape.

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