LLMs vs. Search Engines: Which Traffic Actually Converts?

The digital landscape is no longer a monopoly of traditional search engines. With the rise of Large Language Models (LLMs) like ChatGPT, Claude, and Gemini, the way users seek information has fundamentally shifted.

For marketers in the UK and Europe, the question isn’t just where the traffic comes from, but which platform delivers the highest conversion rate.

FastSearch and RankEmbed in the SERP Evolution

To maintain their dominance in transactional searches, traditional search engines are not standing still. They are rapidly integrating technologies similar to those powering LLMs to speed up indexing and improve result quality.

The concepts of FastSearch and RankEmbed illustrate this evolution:

• FastSearch (Efficiency in Indexing): This refers to the optimization of search infrastructure to retrieve vast amounts of data in milliseconds. While not a single named algorithm, it represents the move towards highly optimized, distributed indexing systems that allow search engines to process user queries and display results almost instantly, a critical factor when competing with the immediate answers provided by LLMs. For the user, this means less time spent waiting and a quicker path to conversion.
  
• RankEmbed (Semantic Understanding): This is where LLM technology directly impacts search ranking. RankEmbed (or similar vector-based ranking systems) utilizes deep learning models to convert both the search query and the document content into high-dimensional vectors (embeddings). The engine then ranks results based on the semantic similarity between the query vector and the document vectors, moving far beyond simple keyword matching. This ensures that a search for “best investment ISA London” returns results that truly match the intent of opening an ISA, not just pages that mention the words, thus dramatically increasing the quality and conversion potential of organic traffic.

By leveraging these advanced, LLM-inspired techniques, search engines ensure that their results—which still carry the direct link-to-transaction advantage—remain highly relevant and fast, securing their position as the primary platform for high-intent conversions.

The “Answer Engine” vs. The “Search Engine”

Traditional Google search results are increasingly dominated by “Zero-Click” searches. Users looking for quick facts or comparisons now turn to LLMs.

 While this might look like a loss in volume for SEOs, it’s actually a filter for quality.

• LLM Traffic: Often informational, top-of-funnel, and research-oriented.
• Search Engine Traffic: Increasingly shifting toward high-intent, bottom-of-funnel queries where users are ready to transact.

The Evolving Landscape of Search: LLMs vs. Traditional Search Engines

The digital information ecosystem is undergoing a profound transformation, fundamentally reshaping how users seek information and, consequently, how Search Engine Optimization (SEO) professionals must approach their strategies. The rise of Large Language Models (LLMs) and generative AI has become a primary catalyst in this shift, challenging the dominance of traditional search engines like Google in specific query categories.

The Rise of Zero-Click and the LLM Filter

A significant trend observed in traditional Google search is the escalating proportion of “Zero-Click” searches. These are queries where the user’s information need is met directly on the Search Engine Results Page (SERP)—often through Featured Snippets, Knowledge Panels, or, increasingly, AI-generated summaries and answers—leading the user to exit the search without clicking through to an external website.

Users are increasingly turning to LLMs for:

• Quick Facts and Definitions: Needing a single, authoritative piece of data or a concise explanation.
• Comparisons and Summaries: Requesting a synthesis of multiple data points or a direct comparison between two or more entities without having to visit and read several competing articles.
• Top-of-Funnel Research: Starting a research journey where the initial goal is to understand a topic broadly, define terms, or establish a foundational knowledge base.

While this diversion of informational, “quick answer” traffic to LLMs might initially appear as a detrimental loss in volume for SEOs, it is, in fact, functioning as an essential filter for quality and intent across the broader search landscape.

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Differentiating Traffic Intent

The primary distinction between the two channels now lies in the user’s underlying intent. LLM traffic is informational and exploratory.

The traffic captured by LLMs is characteristically aligned with the initial stages of the user journey:

• Funnel Stage: Predominantly Top-of-Funnel (ToFu) and early Middle-of-Funnel (MoFu).
• Intent Profile: Highly informational and research-oriented. Users are in the learning and discovery phase. They are asking “what,” “why,” and “how does this work.”
• Monetization Potential: Direct monetization (conversion/transaction) is low. The value here lies in brand awareness, establishing authority, and future lead nurturing.

Search Engine Traffic: High-Intent & Transactional

In contrast, the residual traffic remaining for traditional SEO clicks is becoming significantly more valuable and specialized:

• Funnel Stage: Increasingly shifting toward Bottom-of-Funnel (BoFu) and late Middle-of-Funnel (MoFu) queries.
• Intent Profile: High-intent, often commercial or transactional. Users are asking “which is the best,” “where can I buy,” “how much does it cost,” or “book a consultation.” They have largely completed their initial research and are ready to take the next step.
• Monetization Potential: Direct monetization (conversion, sale, subscription) is high. The clicks are fewer, but the conversion rate of that traffic is potentially much higher.

Strategic Implication for Modern SEO

This paradigm shift necessitates a refinement of SEO strategy. Rather than mourning the loss of high-volume, low-intent clicks, SEO professionals must pivot to dominate the high-value, high-intent traffic:

1. Focus on Transactional SEO: Prioritize content and keywords explicitly targeting commercial and purchase-related queries. This includes optimizing product pages, service landing pages, and detailed buyer’s guides.
2. Optimize for Authority and Trust: For informational queries that remain relevant, the goal is not just an answer but the authoritative, sourceable answer that LLMs will cite or that users will turn to for validation.
3. Content Utility Over Quantity: Content must be more than just informational; it must be actionable. Provide tools, calculators, templates, or clear calls-to-action that facilitate the user’s next step, a utility LLMs cannot fully replace.

Why Search Traffic Still Leads in Conversions

Citing recent industry trends (such as Gartner’s prediction of a 25% drop in search volume by 2026), we must focus on intent. When a user in the UK searches for a “Luxury Charter in the Adriatic,” they are looking for options to book. When they ask ChatGPT “What is it like to sail in Croatia?”, they are merely exploring.

The conversion gap lies in the frictionless path to purchase that traditional SERPs still provide through direct links, which LLMs are only beginning to integrate.

The New Conversion Metric: AI Brand Authority

To succeed on the .eu market, brands must stop measuring success solely by clicks. We need to look at AI Sentiment and Citation.

If an LLM recommends your brand as a trusted authority, the “conversion” happens in the user’s mind long before they even land on your site. This is what we at Bruce Clay call Search & AI Governance.

Build your AI Brand Authority now

Conclusion

The goal is not to choose between LLM traffic and Search Engine traffic, but to optimize for both. However, if your KPI is immediate ROI, traditional search remains the king of conversion—provided your SEO strategy is sophisticated enough to capture “high-intent” users.

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