Customer acquisition via LLMs: what companies need to know to drive visibility and sales

The changing search landscape: search engines vs. LLMs 

While search engines like Google remain dominant, their position is increasingly challenged by AI-powered alternatives. According to a recent study, LLMs now account for approximately 27% of all searches in the United States. At the same time, Google is changing the rules of traditional search. New features like AI Overviews and AI Mode (currently being rolled out in the US) are changing how we use search engines by turning traditional search into a chatbot-like experience.

This isn't just a minor shift in user behavior; it represents a fundamental change in how information is discovered and consumed online. The question is: Are consumer companies ready for when LLMs reach 50% or more of all search activity?

This evolution is further accelerated by platforms like Perplexity AI, which has gained significant traction by offering an AI-first search experience, and Microsoft's integration of OpenAI technology into Bing. For businesses, these developments mean that focusing exclusively on traditional SEO visibility is no longer sufficient. 

Beyond search volume: new KPIs for digital visibility 

For years, Google search volume has been the primary indicator of brand awareness and interest. However, as search behaviors fragment across multiple platforms, this metric tells an increasingly incomplete story.

What should companies measure instead? A more comprehensive approach might include:

1. Cross-platform visibility – presence across traditional search engines and AI platforms 
2. Impressions – how often does your brand appear in traditional search results? 
3. Conversation share – your brand's presence in AI conversations relative to competitors 
4. Content authority metrics – indicators of how authoritative your content is perceived by AI systems 

While Google's search volume remains valuable, it should now be considered part of a broader set of metrics that reflect the complexity of today's search ecosystem. 

Getting cited by AI: beyond traditional SEO 

Visibility in AI responses requires strategies that both build on and diverge from traditional SEO. There are several key factors that influence AI citations, partly already being relevant for traditional SEO as well: 

Authority remains crucial, but applies also to alternative content formats 

Websites with high domain authority are significantly more likely to be cited by AI systems. This suggests that established reputation and link equity – e. g. through citations from forums and review sites – continue to matter in the AI era, albeit through different mechanisms than traditional search. Video content, for example, is becoming increasingly important. Gemini pulls transcripts from YouTube videos and uses them as “citations”, making video a critical content format for visibility in AI-driven search. Various consumer brands have succeeded in using high-volume, high-velocity YouTube shorts to get AI models to associate the brand with authority, relevance, and trust. 

Content freshness and specificity 

AI systems show a preference for recent content, particularly for topics that evolve rapidly. Additionally, content that directly addresses specific questions comprehensively receives more citations than broader, more general content. This means creating targeted, detailed content that directly answers user intent is more important than ever. 

Structured content optimized for quick understanding 

AI systems favor content that is well-structured and easy to parse. This includes:

• Clear headings and subheadings 
• Concise paragraphs 
• Bulleted and numbered lists 
• Data tables 
• Definitions and explanations 
• Usage of structured data 
• Enable AI crawler access in robots.txt and firewall settings 

This structural clarity helps AI systems identify and extract relevant information efficiently, increasing the likelihood of citation. 

E-E-A-T remains center-stage

Expertise, Experience, Authoritativeness, and Trustworthiness (E-E-A-T) have long been important for SEO, but they're even more critical for AI citations. Content from recognized experts and authoritative sources is cited more frequently. This means:

• Demonstrating subject matter expertise 
• Building author credibility through credentials and previous work 
• Providing comprehensive, accurate, and well-sourced information 
• Creating content that demonstrates deep knowledge rather than surface-level coverage 
• Targeting user intent rather than keywords to answer a broad match of potential (follow-up) questions 
   

Technical optimization differences 

While traditional technical SEO factors like page speed and mobile-friendliness remain important for overall web presence, they appear to have less direct impact on AI citations than on traditional search rankings. Instead, technical considerations should focus on: 

• Proper semantic HTML structure since LLMs struggle to read Java script 
• Clear content organization 
• Machine-readable data formats 
• Comprehensive schema markup 
• Citation-friendly content formats 

Converting traffic in the LLM era: new user journeys 

LLMs are revolutionizing consumer journeys as AI systems increasingly bypass traditional website visits by providing direct answers and purchase options. This "zero-click" paradigm—exemplified by Google's AI Mode, its Virtual Try-On (VTO) for apparel and beauty, and Perplexity's "buy with pro" feature – fundamentally disrupts conversion pathways and attribution models. Despite these challenges, brands can leverage emerging opportunities through direct purchase integration within AI interfaces, strategic platform partnerships, and AI-assisted decision-making that guides consumers through comparison and personalization.

Building the AI-first organization 

To thrive in an AI-disrupted marketplace, organizations must evolve beyond superficial AI adoption toward true AI-first transformation. This means restructuring the organization with governance systems designed specifically for AI integration and decision-making. Leaders should prioritize investments in a unified data and AI tech stack that powers capabilities across all business functions while embedding AI within core processes rather than treating it as a peripheral initiative. Most critically, companies must reorient their talent strategy around AI readiness – developing both technical expertise and a culture where human-AI collaboration becomes instinctive.

Would you like to learn more about how to harness this shift to achieve a competitive advantage? Contact our experts using the form below!