As search evolves with the growing adoption of Large Language Models (LLMs), businesses must adapt their SEO strategies. While LLM-powered search is still in its early stages, platforms like Perplexity, ChatGPT, Gemini and others are already providing key insights into how brands can optimize their content to appear in AI-generated results.
My agency works with dozens of high-growth B2B brands who depend on organic engagement to grow their business—which means we need to recognize and leverage LLM search best practices as more users rely on a new method of surfacing information.
Crucial LLM SEO strategies for B2B include:
- Strategic content development
- Semantic SEO
- Technical SEO
- User intent recognition
- E-E-A-T principles (Experience, Expertise, Authoritativeness and Trustworthiness)
- Optimization for AI-driven user behavior
- Continuous testing and adaptation
While many of these elements align with traditional SEO, some require a more nuanced approach to optimize content for LLM search. Let’s explore each.
Strategic Content Development
Unlike keyword-focused searches of traditional search engines, LLM interactions are conversational. Instead of searching for “malware protection for businesses,” a user might ask, “How can I protect my business from malware?”
To align with these queries, brands must create content that directly answers conversational-style questions. This means structuring content with natural, question-based headings like “The Best Software to Protect Businesses from Malware.”
B2B decision-making journeys are often lengthy, spanning awareness to consideration and purchase. Brands should build pillar pages linked to resources such as case studies, guides and testimonials. For example, an HR software company could publish an article titled “Which Benefits Are Most Popular with Your Employees?” with subsections covering topics like identifying new benefits, analyzing employee usage and improving engagement.
Additionally, context is crucial for LLMs. To improve retrieval accuracy, content should cover multiple facets of a topic, providing rich, structured responses.
Semantic SEO
Semantic SEO focuses on optimizing content around broader topics, not just specific keywords. In LLM search, entity-based optimization plays a significant role. Key strategies include:
- Implementing schema markup, such as FAQ, HowTo and Organization schema, to enhance content understanding.
- Building a knowledge graph by establishing a presence on authoritative sites both in your industry and in more broadly referenced properties, like Wikipedia.
Moreover, LLM SEO requires optimization beyond exact-match keywords. Including diverse phrases and synonyms increases contextual relevance. For example, an employee productivity tool should optimize for terms like “employer efficiency software” and “tools for workplace performance tracking.”
Technical SEO
At this stage, technical SEO for LLMs largely mirrors traditional search. However, brands should prioritize:
Data Accessibility
- Ensure all content is crawlable and indexable.
- Optimize API integrations to enhance accessibility.
- Improve page speed and mobile performance.
Structured Data Implementation
- Use structured data to clarify content intent.
- Implement schema types such as “FAQPage,” “HowTo,” and “Product” to improve retrieval accuracy.
User Intent Recognition
B2B content in LLM search, like content optimized for traditional search, must be rooted in an understanding of user intent. Content should align with various intent stages:
- Awareness: “What is {product category}?”
- Education: “How does {product} help {end user}?”
- Technical Understanding: “Key features of {product} platforms”
- Purchase Consideration: “Best {product} tools for {segment of end users}”
For immediate-response queries, brands should provide concise, well-structured answers in bullet points or short paragraphs.
For instance, an airline logistics software company might write: “Analysis of weather and traffic patterns can help private airlines increase passenger satisfaction by more than 20 percent.”
Authority and Trust (E-E-A-T)
Credibility remains vital in SEO, and LLMs prioritize authoritative content that adheres to E-E-A-T principles. To strengthen trustworthiness:
- Include author bios with credentials.
- Cite reputable sources like the Wall Street Journal or New York Times.
- Build backlinks from industry-specific publications.
- Gain mentions from trusted outlets like Fast Company.
Optimization for AI User Behavior
LLM-driven search differs from traditional search engines, requiring content adaptations such as:
- Answering “People Also Ask” queries and conversational follow-ups.
- Structuring content for direct API consumption.
Properties like detailed FAQ pages with Q&A-style content, for instance, are likely to increase AI discoverability.
Vary content types to give AI a greater range of information to digest. For example, a software business for property managers firm can enhance blogs with:
- Infographics summarizing “How to Assess Security Deposit Deductions.”
- Embedded videos explaining “How We Diagnose Structural Issues in Building Complexes.”
Continuous Testing and Adaptation
Because LLM-based search is still evolving, businesses must engage in continuous testing and refinement. Key areas to monitor include:
- Query Optimization: Use tools like Google Analytics 4 and Search Console to analyze LLM-driven queries.
- AI Summarization Performance: Track how content appears in AI-generated summaries.
- SERP Changes: Observe rankings in both traditional and AI-powered search results.
Final Thoughts
The search landscape is undergoing rapid change. Long-term SEO success in the LLM era will depend on continuous learning, testing and iteration based on data-driven insights.
Kelly Ayres is Director of SEO at Jordan Digital Marketing (JDM).
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