Optimizing LLMs for B2B SEO: An overview

We are still in the (very) early days for LLM (large language model) search, but rapidly growing user adoption helps us gain insight into effective tactics that brands can employ to appear in results on platforms such as Confusion, ChatGPT searchGemini and more.

This article looks at those tactics from a B2B lens, broken down into the following SEO initiatives:

Keep in mind that many of these tactics—but not all—should be familiar to SEOs who have experience with traditional search engines.

Content strategy

The first step to creating effective LLM content is to understand the nature of user queries.

LLMs, more than traditional search engines, host conversational queries, such as “How can I protect my business from ransomware attacks?” (where a similar Google query might be “ransomware attack protection for businesses”).

To tailor your content strategy, study the nature of queries and create content that directly answers them. These include chatty headlines like “The Best Software to Protect Your Business Against Ransomware Attacks.”

In B2B, where the journey to purchase is longer, it’s not as simple as optimizing for product-related queries; it is crucial to include educational content to facilitate the level of awareness and engagement of users.

When it comes to the content itself, many of the principles of traditional SEO apply – particularly the need to go broad and deep to establish authority and relevance.

Include supporting content like guidescase studies and user testimonials.

Make sure you work with column pages that link to detailed blogs like “How CRM Helps Sales Teams Close Deals Faster”.

Note that context is very important to LLM for every piece of content (regardless of format).

Optimize for nuanced, contextual responses by addressing multiple aspects of a topic in the same article.

For example, a rich blog post for a fintech company might be titled “What is Embedded Finance? Advantages and Challenges for SaaS Platforms,” with subsections for:

• Advantages for startups.
• Use cases in real world scenarios.
• Integration challenges and how to overcome them.

Semantic SEO

“Semantic SEO” is a relatively recent SEO initiative that means approaching content with regard to the entire topic, not just key elements.

In LLM SEO, the first item of semantic SEO is entity-based optimizationwhich includes:

For example, a cloud solution provider can use schema markup to:

• Label product pages with the “Product” scheme for solutions such as “Cloud Storage Services”.
• Build authority by linking to their business profile on Wikipedia, LinkedIn and/or Crunchbase.

Since semantic SEO expands its focus from keywords, it’s essential to optimize for different phrases and synonyms instead of fixating solely on exact match keywords.

(You can use tools like Google Natural Language Processing or OpenAI embeds to understand the relationship between tools.)

Let’s take a marketing automation platform as an example.

In addition to optimizing for a primary keyword such as “lead generation software”, include synonyms and variants such as “Automated lead management tools” and “B2B marketing platforms”.

Dig deeper: ChatGPT search vs. Google: detailed analysis of 62 queries

Technical SEO

At this moment, technical SEO for LLM is not (as far as I understand) that different from technical SEO for traditional search engines.

To increase your chances of appearing in LLM searches, address the following:

Availability of data

• Confirm that the content can be indexed and indexed by search engines and is available for API integrations.
• Optimize page speed and mobile performance for improved usability.

Structured data

• Leverage structured data to clearly signal intent and relevance.
• Implement detailed schema such as “FAQPage”, “HowTo” and “Product” to improve how LLMs process your content.

Matching user intent

Advanced SEO in traditional search and LLM involves understanding user intent into the content.

For B2B, this content should be strategically distributed across all stages of the buyer’s journey: awareness, education, technical understanding of the solution, and ultimately purchase intent.

For “in-the-moment” queries, provide actionable and direct responses, framing responses in bullet points or concise paragraphs for LLM readiness while providing links to more in-depth resources.

For example, a company that offers AI-based analytics might create content like, “What is predictive analytics in B2B?” and give direct answers such as:

• “Predictive analytics uses historical data to predict future trends. For B2B, this helps identify potential leads and optimize sales strategies.”

Dig deeper: How to optimize for search intent: 19 practical tips

Authority and trust

This is perhaps the area where we (yet) see almost no difference between LLM and traditional search engines: establishment EAT principles is critical.

To do this (if you haven’t already), make sure you own the media:

• It gives priority to experience, expertise, authoritativeness and reliability in all contents.
• Includes author bio, credentials and citations to enhance credibility.
• It cites reliable sources such as Gartner, Forrester or proprietary data studies.
• It builds backlinks from authoritative domains to strengthen your site’s credibility.
• Get mentions in trusted publications to improve how LLMs perceive your brand.

For example, a logistics software company might provide backlinks from:

• Industry publications such as logistics management.
• Mentions in business-oriented media such as TechCrunch or Forbes.

Dig deeper: Decoding Google’s EEAT: A comprehensive guide to quality assessment signals

Optimization of AI features

This initiative is where the practice of SEO differs most from traditional search engines.

The way users interact with LLM is different from the way they interact with the Google search bar.

For LLM-specific content enhancements:

• Focus on content that answers “People Also Ask” and queries that follow the conversation.
• Experiment with creating and optimizing content designed for direct API consumption.

For example, a technology consulting firm could create a resource hub for topics such as “common cloud migration questions” with detailed question-and-answer formats that AI can easily display.

If user behavior continues to include more structured, question-based queries, make sure your content is designed to answer them directly.

For example, a company specializing in ERP software can design content that will appear for queries such as:

• “What are the best ERP solutions for medium-sized companies?”
• “What is the ROI of implementing ERP software?”

Some LLMs (and we expect more to go in that direction) are focused on multimedia.

For them, integrating rich media – using videos, infographics and charts to increase engagement and improve content findability – will help drive engagement in search results.

For example, a cybersecurity company can improve blogs with:

• An infographic summarizing “5 types of cyberattacks companies should watch out for in 2025.”
• Embedded videos explaining “How our real-time threat detection tool works.”

Dig deeper: How to develop your organic approach to the rise of responses

Continuous testing and adaptation

At this relatively early stage of LLM SEO maturity (and our understanding of it), continuous testing, measurement and adaptation are among the most critical initiatives.

In our agency, we are focused on two fronts:

• Query optimization
• Monitor SERP changes
  • Track how your pages appear in chat results or summaries generated by LLM.

As you gather more information about what works, you can find common themes to implement in your accounts.

Dig deeper: How to cultivate SEO growth through continuous improvement

LLM-Driven Search Optimization in B2B

Because LLMs are in their infancy and user behavior is changing so quickly across the search field, find reliable sources and refer to them regularly to stay abreast of trends and developments.

In 12 months, this article might seem terribly out of date, so it’s best to keep your finger on the pulse to adapt quickly.

Dig deeper: Decoding the LLM: How to be visible in generative AI search results

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