We are still at the (very) beginning of LLM (large language model), but rapid user adoption helps us derive insights into effective tactics brands can deploy to appear in results on platforms like Perplexity, Search ChatGPTGemini and more.
This article examines these tactics from a B2B perspective, broken down as follows SEO initiative :
Note that many of these tactics – but not all – should be familiar to SEOs experienced with traditional search engines.
Content strategy
The first step towards creating effective content for LLMs is understanding 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 adapt your content strategy, study the nature of the queries and create content that directly answers them. This includes conversational headlines like “The Best Software to Protect Businesses from Ransomware Attacks.”
In B2B, where the purchasing journey is longer, it’s not as simple as optimizing product-related queries; It is essential to incorporate educational content to facilitate users in the awareness and engagement stages.
When it comes to the content itself, many of the principles of traditional SEO apply, particularly the need to go both broad and deep to establish authority and relevance.
Incorporate supporting content like guidescase studies and user testimonials.
Be sure to work with pillar pages linking to in-depth blogs like “How CRM Helps Sales Teams Close Deals Faster.”
Remember that context is very important for LLMs for each piece of content (regardless of format).
Maximize nuanced and contextual responses by addressing multiple facets of a topic in the same article.
For example, a rich blog post for a fintech company might be titled “What is Embedded Finance?” Benefits and Challenges of SaaS Platforms,” with subsections for:
Benefits for startups. Use cases in real-world scenarios. Integration challenges and how to overcome them.
Semantic SEO
“Semantic SEOis a relatively new SEO initiative that involves approaching content in relation to the topic as a whole, not just keyword elements.
In LLM SEO, the first element of semantic SEO is entity-based optimizationwhich includes:
For example, a cloud solution provider can use schema markup to:
Tag product pages with “Product” schema for solutions like “Cloud Data Storage Services.” Build your authority by linking to their business profile on Wikipedia, LinkedIn and/or Crunchbase.
Since semantic SEO extends beyond keywords, it is essential to optimize for various phrases and synonyms instead of focusing only on exact match keywords.
(You can use tools like Google Natural Language Processing or OpenAI integrations to understand the relationship between tools.)
Let’s take the example of a marketing automation platform.
In addition to optimizing for a primary keyword, like “lead generation software,” include synonyms and variations like “Automated Lead Management Tools” and “B2B Marketing Platforms.”
Dig Deeper: ChatGPT vs Google search: an in-depth analysis of 62 queries
Technical SEO
At this point, technical SEO for LLMs is not (from my understanding) that different from technical SEO for traditional search engines.
To increase your chances of appearing in LLM searches, address the following points:
Data Accessibility
Confirm that the content is crawlable and indexable by search engines and 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 a detailed schema, such as “FAQPage”, “HowTo”, and “Product” to improve the way LLMs handle your content.
Matching User Intents
Advanced referencing in traditional research and LLMs incorporates an understanding of user intent in the content.
For B2B, this content must be strategically distributed at all stages of the buying journey: awareness, education, technical understanding of solutions and, ultimately, purchase intent.
For “snapshot” queries, provide direct, actionable answers, formatting answers as concise bullet points or paragraphs for LLM-readiness while providing links to more in-depth resources.
For example, a company offering AI-powered analytics might create content such as: “What is predictive analytics in B2B?” and provide direct answers such as:
“Predictive analytics uses historical data to predict future trends. For B2B, this makes it possible to identify potential prospects and optimize sales strategies.
Dig Deeper: How to Optimize Search Intent: 19 Practical Tips
Authority and trust
This is perhaps the area where we see almost no difference (yet) between LLMs and traditional search engines: establishing EAT principles are essential.
To do this (if you haven’t already), make sure your media belongs to:
Prioritizes experience, expertise, authority and trustworthiness in all content. Includes author biographies, identifying information, and citations to enhance reliability. Cites reliable sources like Gartner, Forrester or proprietary data studies. Creates backlinks from authoritative domains to build credibility for your site. Get mentions in trusted publications to improve how LLMs perceive your brand.
For example, a logistics software company could get backlinks from:
Industry publications like Logistics Management. Mentions in business-oriented media like TechCrunch or Forbes.
Dig Deeper: Decoding Google’s EEAT: A Complete Guide to Quality Rating Signals
Optimizing AI Features
This initiative is where SEO practices deviate the most from traditional search engines.
The way users interact with LLMs differs from how they interact with the Google search bar.
For LLM-specific content enhancements:
Focus on content that answers “People also ask” and conversational follow-up queries. Experiment by creating and optimizing content designed for direct API consumption.
For example, a technology consulting firm could create a resource center for topics like “common cloud migration questions” with detailed Q&A formats that AI can easily surface.
If user behavior continues to feature 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 display for queries such as:
“What are the best ERP solutions for mid-sized businesses? » “What is the return on investment of implementing ERP software? »
Some LLMs (and we anticipate more will move in this direction) are multimedia focused.
For these, rich media integration – using videos, infographics and charts to build engagement and improve content retrievability – will help drive inclusion in search results.
For example, a cybersecurity company can improve blogs with:
Infographic summarizing “5 types of cyberattacks businesses should pay attention to in 2025”. Embedded videos explaining “How our real-time threat detection tool works”.
Dig Deeper: How to scale your organic approach for answer engine growth
Continuous testing and adaptation
At this relatively early stage of LLM SEO maturity (and our understanding), continuous testing, measurement, and adaptation are among the most critical initiatives.
In our agency, we focus on two fronts:
Query Optimization
Monitor SERP changes
Track how your pages appear in conversational results or summaries generated by LLM.
As you gather more information about what works, you can find common themes to deploy across your accounts.
Dig Deeper: How to Cultivate SEO Growth Through Continuous Improvement
Optimization for LLM-based research in B2B
Because LLMs are in their infancy and because user behavior is changing so quickly in the research landscape, regularly research and reference reliable sources to stay on top of trends and developments.
In 12 months, this article might seem woefully outdated, so it’s best to stay tuned to adapt quickly.
Dig Deeper: Decoding LLMs: How to Be Visible in Generative AI Search Results
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