With the increasing prevalence of AI-powered search and a noticeable rise in monthly LLM referral traffic, businesses and digital marketers are increasingly seeking ways to improve their visibility and brand sentiment in AI-generated responses. This pursuit is motivated by the evolving dynamics of search engine optimization (SEO), as traditional methods are now complemented by strategies that cater to artificial intelligence (AI).
One of the primary obstacles faced by marketing teams is the absence of a dedicated generative engine optimization (GEO) budget and the lack of team members with the capacity to fully support large-scale AI search optimization initiatives. The good news is that there are several strategies that can benefit both traditional SEO and AI visibility concurrently, making content more digestible for search engines, relevant for AI-generated responses, and valuable to the audience. Understanding these optimization techniques is crucial, as SEO is not dead but simply evolving into a more complex and nuanced practice.
According to industry experts, SEO is transforming into GEO, where success depends on being contextually relevant in AI-generated search experiences. This requires a strategic approach to ensure content not only ranks well but is also accurately represented in AI responses. Here are six ways to adapt traditional SEO strategies to bolster visibility in AI-powered search results.
1. Focus on long-tail keywords
The evolution of search behavior has necessitated a shift in traditional keyword research and selection. Brands are now encouraged to prioritize long-tail keywords that align with conversational queries and natural language processing rather than solely focusing on high volume, high competition “head” terms. AI search models often favor these more specific and intent-driven queries, which reflect users’ tendencies to search with full questions or complex phrases over short keywords. Long-tail keywords typically offer lower competition, making it easier for average sites to achieve higher rankings.
Moreover, considering that a significant percentage of daily searches are unique and have never been performed before, there is a notable demand for fresh and niche content optimized for emerging search trends. Hence, adjusting keyword strategy to include low-volume, specific terms can respond to this demand effectively.
How to adapt
Stop filtering out keywords with low search volumes, as even these terms can drive significant engagement over time. Conversational queries from platforms like Google’s People Also Ask, AnswerThePublic, and forums such as Reddit should be incorporated. Semantic search can be optimized using related phrases, synonyms, and natural language variations within content. Implementing helpful FAQs within content can also capture multiple long-tail queries.
2. Enhance content clarity and formatting
To enhance visibility in AI-generated search results and improve user experience, content clarity and structure play critical roles. AI models exhibit a preference for well-organized, skimmable, and concise information. These characteristics not only help with search rankings but also ensure users can quickly digest the provided content.
How to adapt
Adjust content development processes to include key takeaways at the top, write clear and concise summaries for primary sections, and utilize proper heading structures (e.g., #, ##, ###) to break up text. Implementing tables of contents for longer content, with jump links to improve the user experience, is also beneficial.
In addition, leveraging AI tools to refresh existing content can lead to improved SEO and AI visibility. Incorporating key takeaways and enhancing the structure of already published content can yield quick results. Agencies have observed AI Overviews integrating re-formatted content in as little as 24 hours post-implementation in certain instances.
3. Offer balanced viewpoints
AI models, designed to present unbiased and comprehensive summaries, heavily weigh various viewpoints to avoid bias. This is especially noticeable when users seek recommendations or comparisons. Balanced and unbiased content tends to be cited more frequently in AI-generated responses.
How to adapt
Clearly stating pros and cons, adding strengths and weaknesses, and incorporating benefits and drawbacks within content can cater to this need. Summary tables are particularly useful for comparisons and aid both users and AI in extracting valuable information.
Additionally, adopting less absolute language and integrating more comparative language, as AI prefers nuanced language, is recommended. For instance, using phrases like “best for” or “more ideal when” instead of definitive opinions can be beneficial. Addressing counterarguments adds depth and provides a comprehensive perspective, further improving content relevance and credibility.
4. Boost technical SEO
Content quality is pivotal, but technical SEO forms the foundation for enabling AI models to accurately interpret and represent a site’s information. While AI models do not crawl sites in real-time like traditional search engine crawlers, they still rely on well-structured content to render accurate search results.
How to adapt
Implementing technologies like a hypothetical llms.txt file, which can be used to provide specific content to AI models, though its utility is still uncertain. Schema Markup is essential, offering explicit signals about content meaning. Article, FAQ, product, and review schemas are among the types to consider, and automating schema deployment can streamline this process.
Ensuring crawlability and indexation is fundamental. This involves maintaining logical site architecture, strong internal linking, minimizing unnecessary redirects, and properly managing the robots.txt file to ensure fast loading pages. Additionally, since many AI crawlers do not execute JavaScript, ensuring vital content is server-rendered or visible in raw HTML rather than just JavaScript is crucial.
5. Rely on data-driven insights
AI models prioritize authoritative, credible information, which can be bolstered by a data-driven approach. Unique data enhances content’s credibility and attractiveness for AI citations, distinguishing it from the plethora of generic, machine-generated content.
How to adapt
Leveraging proprietary data and tailored data collection methods such as surveys and case studies helps create unique data sets. This differentiation ensures content stands out in a landscape filled with low-quality, regurgitated material. Citing credible and authoritative sources when referencing external information is also vital. If AI is used to scale content creation, ensuring the uniqueness and originality of the output through plagiarism checking is mandatory.
6. Track and evaluate performance
Efforts to improve AI visibility must be tracked and evaluated over time to determine their impact. This tracking is also essential for developing a business case for AI optimization budgets and resources and demonstrating tangible benefits and gains.
How to adapt
Creating dashboards capable of tracking LLM referral traffic, sources, ratios, and referral conversions is recommended. These dashboards should compare AI overview presence over time using tools like Semrush. Budgeting for monitoring tools like Scrunch AI or the Semrush AI toolkit can provide valuable insights into LLM visibility across various AI platforms.
Ultimate goal: integrating advanced tactics
For those aiming for a more comprehensive AI optimization strategy, integrating advanced tactics can offer substantial benefits:
- Digital PR for authority building: Online mentions, citations, and brand authority significantly influence AI-generated search responses. Mentioning brand names across relevant web conversations enhances visibility.
- Optimizing Wikipedia presence: Wikipedia’s credibility in the digital ecosystem makes it a valuable resource for AI models. A well-maintained Wikipedia page can significantly impact online authority, although it requires strict adherence to guidelines and ongoing updates.
Key Takeaway
AI models put a strong emphasis on sourcing from authoritative and credible information, which can be enhanced through a data-driven strategy. Unique data sets not only boost content’s credibility but also increase its appeal for AI citations, making it stand out from the flood of generic, machine-generated content.
How to Adapt
To effectively adapt, it’s crucial to leverage proprietary data and use customized data collection methods like surveys and case studies. This approach helps in creating unique data sets that can distinguish your content in an environment saturated with low-quality, repetitive material. Citing credible and authoritative sources when referencing external information is also essential. If AI is used to scale up content creation, it’s mandatory to ensure the output’s uniqueness and originality through rigorous plagiarism checks.
Employing these techniques means your content can not only avoid being lost in the sea of generic information but also be recognized as a reliable and distinctive resource. The aim is to create content that is not only data-rich but also trustworthy, thereby maximizing its value for both human readers and AI systems.
