Relevance in AI-powered search
Why marketers more than ever need to do their homework
Relevance in AI-powered search
The way people search for information on the internet is currently undergoing a fundamental change. Instead of entering traditional search queries into Google and then clicking through links, more and more users are turning to AI-based applications. Systems like ChatGPT, Perplexity, or Google's new AI Overviews provide answers partially directly and without the detour through traditional search result pages. Companies are now rightly wondering how they can remain visible with their content in the future. We have addressed this question for you and aim to answer it with this article.
AI-based search - just another hype?
The actual extent of the change in web search is currently difficult to grasp. Initial studies (e.g. by Sparktoro) show that AI-based searches currently account for only about one percent of all desktop searches. There are indications that the topic is at least partially driven by hype. However, it is likely that this share will grow significantly in the near future, especially as the systems continue to evolve technically and integrate even better into everyday usage contexts. For marketers, this raises the question on the surface of how to remain visible in this new search world. In depth, it is about which content is still captured by the AI systems and how to ensure that one's own offerings appear in the responses. The temptation is great to immediately engage in operational measures such as AI-optimized SEO or prompt engineering. However, before focusing on tools or techniques, a much more fundamental task is at hand.
Visibility starts with relevance - and relevance starts with the customer.
Whoever wants to appear in the responses of AIs in the future must understand how these systems work. They do not evaluate content based on classical ranking factors or individual keywords, but primarily based on their content relevance. This means: Only those who provide real answers to real problems will even be considered. The basis for this - and this is actually nothing new - is a deep understanding of one's own target audience. It is not sufficient to use rough target group descriptions in the style of 'medium-sized companies in the mechanical engineering sector'. (Well, that has actually never been sufficient.) What is crucial is dealing with specific customer situations. What problems are my customers facing? What wordings do they use to search for solutions? What benefit could my product or service provide for the customer in this context? And at what point in the decision-making process does my product or service even appear as an option? Only those who can answer these questions are able to develop content that makes sense for both humans and machines. This sounds trivial, but unfortunately is still often not practiced. Established tools such as persona development, customer journey mapping, or the digital relevancy map are available to systematically make these perspective shifts. A particularly effective strategy is to address individual customer issues as concretely as possible, show appropriate solutions, and clearly state the resulting benefits. The clearer and more tangible this connection becomes, the more likely both users and AI systems will recognize the relevance of the offer.
Content must not only be relevant, but also "understandable" for search engines and AIs.
Once the content relevance has been cleanly worked out, the second step begins: The best possible technical preparation for AI-driven searches. Because in order to be considered there, it is not enough to write good content. They should be structured and prepared in such a way that they can be efficiently processed by machines. This starts with a clear structure according to search intentions – that is, the question of which information need a text actually satisfies – and extends to the use of structured data formats like schema.org markups or semantically unambiguous navigation structures. FAQs are a very good way to execute a variety of problem and solution combinations and to mark them as such using structured data.
Especially in a time when Search-GPT systems are not based solely on pre-trained models, but regularly retrieve content in real time from websites, this technical readability becomes a crucial factor. Because most AI systems do not rely solely on stored knowledge databases, but access current web content – be it through interfaces, APIs, or classic search engine queries in the background. What is found there must be immediately usable – otherwise it will be out of context.
KI-Crawlers still struggle with JavaScript (yet)!
In the good old days of SEO, it has always been good practice to make important content as accessible as possible for search engine crawlers. This also applies to AI crawlers. Interestingly, AI crawlers, for example, struggle to understand JavaScript. In a world where headless content management systems are increasingly used, this naturally has consequences. If you want content from such a system to be interpretable by AI crawlers, you should avoid client-side rendering and instead prefer server-side rendering.
Is good old online PR making a comeback?
In addition to the relevance of the content for the target audience, another factor seems to be essential for presence in AI searches or overviews. Many blog articles mention the frequency of the mention of one's own brand on the web (brand web mentions) as the most important factor. This is even more important than the classics such as backlinks, branded links, and branded searches. This also seems logical, as some Search-GPT applications have no knowledge of these factors. Therefore, many SEOs and GEOs predict a comeback of the good old online PR, where marketers retain control over their texts and thus the density of the company and product brands used.
The new search world does not require a new mindset, but rather clean foundational work.
Many companies are currently facing the question of how to adapt their marketing to the new logic of AI-powered search. The good news is: The fundamental principles of good digital communication still apply. What has changed is the scale at which content is evaluated and distributed. It is not enough to provide general statements or superficial product descriptions. Instead, it is crucial to design content in a way that precisely addresses specific issues of the target audience, shows understandable solution paths, and clearly names the resulting benefits for the customer. This content sharpening is convincing not only for human readers but also increases the chance of being considered a relevant source by AI-based systems. To remain visible, there is no need to fear new tools or technologies. Instead, it is important to consistently work on relevance - in terms of content, structure, and strategy. The most important measure for this is not the introduction of new software, but the honest engagement with what truly moves customers. Or in other words: Those who do their homework will also be found in the world of SearchGPT & Co. Not because they shout the loudest - but because they provide the right answers.
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