Keyword research
Keyword research is the systematic search for the words and questions with which people search for a topic, product, or offering. The goal is to find out which terms your target audience actually uses, how often they are searched for, and what intent lies behind them. The results then steer which content you create and optimize, for classic search engines and increasingly for AI systems as well.
Why keyword research matters
Without keyword research, you write content into the blue. You guess what people search for instead of knowing it. This often leads you to use terms no one types in, while the phrasings actually asked for are missing. Clean research shows you where real demand exists and where only you yourself consider a word important. It also helps you set priorities: topics with high demand and matching intent first. In the context of AI visibility this applies doubly, because AI assistants base their answers on content that fits the real questions of users. Whoever hits the language of their target audience is more likely to be found and cited.
How keyword research works
You start with a list of terms around your offering, the so-called seed keywords. You expand these with tools that show related search terms, questions, and their search volume, that is, the estimated number of monthly searches. Then you assign each term a search intent: does someone want to know something, buy, or find a specific page? After that you cluster related terms into topics, instead of building a separate page for each individual word. Especially valuable are long-tail keywords, that is, longer, concrete search phrases with four or more words. They have less volume but clearer intent and fit well with the fully phrased questions people ask AI assistants.
Common mistakes
The classic mistake is chasing terms with high search volume without checking the intent. A word can be searched for a thousand times over and still have nothing to do with your offering. Just as risky is keyword cannibalization: several of your pages target the same term and compete against each other instead of complementing one another. Many also stuff texts full of keywords, which sounds unnatural and convinces neither people nor AI. Another mistake is doing the research once and never updating it, even though language and demand constantly shift. And finally: whoever thinks only of individual words instead of whole questions misses precisely the conversational phrasings that become decisive in the AI age.
Relation to AI recommendations
In classic search, people type in keywords. In AI assistants like ChatGPT or Perplexity, they ask whole questions in natural language. Your keyword research must therefore go beyond individual words and capture real questions, comparisons, and problem descriptions. When you know how people phrase their concerns, you can write content that answers these questions directly and citably. AI systems draw on exactly such passages when they compile an answer. Keyword research thus becomes the bridge between what people ask and what AI recommends. It is therefore a foundation of generative engine optimization, that is, optimization for generative search systems, not just an SEO tool of yesterday.
Example
Imagine a small bike shop that so far has bet only on the phrase "buy a bike". During keyword research the owner discovers that many people search specifically for "e-bike for commuters under 3000 euros" or "repair a flat tire yourself guide". He recognizes two intents: buying and learning. In response he creates a repair guide and a comparison page for commuter e-bikes. Weeks later his guide shows up as a source when someone asks an AI assistant about the tire repair. Guessed terms became targeted, sought-after content.
Common questions
What is the difference between a keyword and search intent?
A keyword is the concrete search term someone types in. The search intent is the purpose behind it, that is, whether the person wants to know something, buy, or find a specific page. Two identical keywords can have different intents. That is why it is not enough to collect only words, you also have to understand why people search for them.
Do I still need keyword research if people increasingly ask AI?
Yes, even more so. AI assistants answer questions based on existing content. When you know how people phrase their concerns as whole questions, you can answer exactly these questions citably. Keyword research shifts in the process from individual keywords toward complete, natural questions and topic areas.