Keyword Research 2026: From Google Searches to AI Prompts

Keyword research determines whether content is found or remains unread. It is the first step of every SEO and content strategy and answers a simple but consequential question: What is the target audience actually searching for, and with what intent? This guide leads step by step through the research process, from seed keywords through tool-supported expansion to a prioritized keyword list. It shows how to classify search intent, read metrics correctly, bundle keywords into topic clusters, and align your research with AI search and the Austrian B2B market.

Why Keyword Research Is the Foundation of Every Content Strategy

Without proper keyword research, content is created past the actual need. Research provides the data foundation for which topics have demand, how high competition is, and what format a search query expects. In the Austrian market, this work practically coincides with Google. Google holds a search engine market share of around 82 percent in Austria, ahead of Bing with 9.01 percent (as of May 2026, all platforms), on mobile devices even over 90 percent (May 2026). Anyone researching for the DACH region is thus researching almost exclusively for Google and its AI answers.

A central finding shapes the entire discipline: The majority of demand does not lie in a few popular terms, but in the long list of rare, specific search queries. An international analysis of 306 million keywords shows that 91.8 percent of all search terms are long-tail keywords with 1 to 100 searches per month (international survey). The median search volume per keyword is only 10 searches per month (international survey). An evaluation of Ahrefs' US database confirms the magnitude: around 93 percent of all keywords have fewer than 10 searches per month (US database). From this follows the most important strategic consequence: Good research does not only chase large volumes but systematically taps the breadth of specific, purchase-ready queries.

The Keyword Research Process Step by Step

Reliable research follows a clear sequence from rough starting point to prioritized list.

• Collect seed keywords: Start with 5 to 15 basic terms that describe your offering. Sources are your own service portfolio, customer language, sales inquiries, and the terms with which competitors rank.
• Brainstorming and customer language: Add synonyms, problems, and use cases. B2B target groups often search differently than a company internally names its products. Note the actual wording from support tickets and sales conversations.
• Tool-supported expansion: Expand the seeds with tools to hundreds of variants. Google Keyword Planner provides volume and competition data directly from Google Ads, Ahrefs and Semrush add Keyword Difficulty, SERP data, and keyword ideas, AnswerThePublic visualizes questions around a topic.
• Evaluate questions and related searches: Use People Also Ask, Google autocomplete, and related searches at the page bottom. They show how real users formulate and what follow-up questions arise.
• Cluster and prioritize: Group the raw list by topic and intent and prioritize by relevance, effort, and business value, not by search volume alone.

The result is not a flat list, but a keyword map structured by topics and intent that flows directly into content planning.

Classify Search Intent and Derive the Right Format

Search intent is more important than search volume. A keyword with high volume is worthless if the created content misses the intent behind it. Classically, four intent types are distinguished:

• Informational: The user seeks knowledge, such as a guide, definition, or comparison. Appropriate format: guide, tutorial, glossary.
• Navigational: The user wants to reach a specific website or brand. Appropriate format: the respective brand or product page.
• Commercial: The user researches before a purchase decision, compares providers, or reads tests. Appropriate format: comparison page, case study, service page.
• Transactional: The user wants to act, buy, request, book. Appropriate format: product, offer, or contact page.

The information-oriented part clearly dominates the web. A scientific analysis of web search queries shows that over 80 percent of all search queries are informational, with around 10 percent each navigational and transactional (international study). This distribution explains why informative content forms the foundation of visibility and is simultaneously the most common entry point in AI answers.

The most reliable method for intent determination is SERP analysis: Enter the keyword on Google and check which content actually ranks. If guides are there, the intent is informational. If shop or product pages dominate, it is transactional. The currently ranking results are Google's interpretation of intent. Anyone offering a different format rarely ranks.

Read and Prioritize Keyword Metrics Correctly

Four metrics form the core of every prioritization.

• Search volume: Estimated monthly search queries. An orientation value for demand, but never the sole criterion.
• Keyword Difficulty (KD): An estimate of how difficult a top ranking is, usually derived from the strength of already ranking pages.
• Cost-per-Click (CPC): The average click price in Google Ads. A high CPC signals commercial value and purchase intent.
• SERP features: Featured Snippets, People Also Ask, Local Pack, or AI Overviews change how much organic click is even achievable.

The critical mistake is fixation on volume. Since 91.8 percent of all search terms are long-tail with 1 to 100 searches per month (international survey) and around 93 percent of all keywords are below 10 searches per month (US database), a pure volume strategy would ignore the majority of demand. Long-tail and zero-volume keywords are valuable for three reasons: They have lower competition, more precise intent, and thus higher conversion probability. In B2B with small, specialized target groups, precisely this purchase-ready long tail is often more valuable than generic high-volume terms. A special feature applies to conversational queries: over 95 percent of conversational long-tail keywords have no measurable search volume (US database), do not appear in any tool, and are becoming more relevant precisely through AI search.

Topic Clusters and Pillar Pages Instead of Isolated Keywords

Modern keyword research thinks in topics, not individual terms. Instead of creating a separate page for each keyword, you bundle related keywords into thematic clusters and map them as a cluster structure:

• Pillar page: A comprehensive main page on the overarching topic, targeted at the central, broad keyword.
• Cluster articles: Multiple in-depth pages on specific sub-questions and long-tail keywords, each focused on a clearly defined intent.
• Internal linking: Pillar and clusters link consistently to each other. This structure signals topical depth and distributes authority purposefully.

The advantage is twofold. First, cluster logic avoids keyword cannibalization, i.e., the case where multiple own pages compete for the same keyword. Second, it builds topical authority: Search engines recognize that a website comprehensively covers a topic instead of just serving individual terms. For research, this means assigning each keyword to a cluster already during collection and thus planning the content architecture from the data itself.

Semantic Keywords, Entities, and Information Gain

Beyond the main keyword, search engines today expect content to semantically cover a topic completely. The older term LSI (Latent Semantic Indexing) is often used for this but falls short. Two more modern concepts are decisive:

• Entities: Clearly identifiable terms such as people, places, organizations, products, or concepts. Search engines understand topics through relationships between entities, not through pure word similarity. Relevant entities therefore explicitly belong in the text.
• Information gain: The unique added value that content offers compared to already ranking competition. Anyone who only repeats what already exists tenfold provides no additional information gain and becomes less visible.

Information gain is simultaneously the most important lever for visibility in AI answers. A scientific study (Princeton et al., KDD 2024) shows that targeted optimization methods can increase a source's visibility in generative search engines by up to 40 percent (international research). According to the study, the most effective methods were adding statistics, citations, and source references, which achieved a relative improvement of 30 to 40 percent (international research). For practice, this means: unique data, documented numbers, own empirical values, and clear definitions create the added value that both classic rankings and AI Overviews reward.

E-E-A-T and Content Quality as a Control Variable

Keyword research does not end with word choice. It also determines which topics a company can credibly occupy. The quality framework for this is provided by E-E-A-T: Experience, Expertise, Authoritativeness, and Trustworthiness. For sensitive Your-Money-or-Your-Life topics around finance, health, or law and in the B2B context, Google weights these signals particularly strongly.

For research, this results in a filter: Prefer keywords and topics to which your company can contribute real experience, demonstrable expertise, and data. A topic that is only superficially served, without demonstrable competence, is difficult to rank and provides little information gain. Topical authority emerges when a provider covers a narrowly defined field in depth, instead of writing broadly and shallowly about everything. Precisely this focus is a realistic competitive advantage in DACH B2B with clear niches.

AI-Supported Keyword Research and the EU AI Act

AI tools significantly accelerate keyword research and content creation. AI prompts themselves become a new keyword source: The question of what users ask ChatGPT, Perplexity, or Claude supplements classic research with conversational, often zero-volume queries. Adoption is rapid in the Austrian market. In 2024, 20.3 percent of Austrian companies with 10 or more employees used AI, up from 10.8 percent in 2023 (Austria), and 65 percent of AI-using companies employ it for text recognition and processing (Austria).

What matters is what Google rewards and what it penalizes. Google does not evaluate AI content negatively across the board, but by quality. The official guidance states: Using AI provides content no special bonus; what matters is whether it is useful, helpful, original, and meets E-E-A-T (Google). What is penalized, however, is content generated primarily to manipulate rankings, which according to Google violates spam guidelines. The biggest risk with uncritical AI use is thin and duplicate content: mass-generated texts without their own information gain. In addition, there is a legal obligation: Under Article 50 of the EU AI Act, AI-generated or manipulated content must be labeled in a machine-readable format as artificially generated from August 2, 2026 (EU, also applies to Austria). AI thus belongs in the research and creation process as a tool, not as an autopilot.

Zero-Click and SERP Feature Strategy

A growing share of searches no longer leads to a click on an external website. According to an international evaluation, in the first four months of 2026, around 68.01 percent of all Google searches ended without a click (international survey, US panel). For the EU, the same source series reports for 2024 59.7 percent zero-click searches, with 374 clicks per 1,000 searches to the open web (EU). This development is intensified by AI Overviews. An analysis of 10 million keywords shows that AI Overviews were triggered for between 6.49 percent and 24.61 percent of search queries depending on the month in 2025 (international survey) and that in January 2025 91.3 percent of these AI Overview queries were informational (international survey). Where an AI Overview appears, the organic click rate of information-oriented queries decreases according to an analysis of 3,119 search queries by 61 percent, from 1.76 to 0.61 percent (international survey, since mid-2024).

For keyword strategy, this means deliberately planning SERP features as a target. Specifically research questions that appear as Featured Snippets or in People Also Ask, and answer them precisely and structured directly in the text. Visibility in a snippet or an AI Overview is also a brand and trust signal without a click. The strategic shift goes from pure click optimization toward presence at the places where the answer is created.

DACH and Austria-Specific Keyword Research

International tools often only provide US or DE data. For the Austrian market, deliberate localization is necessary.

• Austrian German and Austriacisms: Terms deviate from the German standard. Searches are for Jänner instead of Januar, Bewerbungsschreiben with different conventions, Sackerl, Trafik, or Greißler. Anyone who only adopts DE keywords misses the local search language.
• Regional modifiers: Add location and regional references to keywords like Wien, Graz, Linz, Salzburg, or federal state names. Local B2B queries combine service and location.
• Legal and market terms: Austrian technical terms like Firmenbuch instead of Handelsregister, WKO, Gewerbeschein, or ATU-Nummer signal genuine market reference and meet the language of the B2B target group.
• B2B niche keywords: In specialized industries, search volume is small, but purchase intent is high. This is precisely where the value of the Austrian long tail lies.

Since Google dominates the Austrian market with around 82 percent market share (May 2026), these local terms can be reliably validated via Google's own sources like autocomplete and related searches with AT location settings.

Predictive SEO and Trend Research

Classic research maps existing demand. Predictive SEO supplements it with the question of which topics will generate demand tomorrow. Anyone who occupies an emerging keyword early builds authority before competition enters.

• Evaluate Google Trends: Identify rising and falling interests as well as seasonal patterns. A term with moderate volume but a clear upward curve is often more valuable than a stagnating high-volume term.
• Plan for seasonality: Many B2B topics follow annual cycles, such as around budget planning, annual reports, or regulatory deadlines. Content must be ready before the demand peak, not after.
• Use regulatory and technological triggers: New laws, standards, or technologies reliably generate new search queries. The EU AI Act with its labeling requirement from August 2, 2026 (EU) is an example of a topic whose demand is predictably growing.
• Maintain a topic roadmap: Keep emerging topics in a roadmap and assign them to your clusters, instead of only reacting to existing demand.

Common Mistakes in Keyword Research

• Optimizing only for search volume: High volumes are contested and often generic. The purchase-ready long tail remains unused, even though it represents the majority of demand.
• Ignoring search intent: Content that misses the format expected by the SERP does not rank, no matter how well written.
• Keyword cannibalization: Multiple own pages for the same keyword compete with each other and weaken each other. Cluster structure prevents this.
• Keyword stuffing: Mechanical repetition of keywords harms readability and ranking. Semantic coverage via entities replaces stuffing.
• AI mass content without added value: Generated texts without their own information gain create thin and duplicate content and fail E-E-A-T.
• Research as a one-time action: Search behavior, competition, and AI answers change continuously. A keyword list without regular maintenance becomes outdated.

Metrics and Measuring Research Success

The success of keyword research is not shown in the length of the list, but in measurable results.

• Ranking development: Track positions for prioritized keywords and clusters over time, not just for individual high-volume terms.
• Organic traffic and clicks: Google Search Console shows which queries actually bring clicks and impressions, including the long-tail queries that no tool predicts.
• Conversions from organic traffic: The decisive metric in B2B. A keyword is only valuable if it leads to inquiries or leads.
• SERP feature and AI visibility: Check whether content appears in Featured Snippets, People Also Ask, or AI Overviews. This visibility increasingly works even without a click.
• Topical authority: Measure whether entire clusters gain visibility, as an indicator that the thematic strategy is working.

Further Reading and Conclusion

Keyword research is more than collecting terms with search volume. It is the strategic translation of a target group's real search behavior into a prioritized content architecture structured by intent and topics. The most effective principles are consistent: search intent before volume, topic clusters before individual keywords, information gain before repetition, and genuine E-E-A-T competence before breadth. In the DACH region, deliberate localization to Austrian German and B2B niches is added.

With AI search, research expands to include conversational queries and visibility in AI Overviews, without invalidating the fundamentals. Sensible next steps are an inventory of existing rankings, building an initial cluster map for the most important topic, and integrating SERP and trend analysis into a recurring research process.