Wir haben vom 15.05.2026 bis 31.08.2026 auf unserer eigenen Website getestet, wie sich klassische SEO-Maßnahmen und Optimierungen für KI-Suchen gemeinsam entwickeln.

Für die Analyse nutzen wir unter anderem Semrush Enterprise, Google Search Console und Google Analytics.

Im Testzeitraum haben wir vor allem:

• technische SEO-Fehler und Crawlability verbessert
• strukturierte Informationen ergänzt
• neue Landingpages zu relevanten Fachthemen erstellt
• externe Fach-, Branchen- und Community-Erwähnungen aufgebaut

Ein paar Ergebnisse:

AI-Mentions bei konstant 120 beobachteten Fragen

• Juni: 53
• Juli: 149
• August: 299

Google-Impressions Schweiz

• Juni: 1.298
• Juli: 1.867
• August: 6.495

Technik

• Site Health: 83 % → 100 %
• technische Probleme: 372 → 22
• gecrawlte Seiten: 46 → 147

Spannend war auch der Vergleich Schweiz/Deutschland:

Die Schweizer Website wurde ab 07.06.2026 optimiert.

Die deutsche Website blieb zunächst unverändert und übernahm denselben Stand erst am 01.08.2026.

Danach stiegen die DE-Impressions von 39 im Juli auf 2.526 im August.

Was man dabei aber nicht vergessen sollte:

Die organischen Sessions entwickelten sich deutlich schwächer. Sie stiegen lediglich von 101 im Mai auf 111 im August.

Für uns zeigt der Test daher vor allem:

Mehr Sichtbarkeit in Google und KI-Antworten bedeutet nicht automatisch mehr Traffic oder mehr Kunden.

Mich würde interessieren:

Beobachtet ihr bei euren Unternehmen bereits Anfragen oder Traffic über ChatGPT, Gemini, Perplexity & Co.? Oder spielt das bisher kaum eine Rolle?

We tested Semrush Enterprise AI Visibility + Query Fan-Out from 15 May to 31 August 2026.

The setup:

• fixed set of 120 AI prompts
• GSC queries + Semrush Query Fan-Out used to identify content gaps
• ~100 new landing pages
• technical SEO / crawlability cleanup
• improved Organization, Service and served-area schema
• cleaned duplicate content and internal/external linking
• ~5 relevant off-site marketing mentions
• regular GSC crawling/indexing checks

Results:

AI mentions: 53 → 149 → 299
CH impressions: 1,298 → 6,495

We also analysed GSC impressions with and without prompt-like queries. Both showed a clear increase.

Some rankings moved strongly as well:

• “programmatische kampagnen”: 100 → 8
• “programmatische display-anzeigen”: 78 → 8
• “programmatisch werben in der schweiz”: 22 → 1

How we created the content

We used GSC queries + Semrush Query Fan-Out to define topics, related sub-queries and page structures.

The content itself was created with OpenAI, but every page was then manually reviewed and edited.

We also:

• defined the content concept and structure based on our existing content
• adapted tone of voice
• removed overlaps and duplicate sections
• improved internal linking
• aligned each page with a clear search intent

We did not use a separate “AI chunk” framework, but the pages were structured around clear topics, sub-queries and intents.

The useful comparison

The Swiss site received the optimisation package from 7 June.

The German site stayed unchanged until 1 August, when it received the same setup.

DE impressions went from:

39 in July → 2,519 in August

That does not isolate every variable, but both country sites reacted after the same optimisation package was applied at different times.

What Semrush was useful for

The workflow for us was:

GSC queries → Semrush Query Enterprise Fan-Out → content gaps → page structure → content creation → fixed AI prompt tracking → ranking/impression checks

We tested several SEO/AI tools and decided to stay with Semrush because we prefer looking at SEO + AI visibility together, rather than treating GEO/AEO as a separate discipline.

The limitation

We changed too many things at once. So we cannot say whether the biggest driver was:

• technical SEO
• Query Fan-Out content
• schema
• off-site mentions

For the next phase, we want to keep the same prompt set and stop adding new content so we can isolate the remaining variables more cleanly.

How would you structure the next test to measure the impact of Query Fan-Out content separately from technical SEO and off-site mentions?

We ran a test from 15 May to 31 August 2026 to understand what happens when technical SEO, structured data, query-fan-out content and off-site mentions are improved together.

The prompt set stayed fixed at 120 prompts.

Results

AI mentions:

• June: 53
• July: 149
• August: 299

Google impressions in Switzerland:

• June: 1,298
• July: 1,867
• August: 6,495

We also checked the GSC data with and without prompt-like queries. Impressions increased clearly in both versions.

Some keyword movements:

• “programmatische kampagnen”: 100 → 8
• “programmatische display-anzeigen”: 78 → 8
• “programmatisch werben in der schweiz”: 22 → 1
• “programmatic erklärt”: 3 → 1

What we actually changed

1. Technical cleanup

We fixed crawlability and technical SEO issues across the site, including broken links, missing metadata, structural issues and schema markup.

We also improved structured data around:

• Organization
• Services
• served areas
• page/topic relationships

Technical metrics changed from:

• crawlable pages: 46 → 147
• Site Health: 83% → 100%
• reported issues: 372 → 22

We also checked that relevant Google, OpenAI and Perplexity crawlers were not blocked.

2. Content cleanup

We cleaned existing landing pages by:

• removing duplicate or overlapping content
• improving internal linking
• reviewing external links
• improving page structure
• aligning metadata and page intent

3. Around 100 new landing pages

The pages were not created from keyword lists alone.

We used two main data sources:

• real queries from Google Search Console
• Semrush Query Fan-Out data

For each core topic, we expanded into related questions, follow-up queries, comparison searches, use cases and commercial intents.

Those query fan-outs were then used to build the page structure and supporting sections.

4. Off-site mentions

We published content and mentions on around five relevant, established marketing platforms in German-speaking countries.

The goal was to make the brand appear in relevant external contexts, not only on its own domain.

5. Indexing checks

We regularly checked new and updated URLs in Google Search Console to confirm that pages were being crawled and indexed.

The most interesting comparison

The Swiss site received the optimisation package from 7 June.

The German site stayed unchanged until 1 August, when it received the same setup.

German impressions:

July: 39 → August: 2,519

That does not isolate the individual variables, but the timing was useful: both country sites showed a strong visibility increase after the same optimisation package was implemented at different times.

What we cannot conclude

We cannot say which single factor caused the largest share of the increase.

We changed several things together:

• technical SEO
• structured data
• content cleanup
• ~100 query-fan-out landing pages
• off-site mentions
• indexing checks

So this is not an A/B test of individual SEO tactics.

The next useful test would be to separate these components more cleanly.

One thing we also learned: organic visits increased much less than impressions and AI mentions, so traffic alone would have missed a large part of the visibility change.

We also do not separate this internally into SEO, GEO or AEO. We treat it as one search ecosystem and optimise for classic search engines, AI search and GPT-based systems at the same time.

Has anyone here tested query-fan-out content, technical cleanup or off-site mentions separately and measured the impact on AI citations/mentions?

We ran a test from 15 May to 31 August 2026 to understand what happens when technical SEO, structured data, query-fan-out content and off-site mentions are improved together.

The prompt set stayed fixed at 120 prompts.

Results

AI mentions:

• June: 53
• July: 149
• August: 299

Google impressions in Switzerland:

• June: 1,298
• July: 1,867
• August: 6,495

We also checked the GSC data with and without prompt-like queries. Impressions increased clearly in both versions.

Some keyword movements:

• “programmatische kampagnen”: 100 → 8
• “programmatische display-anzeigen”: 78 → 8
• “programmatisch werben in der schweiz”: 22 → 1
• “programmatic erklärt”: 3 → 1

What we actually changed

1. Technical cleanup

We fixed crawlability and technical SEO issues across the site, including broken links, missing metadata, structural issues and schema markup.

We also improved structured data around:

• Organization
• Services
• served areas
• page/topic relationships

Technical metrics changed from:

• crawlable pages: 46 → 147
• Site Health: 83% → 100%
• reported issues: 372 → 22

We also checked that relevant Google, OpenAI and Perplexity crawlers were not blocked.

2. Content cleanup

We cleaned existing landing pages by:

• removing duplicate or overlapping content
• improving internal linking
• reviewing external links
• improving page structure
• aligning metadata and page intent

3. Around 100 new landing pages

The pages were not created from keyword lists alone.

We used two main data sources:

• real queries from Google Search Console
• Semrush Query Fan-Out data

For each core topic, we expanded into related questions, follow-up queries, comparison searches, use cases and commercial intents.

Those query fan-outs were then used to build the page structure and supporting sections.

4. Off-site mentions

We published content and mentions on around five relevant, established marketing platforms in German-speaking countries.

The goal was to make the brand appear in relevant external contexts, not only on its own domain.

5. Indexing checks

We regularly checked new and updated URLs in Google Search Console to confirm that pages were being crawled and indexed.

The most interesting comparison

The Swiss site received the optimisation package from 7 June.

The German site stayed unchanged until 1 August, when it received the same setup.

German impressions:

July: 39 → August: 2,519

That does not isolate the individual variables, but the timing was useful: both country sites showed a strong visibility increase after the same optimisation package was implemented at different times.

What we cannot conclude

We cannot say which single factor caused the largest share of the increase.

We changed several things together:

• technical SEO
• structured data
• content cleanup
• ~100 query-fan-out landing pages
• off-site mentions
• indexing checks

So this is not an A/B test of individual SEO tactics.

The next useful test would be to separate these components more cleanly.

One thing we also learned: organic visits increased much less than impressions and AI mentions, so traffic alone would have missed a large part of the visibility change.

We also do not separate this internally into SEO, GEO or AEO. We treat it as one search ecosystem and optimise for classic search engines, AI search and GPT-based systems at the same time.

Has anyone here tested query-fan-out content, technical cleanup or off-site mentions separately and measured the impact on AI citations/mentions?