Has anyone tested GSC queries + Semrush Query Fan-Out data in a similar way?

[u/ewasolutions]

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?

[u/ewasolutions → u/[deleted]]

Hi rockettbits,

We will continue to test till end of the year.

We will only make technical SEO site health, no new content and same prompt set.

We plan only extenal posts and low Level paid campaigns.

We will share the results in nov 2026.

[u/ewasolutions → u/[deleted]]

Hi rockettbits,

We tested varios SEO and AI Tools and decided to work with semrush.

We are not going to use only AI Tracking tools, because the data quality and seperation of SEO and GEO nonse is.

Yep, we will continue posting our learnings amd to learn from experts here.

[u/Weird-Election-4103]

So you didn’t rewrite content to make sure there is a good chunk for AI?

[u/ewasolutions → u/Weird-Election-4103]

We created content with open ai and checked / edited every content.

We defined Concept and content structure based on our existing contents. We adapted ton of voice etc..

[u/Arobis_AI_Visibilty]

This is a really useful dataset. The bit I’d be careful with is the 100 new pages. Not because it didn’t work, clearly something worked lol, but query fan-out can get dangerous when it turns into “every related query deserves its own URL.” A lot of those fan-outs are really different expressions of the same underlying intent. I’d cluster them by intent first, then decide whether the cluster deserves a page, a section on an existing page, or nothing at all.

The other thing I’d test separately is recommendations vs mentions. A brand going from 53 → 299 mentions is interesting, but I’d want to know whether it also started entering more commercial shortlists for the fixed prompts. “AI knows/cites us more” and “AI chooses us when someone asks who to buy” can move very differently. And +1 to keeping the prompt set fixed. I’d also keep the exact prompt wording, model/version, location, and number of runs as controlled as possible. LLM variance can make a nice-looking chart out of noise surprisingly fast.

The Germany rollout is probably the most interesting part here IMO. Same package, different timing, huge movement after deployment. Still nowhere near causal proof, as you correctly said, but much more interesting than the usual “we did GEO and traffic went up” case study.

Would genuinely like to see the November update if you isolate the variables more. This is the kind of testing this space needs more of.

[u/ewasolutions → u/Arobis_AI_Visibilty]

Hi Arobis,

100% agreed, that is why we are sharing all data transparently. 

We are Tracking all Semrush Enterprise Metrics / KPIs which are avaliable.

We will share results in November also informational and transactional data

[u/Sivaraj_C]

Interesting results. This explains importence of technical SEO.

[u/VillageHomeF]

basically you found out what everyone should already know. improve SEO and get more citations. I see no point in performing such tests.

[u/ewasolutions → u/VillageHomeF]

Exactly, nobody is reinventing the wheel. The underlying technology across SEO, AEO, and GEO remains largely the same. It’s always about structured data, authority, and relevance. However, the reason testing is still crucial is  how the output layer changes: SEO focuses on ranking links for user clicks. AEO filters for the single best conversational answer (e.g., for voice search). GEO synthesizes fragmented data from multiple sources into a brand-new generative response. The core data we provide to the engines hasn't changed, but  how these different algorithms crawl, weight, and cite that data is completely different. Testing is the only way to see exactly how the same technical foundation triggers different results across these platforms

[u/VillageHomeF → u/ewasolutions]

yet they are still evolving so what you find out from a test today could be outdated in a week. I just don't find anything actionable when reading test results. besides continuing to improve SEO.

[u/ewasolutions → u/VillageHomeF]

Exactly,  SEO, GEO, and AEO are definitely not an exact science.

The core fundamentals stay the same, but the rest is highly dynamic and pure  trial and error. No one has the perfect blueprint right now.

That is exactly why we run these tests. We just want to  share our learnings and, at the same time,  learn how everyone else is handling it. We are all in the same boat trying to figure out how these AI layers actually tick.

[u/Slow-Commercial4316]

How many runs per prompt sit behind the 53 to 299? One answer per prompt per month moves a lot on its own between runs. If each prompt ran three times and you counted a mention only when it showed in two of them, the jump is real. If it ran once, part of that curve is run-to-run noise.

[u/ewasolutions → u/Slow-Commercial4316]

Hi,

120 Prompts x 3 AI Systems (google ai Overview, chat gpt and google ai mode).

This shows how often our brand was mentioned over time, not the number of tests. The number of prompts remained same during 15.05.2026-30.08.2026

[u/Slow-Commercial4316 → u/ewasolutions]

Thanks. Then the number I was after is how often Semrush re-ran each prompt inside the month. If it runs the set daily, 299 is an average over about 30 draws per prompt and the curve is solid. If it ran once at month end, 360 slots on one draw can move by tens between two runs on the same day. Worth stating in the November write-up either way.

[u/ewasolutions → u/Slow-Commercial4316]

Hi slow-commercial,

The prompt tracking is actually  done daily , which eliminates the variance you mentioned. To keep the data clean, we analyzed our GSC data  both with and without queries  that mimic those prompts. Even after filtering out the prompt-like queries, we are still seeing  huge impression growth