How Botfusions Measures AI Visibility — Methodology
This page exists to make our AI Visibility (GEO) measurement transparent and auditable. Instead of fabricated expert quotes, awards, or scores, you get: the real methodology, cited public research, and honest limitations.
Last updated: · Owner: Ömer Cenk Tokgöz, Founder, Botfusions
How is the AI visibility score calculated?
The AI Visibility Score is computed from the answers of the AI engines that actually ran in a scan; our measurement stack today covers ChatGPT, Gemini and Perplexity, and the engine set is stated in every report. Four signals are weighted: Mention 40%, Position 30%, Recommendation 20%, and Citation 10%. The measurement frame and its limits are stated on this page; a score is comparable only against another measurement taken with the same engine set and the same depth.
The Botfusions Visibility Matrix: Per-Engine Measurement
The AI Visibility score is derived from a public, reproducible benchmark. The numbers below come from our scan records — no new statistic is invented.
Each monitored query generates measurements per engine and per persona cohort. Our measurement provider's full scan collects 200–300 answers; at that sample size the noise margin is roughly ±4–5 points per engine. The current engine set is ChatGPT, Gemini and Perplexity. If an engine fails to answer part of the questions, the scan counts as a “partial measurement” and its numbers are not published. That is why each engine's own noise floor is reported alongside it; a difference smaller than that band is not treated as meaningful.
The Weighted AI Visibility Score
Measurements are reduced into four weighted signals. AI Visibility Score = Mention (40%) + Position (30%) + Recommendation (20%) + Citation (10%).
Whether the brand appears in the generated answer at all. Highest weight because you cannot be chosen if you are absent from the answer.
Where the brand sits within the answer. Attention drops sharply as readers move down; a mention in the opening lines is worth the most.
Whether the brand is recommended, listed neutrally, or mentioned with caveats. A neutral-heavy mix means the engine knows you but will not recommend you.
Whether the engine includes a direct link to the brand's domain. Not every engine links out (ChatGPT cites in a minority of standard responses), but when present it is the strongest direct-traffic proxy.
What Public Research Shows
The findings below are NOT Botfusions's invention. They belong to published GEO research and are reproducible in the literature. We cite them to explain why our own methodology rests on these signals.
Two foundational academic inputs frame what good AI visibility looks like. The first is the KDD 2024 Princeton study that established the GEO-bench framework and quantified how specific content changes lift citation rate — “GEO: Generative Engine Optimization”. The relative improvements are public, peer-reviewed, and reproducible:
| Optimization method | Tactic | Visibility lift |
|---|---|---|
| Cite sources | Add explicit inline references to assertions | +115.1% |
| Statistics addition | Replace qualitative sentences with precise data | +40.0% |
| Quotation addition | Integrate attributable expert quotes | Significant |
| Fluency optimization | Improve grammatical structure and flow | +28.0% |
| Combined methods | Merge multiple tactics (over any single method) | +5.5% |
The study's structural insight is decisive: traditional keyword-density tactics perform poorly in generative search. Engines reward semantic authority, structured data, and dense factual content. The same study found a measured reduction from keyword stuffing — meaning stuffing hurts rather than helps visibility.
Botfusions's Own Benchmark Claims
The figures below are Botfusions's own measurements (not the external research above) and describe our own brand visibility — the unflattering parts included:
- ●Our own brand was mentioned in 11.2% of ChatGPT answers in the 15 September 2026 scan (249 answers, ±3.9 points). On 6 October 2026 we withdrew the Gemini and Google AI Overview 0% results we had published here, and the numbers of the 5 October 2026 scan: our measurement provider reported that the Gemini/AI Overview zeros were a measurement error and that the 5 October scan was only partially measured. This page does not hide withdrawn numbers either.
- ●The engine set varies by scan and is recorded with every scan; our measurement package today covers ChatGPT, Gemini and Perplexity. Google AI Overview is in the package definition but our provider does not measure it at the moment; Claude and Grok will be added as a paid add-on; the published Turkish Translation Agency (formerly Alafranga) case was measured on Claude and Gemini. Copilot and DeepSeek have never run — our rate there is not "0%", it is unmeasured.
How We Research and Keep Content Current
Research
Every material claim is backed by public academic research and primary sources (e.g. the GEO-bench, the KDD '24 paper). Estimates are clearly labeled as 'lab' or 'empirical'.
Sourcing
Statistics are presented with citations to public sources. External research is never presented as Botfusions's invention. No fabricated case studies or customer quotes are used.
Freshness
Content is updated when a genuine edit is made; otherwise it stays equal to the publish date (honest: 'unchanged since publication').
Limitations & Honesty Note
AI outputs are non-deterministic. The same query can surface different brands depending on context, location, and conversation history. For that reason the AI Visibility Score is an estimate — not a deterministic rank — and we use persona cohorts and repeated measurement to reduce volatility, though we cannot eliminate it entirely.
The percentage improvements we cite from public research (e.g. +115.1% cite sources) were measured under the original study's conditions and may not replicate identically for every brand or vertical. Every 'Botfusions' figure on this page belongs to our own benchmark; every external figure belongs to published research — we never conflate the two.
This transparency is itself an E-E-A-T trust signal: we state plainly what we can and cannot measure.
Next Steps
Want to put this methodology to work? Run the free readiness audit or read the case studies.