How to measure your visibility in generative engines

The most important metric is the "inclusion rate": out of a fixed set of representative prompts in your sector, what percentage includes your brand as a source, mention or example. It's the equivalent of classic SEO's "impression share", but for generative engines.

Alongside the inclusion rate, three more metrics complete the picture: position within the answer (first mention vs marginal citation), citation accuracy (whether the model describes your brand correctly or distorts it) and coverage by engine (you show up in ChatGPT but not Perplexity, or vice versa).

The first step is to define which prompts you'll track. You need a fixed set of 30-100 questions representing real buying decisions in your sector. Mix informational ("what is X?"), comparative ("X vs Y"), recommendation ("best X for Y") and problem-solving ("how to solve Z") queries.

The trick is to think as a user, not as your marketing team. If you sell B2B CRM, don't track "midrocket CRM features": track "which CRM do you recommend for a 30-person B2B startup with long sales cycles". Conversational questions reflect real LLM behaviour.

The GEO tools market has exploded in the last 18 months. The most mature options today are Otterly, Profound, AthenaHQ and Peec AI, which automate the periodic execution of your prompts across multiple engines and deliver share-of-voice dashboards, time evolution and competitive analysis.

As a complement, Ahrefs, Semrush, Sistrix and SE Ranking already include AI Overviews tracking for specific keywords, covering the Google side of GEO. For small-scale manual tracking, running prompts by hand and storing them in a spreadsheet works perfectly at the start.

Competitive analysis in GEO is especially revealing. For every tracked prompt, log which brands are cited most often. This gives you a clear map of who dominates the conversation in your sector and where you have room to grow.

Common patterns: brands with strong Wikipedia and reference-media presence dominate broad answers; brands with very specialised answer-first content dominate technical answers; brands with proprietary data dominate comparative answers. Understanding what kind of citation each competitor dominates tells you which lever to pull to compete.

In the medium term, GEO metrics should live in the same dashboard as SEO metrics. This eases executive reading and reinforces the idea that GEO and SEO are two sides of the same goal: digital visibility. Looker Studio, Power BI or Tableau enable this integration via native connectors or exports from GEO tools.

A typical dashboard has three blocks: classic SEO (rankings, traffic, conversions), GEO (inclusion rate, share of voice, accuracy) and an executive block of combined visibility health. The last one is the easiest to read at C-level.

The recommended cadence for GEO tracking is monthly, though advanced teams run some critical prompts weekly. A higher cadence creates more noise than signal due to the natural variability of LLM responses.

Reporting must go beyond the numeric metric: include concrete citation examples (with screenshots), qualitative explanations of changes and a 30-day action plan. Without that interpretive layer, GEO dashboards end up being ignored.

Several limitations have to be assumed. LLM responses vary between runs, which introduces noise. Tool coverage across regions and languages is uneven. And closed engines (like ChatGPT) don't offer official tracking APIs, so tools rely on indirect methods that can break.

Despite these limitations, measuring GEO is radically better than not measuring. The goal is to detect trends and relative movements, not absolute certainties. With a stable prompt set and a steady cadence, the signal is solid enough to guide decisions.