How Marketing Miner Measures and Improves AI Visibility

Updated

Marketing Miner measures AI visibility by tracking how AI assistants such as ChatGPT, Gemini, Perplexity, AI Overviews and AI Mode mention you and your competitors, then exposing the underlying data — the fan-out queries those assistants run in the background, the websites they draw on, and the full answers they produce. It is built for e-shop and business owners, marketers, SEO specialists and agencies who want to know how visible they are in AI and search, and what to change. The AI visibility features sit alongside more than 40 SEO features in the same tool, and the MCP connection that feeds this data into ChatGPT or Claude is included in all paid plans.

What Marketing Miner actually measures

The AI visibility module answers a specific question: when an AI assistant responds to a prompt in your category, where do you appear — and where do your competitors appear instead?

The data it returns falls into three groups:

  • Fan-out queries — the background searches ChatGPT and Gemini run when composing an answer. These are the queries you would never see from a normal keyword tool, because the user never typed them.
  • Source websites — the pages the assistant actually drew on when writing its answer. This shows which domains are being cited in your place.
  • Full answers — the complete response text, not just a mention count, so you can judge tone, framing and accuracy.

On top of the raw data, the tool tracks share of mentions and compares it against competitors, and shows how that share changes over time. That time dimension matters: a single snapshot tells you where you stand, but the trend tells you whether your content work is moving anything.

Platform coverage is ChatGPT, Gemini, Perplexity, AI Overviews and AI Mode. ChatGPT ads and their monitoring are also covered.

How the measurement turns into improvement

Measurement alone does not raise visibility. The workflow only becomes useful when the data points at a concrete edit.

A practical loop looks like this:

  1. Pick a prompt or topic where you expect to be recommended.
  2. Read the fan-out queries the assistant generated. These reveal the sub-questions the model treats as relevant — often more specific than your target keyword.
  3. Check the cited sources. If competitors are cited and you are not, those pages show the format and depth the model is rewarding.
  4. Compare your mention share over time after publishing changes, rather than judging a single run.
  5. Rewrite or add content that answers the fan-out queries directly and is structured so it can be extracted as a source.

For example, if you sell running shoes and ChatGPT recommends three competitor guides but never your sizing page, the fan-out queries might show the model is searching for "wide toe box running shoes" and "running shoe sizing vs brand". Those are the gaps to fill — not the head term you were originally optimising for.

Feeding AI visibility data into your AI assistant

The MCP (Model Context Protocol) connection is the part that changes how you work day to day. Without it, when you draft content in ChatGPT or Claude, the model has no access to your real search data. With it, your assistant can pull in:

  • Search data (what people actually search for)
  • Data from Google Search Console
  • The AI visibility data described above

The stated benefit is that content, optimisation and decisions get built on real numbers instead of the model's guesses. It works in ChatGPT, Claude and Claude Code, requires no technical skills, and comes with a step-by-step guide. According to the site, it is included in all paid plans — so this is a paid-tier capability, not a free one.

Who this fits, and what to check first

Marketing Miner suits teams that already publish content and want to know whether AI assistants are picking it up. The site notes it is used by thousands of users, from the largest brands to sole traders, and that the company has been building the tool since 2015 with a team of ten people.

Before committing, verify two things on the pricing page: which plan tier you need for the AI visibility features you want, and whether the MCP connection (listed as included in all paid plans) covers your specific assistant. If your main goal is traditional rank tracking, the broader 40-plus feature set is the relevant part; if your goal is specifically AI answer visibility, the fan-out queries and cited-source data are the features to evaluate.