Free · One and a half pages
There is no page two in an AI answer
Twenty real buyer questions in your category, run through ChatGPT, Perplexity, Google AI Overviews and Gemini, scored on who gets cited. A page and a half. Free. It will annoy you.
Your buyers have started asking instead of searching.
When they search, they get ten links and a long tail, and rankings three through ten are still a business. When they ask, they get one or two named sources and everything else gets nothing.
Which means the winner is not whoever publishes the most. It is whoever the model has decided is the authority on a specific question. That is an ownable position, it is decided question by question, and right now it is cheap.
In eighteen months it will not be.
What is in the report
- 01
Twenty questions
Real ones your buyers ask — not keywords. We draft them from your category and you can swap any of them before we run it.
- 02
Four models
ChatGPT, Perplexity, Google AI Overviews, Gemini. Same questions, same day.
- 03
Citation share
Who gets named, how often, and on which questions — including the competitors you did not expect to see.
- 04
The gap list
The questions nobody currently owns in your category, ranked by how buyable they are and how close you already are.
The companies winning this are not publishing more. They pick a narrow set of questions they intend to own, then answer them better and more structurally than anyone else. Category design, applied to a machine reader.
How it works
We draft twenty questions
Real buyer questions from your category. You see them first and swap any that miss.
Four models, same day
The same twenty questions through ChatGPT, Perplexity, Google AI Overviews and Gemini.
Scorecard + gap list
Who gets cited, how often, and the ownable questions nobody holds yet.
Why proprietary data wins this
If you own data, research or an audience that nobody else has, you have the one thing a language model cannot synthesise from everyone else's content — and the one thing it will cite by name.
Most companies sitting on proprietary data publish it as a gated PDF, which is invisible to every model on this list. That is usually the single highest-return change we find.
Who is doing this
The Second Click is a marketing operations firm in York, Pennsylvania. We built 2ndClick, a platform for tracking AI search visibility and multi-model attribution, because the tooling to answer this question did not exist and our clients kept asking it.
This report is run on that platform. There is no software purchase attached to receiving it.
Run it on my category
Scorecard back within three business days. You will see the questions before we run them.
Questions
Is this SEO?
No, and treating it as SEO is why most companies are losing it. Ranking and being cited are different mechanics with different inputs.
How is this different from a rank tracker?
A rank tracker tells you where a page sits. This tells you whether a model names you when a buyer asks a question, which is increasingly the only moment that exists.
Do the results change day to day?
Somewhat. That is why the report records the date and the exact prompt, and why we rerun the same twenty questions if you want a trend line later.
Twenty questions. Four models. One uncomfortable page.
Send me my category's citation scorecard