Why it is useful to measure
Perplexity shows its citations openly. That makes it the easiest of the three counted surfaces to learn from: you can see which sources it drew on, which tells you where the answer about your category is actually being written.
For diagnosis that is worth more than the score itself. A Perplexity answer that cites four directories and no business websites tells you exactly where the work has to happen.
It moves independently
In our own measurement, Perplexity has moved while the other two surfaces held flat, sometimes because a comparison page or directory entered or left its source set, not because anything about the business changed.
This is the argument for per-surface reporting and for keeping raw responses. A blended score would have shown a change with no way to tell whether it was your position, a competitor's gain, or a shift in what the engine consulted. Those three call for completely different responses.
What to do about it
Work the sources it cites. Because it discloses them, the target list is unusually legible, and correcting how you are represented on those sources is more direct than any on-site change.
Read in order, or jump
Twelve chapters, in the order the work has to happen. The first four are the spine; the platform chapters at the end include two surfaces we deliberately do not score, and say why.
01 How AI search picks local businesses 02 Eligibility: can AI even see your site? 03 Robots.txt: block AI crawlers or allow them? 04 Measuring AI visibility honestly 05 Entity foundations for local businesses 06 Reviews as AI input 07 Citation surfaces: where AI engines look 08 Getting ChatGPT to recommend your business 09 Showing up in Perplexity 10 Showing up in Google AI Overviews 11 Showing up in Gemini 12 Copilot, and why we don't sample itSee where you actually stand
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