How to Track Your Practice's AI Visibility by Market

Step 1 — write the questions your prospects actually ask #

Avoid generic prompts like "best clinic Hong Kong." Real buyer questions are more specific and closer to how people actually type:

• "Best aesthetic clinic in [district] for [treatment]?"
• "Is [your practice name] trustworthy? What do reviews say?"
• "[Your practice] vs [named competitor] — which is better for [service]?"
• "How much does [service] cost in Hong Kong?"
• "Do I need a lawyer for [scenario]?" (for legal practices)

Fifteen to twenty questions, covering discovery ("who's best"), trust ("tell me about X"), price, and comparison, is enough for a first pass.

Step 2 — run each question across the assistants your market can reach #

Test the same question across the assistants your market can reach — ChatGPT, Gemini and Perplexity in most overseas and inbound markets, or Gemini, Perplexity, DeepSeek and Qwen in Hong Kong — and, if your clients search in more than one language, run the same set again in Chinese. It is common to find a practice performs very differently by market and by language; this gap is frequently the single most valuable finding.

Keep the two surfaces separate from the start. A Hong Kong practice should never average ChatGPT and Claude results into a local score, because neither assistant can be opened by a local customer. The Hong Kong surface is Gemini, Perplexity, DeepSeek and Qwen. The overseas and inbound surface is ChatGPT and Claude. Each is a different audience with different sources and different answers.

Step 3 — log four things per answer #

Field	What to record
Mentioned?	Yes / no
Position	Named first, named among others, or not named
Sentiment	Positive, neutral, or cautionary
Accuracy	Any fact stated about you that is wrong or outdated

A simple spreadsheet is sufficient for a first pass. The pattern that matters is not any single answer, but the aggregate: what fraction of your 15–20 questions name you, and who is named instead. Run the same set in each market and each language, and keep the columns separate so the results never mix.

Step 4 — repeat monthly #

Model providers update retrieval and generation behaviour without changelog. A result from today is a snapshot, not a permanent state. Running the same question set on a fixed cadence — monthly is a reasonable default — is what turns a one-off curiosity into a usable trend line, and is what any credible AI-visibility engagement should be reporting back to you.

What this manual method won't tell you #

It surfaces the pattern; it does not tell you why a competitor is named instead of you, or which specific web sources the model is drawing from. That requires examining the citations an assistant provides (where available) and cross-referencing which directories, reviews, and articles a competitor appears in that you do not — the "citation neighbourhood" that actually drives the answer. That deeper analysis is the basis of a full audit.