How it works
Inclusion rate turns a set of patient-style questions into a measurable visibility signal. You begin with prompts that reflect how real prospects research care, such as “best med spa for acne scars in Toronto” or “where can I get natural-looking lip filler near me?” The prompt set should cover the treatments, locations, concerns, and comparison questions that matter to your clinic.
The basic process is:
- Build a fixed list of relevant prompts.
- Test them on the AI platforms you want to monitor.
- Record whether your clinic appears as a genuine recommendation.
- Divide the prompts containing your clinic by the total prompts tested.
- Repeat the test on a consistent schedule.
The counting rule matters. A clinic named in a useful shortlist may qualify. A clinic that appears only in copied directory text or an unrelated passage may not. Set the rule before testing, then apply it consistently.
AI answers can change with prompt wording, model updates, location, and time. Inclusion rate is therefore not a permanent score. It is a repeatable snapshot that becomes more useful when you compare the same prompt set across periods, platforms, treatments, and markets.
Why it matters for aesthetic clinics
Patients increasingly use AI answers to narrow their options before visiting clinic websites. If your clinic is repeatedly absent from relevant answers, it may never enter the consideration set, even when your services, reviews, and clinical experience are strong.
Inclusion rate helps you separate broad online visibility from visibility for the questions that can influence bookings. A clinic might appear often for its own name but rarely for non-branded questions such as “best treatment for sun damage” or “med spa offering Morpheus8 near me.” That gap points to a discoverability problem, not necessarily a reputation problem.
The calculation is simple. If you test 20 relevant prompts and your clinic is recommended in 6 answers, your inclusion rate for that prompt set is 30%. That figure is not a universal performance benchmark. Its value comes from comparison: this month versus last month, one treatment versus another, or your clinic versus the brands appearing most often.
The metric can also guide practical work. Low inclusion for a treatment may signal weak service pages, unclear location information, thin expert content, limited third-party validation, or inconsistent business profiles. Inclusion rate does not identify the cause by itself, but it shows where closer investigation is worthwhile.
Inclusion Rate vs Citation Share
Both metrics examine AI answers, but they answer different questions.
| Metric | What it measures | Best use |
|---|---|---|
| Inclusion rate | How often a clinic is recommended across a defined set of prompts | Tracking whether the clinic enters relevant answer sets |
| Citation share | How much of the cited-source presence belongs to a clinic or its website | Tracking whether the clinic's content is being used as supporting evidence |
A clinic can have a strong inclusion rate without earning many direct citations if AI platforms recommend it based on third-party sources. It can also earn citations without being recommended as a provider. Track both when possible, but do not treat them as interchangeable.
The Ownerized take
We treat inclusion rate as a directional business metric, not a vanity score. We segment it by treatment, patient concern, location, platform, and prompt type so the clinic can see where it is being considered and where it is missing. Then we connect those gaps to checkable work across pages, profiles, reviews, and source coverage through the AI Growth System.
Common mistakes
- Testing only branded prompts. Asking an AI about your clinic by name measures recognition after discovery. It does not show whether new patients can discover you.
- Changing the prompt set every time. New prompts can be useful, but changing the entire denominator makes period-to-period comparisons unreliable. Keep a stable core set and report experimental prompts separately.
- Counting every mention as inclusion. A passing reference, duplicate listing, or unrelated citation is not the same as a recommendation. Use a written scoring rule.
- Combining unlike searches. Treatment research, provider recommendations, safety questions, and local searches reflect different patient needs. Segment them before drawing conclusions.
- Relying on one test run. AI outputs vary. Repeated tests provide a more dependable view than a single screenshot.
- Optimizing for the score alone. More mentions are not useful if the information is inaccurate, poorly matched to the prompt, or unsupported. Review answer quality alongside inclusion.
- Ignoring what happens after inclusion. Being recommended creates an opportunity, not a booking. Your website, reviews, response process, and consultation experience still determine whether interest becomes revenue.
Frequently asked questions
What is a good inclusion rate for a med spa?
A good inclusion rate is one that improves across a stable, commercially relevant prompt set while the recommendations remain accurate. There is no universal target because results depend on the treatments, city, competition, AI platform, and counting method. Compare performance by segment and over time instead of chasing a generic percentage.
How often should a clinic measure inclusion rate?
Measure inclusion rate on a consistent schedule that matches how quickly you can act on the findings. Monthly tracking is practical for many clinics, with additional checks after major page, profile, or positioning changes. Keep the core prompts and scoring rules stable so changes in the result remain interpretable.
How is inclusion rate different from general AI visibility?
Inclusion rate is one defined measure within AI visibility. It counts how often a clinic is recommended across a specific prompt set. AI visibility is broader and can include citations, brand descriptions, factual accuracy, sentiment, source coverage, and prominence within each answer, even when no direct recommendation appears.
How can an aesthetic clinic improve its inclusion rate?
Improve inclusion rate by strengthening the evidence AI systems can find and interpret. Publish clear treatment and location pages, keep clinic profiles consistent, earn credible reviews and third-party mentions, answer real patient questions, and correct outdated information. Retest the same prompts to see whether those changes affect recommendations.
Does a higher inclusion rate mean more patient bookings?
A higher inclusion rate can place your clinic in more patient consideration sets, but it does not prove that bookings increased. Confirm business impact with website analytics, call tracking, intake questions, consultation records, and appointment outcomes. AI visibility should be evaluated alongside lead quality and revenue, not treated as the final result.
