Glossary · Beginner
What Is Position-adjusted Visibility? Definition and Example
Position-adjusted visibility is an AI visibility metric that weights each brand mention by where it appears in an answer, so being named first counts more than being named last. Plain mention rate treats every mention equally. Position-adjusted visibility reflects the reality that buyers usually focus on the first options an AI suggests.
On this page
- How is it calculated?
- Worked example
- What are the limits?
- How should you report it?
- When is it most useful?
- From MentionX data: where brands sit in AI lists
- Worked example: Ledgerline's two numbers
- How do you define position?
- Common questions
- Related
Checked on 31 October 2026. "Our view" marks MentionX opinion. Ledgerline is a fictional company used for illustration.
How is it calculated?
There's no industry standard. One simple, transparent method:
| Position in answer | Weight (illustrative) |
|---|---|
| 1st brand mentioned | 1.0 |
| 2nd | 0.8 |
| 3rd | 0.6 |
| 4th | 0.4 |
| 5th or later | 0.2 |
| Not mentioned | 0 |
Position-adjusted visibility = sum of weights ÷ number of answers.
These weights are illustrative. Choose your own, document them and keep them fixed so results are comparable over time.
Worked example
A calculation to show the method, not real data. Across 10 answers, a brand appears 1st twice, 3rd three times and 5th once, and is absent four times.
(2 × 1.0) + (3 × 0.6) + (1 × 0.2) = 4.0 → 4.0 ÷ 10 = 0.40
Its plain mention rate is 6 out of 10, or 60%. The adjusted score shows that most mentions are not at the top.
What are the limits?
- Arbitrary weights: different weights change the score, so compare only within one method.
- Position isn't preference: a brand listed third may still be the one recommended.
- Variance: positions shift between runs; use repeat runs.
How should you report it?
Pair it with:
- Mention rate: are you present?
- Recommendation rate: are you chosen?
- Competitor share: who's ahead?
When is it most useful?
Position-adjusted visibility is most useful for list-style prompts, such as "best tools for…" or "top alternatives to…", where answers name several brands in order. For single-answer prompts, such as "what is the cheapest option?", position matters less, and recommendation rate tells you more. Many teams report the adjusted score only for comparison and category prompts, and say so in their method notes.
From MentionX data: where brands sit in AI lists
Method. Read-only aggregate queries over MentionX scans from 22 August to 29 October 2026 (UTC): 2,217 engine runs (2,154 completed) across 18 anonymised projects and six engines.
Across 102 answers where the tracked brand appeared in a list of options:
| Position in list | Share of answers |
|---|---|
| 1st | 18% |
| 2nd–3rd | 14% |
| 4th–5th | 13% |
| 6th or lower | 56% |
A plain mention rate treats all of these the same. With the example weights above, most of these mentions would score low, which is closer to how a buyer skimming a list would experience them.
Positions also move between runs. When we reran prompts, results changed for 11 of 28 prompt-engine pairs, so run each prompt several times before scoring.
Our view: position-adjusted visibility is most useful for recommendation and best-of prompts, where answers are lists. It adds little for brand-specific prompts, where the brand is usually the subject.
Limits. A convenience sample of mostly B2B software projects, run through engine APIs, which can behave differently from the consumer apps. Perplexity, Copilot and DeepSeek figures come from a single day (26 October 2026), so treat them as a snapshot, not a trend.
Worked example: Ledgerline's two numbers
Ledgerline is a fictional accounts-payable tool. Figures are illustrative.
Ledgerline's mention rate for best-of prompts stays flat at around a third of answers between two quarters. Its position-adjusted score rises, because it moves from the bottom of most lists into the top three in several. Reporting only mention rate would have hidden the improvement; reporting only the adjusted score would have hidden that it still misses two answers in three.
How do you define position?
Count the order in which brands are first named in the answer, or their rank in a numbered list. Pick one rule and apply it consistently. Mentions in a closing sentence ("other options include…") usually deserve a low weight.
Common questions
Is first position always best? Usually, but an accurate description lower down can matter more than a vague one at the top.
Related
Questions
Why adjust for position?
Readers pay more attention to the first options in a list, so a first-place mention is likely worth more than a fifth-place one.
Is there a standard formula?
No. Tools use different weightings. What matters is using the same method consistently and stating it.
Should it replace mention rate?
No. Report it alongside mention rate and recommendation rate to give a fuller picture.