Understand average position

Average position describes where your brand appears when a collected answer assigns it a position. A position of 1 is first; a lower number means an earlier position.

How Erlin calculates it

For every answer, Erlin finds where your brand appears in the list. If you appear more than once, it keeps your best (lowest) position. If an answer gives no list position but cites sources, Erlin uses the place of the first cited source from your website. Answers with no position for you are left out.

Avg. position = sum of your best position in each ranked answer ÷ number of ranked answers

Worked example: Acme has 5 answers. Its best positions are 2, 1 and 3 in three answers, it is not listed in one, and in one its site is the 2nd source cited: (2 + 1 + 3 + 2) ÷ 4 = 2.0. When no answer ranks you, the tile shows None.

Read the metric in context

Open AI Visibility > Overview and find Avg. position in Visibility over time. Select the reporting period and review the prompt scope before comparing results.

Position does not tell you how often the business appears. A brand that leads one answer but is absent from many others can still have a strong average position.

Check the answers behind the number

Open a relevant prompt in Prompt details. Review its platform, answer and sources. Compare like-for-like questions before attributing a movement to a content change.

Read the ranked answer to understand the recommendation. A numbered list may cover different use cases, so the first business is not automatically the best fit for every buyer.

Avoid misleading comparisons

Changing the prompt set, platforms or the locations prompts run from changes the evidence being summarized. Keep a record of those changes. A period with missing answers should not be treated as equivalent to a complete set of checks.

Prompt Discovery volume is an estimate of demand. It is separate from the answers used to assess visibility, and neither number is a count of website visitors.

Decide what to investigate next

Review competitor gaps and cited sources to find the specific page or question behind a change. Use Brand mention rules for recognition errors, and recommendations for possible next actions.

See How Erlin works for the measurement boundaries. A stronger metric is evidence about tracked answers, not a guaranteed business outcome.