What does the u201cMeasure Share of Model Over Timeu201d workflow help me complete?

Measure LLMIC Share of Model with controlled prompt sets and compatible provider runs, then interpret trends with their sample sizes.

What should I prepare before I follow the Measure Share of Model Over Time guide?

Load the relevant crawl, define the prompt or question set, and connect only the supported provider needed for the measurement. Record the provider, model, location, language, and run time so the observation can be interpreted later.

Which details should I review during Measure Share of Model Over Time?

Review the exact prompt, returned answer, mention or citation, cited URL, provider metadata, and any unsuccessful check. Keep measured provider output separate from local readiness checks and editorial recommendations.

How should I act on the result from Measure Share of Model Over Time?

Turn the observation into a review task with the affected page, captured answer evidence, intended audience need, proposed change, and owner. A missing mention is an observation from that test, not proof that the brand is invisible everywhere.

How do I verify that the Measure Share of Model Over Time workflow worked?

Repeat the same saved question set with comparable provider settings and record the new dated observation. Do not compare different prompts, models, markets, or time periods as if they were the same measurement.

What are the limits of Measure Share of Model Over Time?

A provider run is a dated observation, not a probability or ranking guarantee. Results can change by model, prompt, location, personalization, availability, and time, and a local checklist cannot prove inclusion in an AI answer.

What should I do after completing the Measure Share of Model Over Time workflow?

Save or export the evidence, assign the approved action, and use the related guides linked on the page for the next check. After implementation, repeat the same workflow against a fresh crawl so the result is comparable.

Measure Share of Model Over Time

Share of Model summarizes how often defined brands appear in a controlled set of successful AI answers. The denominator matters as much as the percentage.

Build availability: This guide describes the September 2026 development workflow. Some controls may not yet be included in the public installer. Check the release notes and your app version before following the steps.

Evidence flow

Save the question setVersion prompts, audiences and target brands.
Run controlled samplesKeep provider settings and repeats fixed.
Exclude failed answersUse only eligible successful observations.
Compare trendsMatch question versions and returned models.

This visual explains the review sequence. It is not a performance forecast or a set of measured customer results.

Start with a clear question

Open the relevant LLMIC dashboard only after loading the crawl, property or saved observation needed for this review. For share of voice, record the website, date, filters and document layer before interpreting a result. This keeps the work reproducible when another team member reviews it later.

Read the evidence before the recommendation

Open the affected page or row and inspect the retained evidence. Counts help you find patterns, but the page-level source explains what was actually measured. When coverage is partial, a response failed, or a legacy crawl omitted the required field, keep that state visible. Do not turn unavailable evidence into a failed check.

Choose the smallest useful action

Write an instruction that names the exact URL, observed condition and intended result. Preserve intentional behavior and review shared patterns before applying a site-wide change. If a suggestion creates new wording, markup or redirects, a person should approve the facts and destination before publication.

Understand the limits

Share of Model is not the same as web impressions, market share or revenue. Do not add AI observations to Search Console impressions or claim statistical significance without an appropriate study.

Quick reference

Evidence or state	How to use it
Share value	Read it with eligible answer count and question coverage.
Trend change	Check whether prompt or provider settings changed.
Small sample	Treat movement as descriptive, not significant.
Unresolved brand	Review entity matching before counting it.

Check the result

After the reviewed change is deployed, collect fresh evidence with a compatible configuration. A task marked complete records workflow progress; it does not prove the live page changed. Save the verification time and result, then use Verify SEO Fixes for supported checks or run a complete follow-up audit.

Continue your workflow

Return to the documentation home, learn how to interpret audit results, or organize reviewed work in the Action Queue and Fix Generator. Remove private information before sharing exports, and contact LLMIC support with a sanitized example when a result cannot be explained.