How do you improve Share of Model?

Find the buyer questions where competitors are named and you are not, read which sources the model cited instead, earn or correct coverage on those specific sources, fix inaccurate descriptions of your company wherever models read them, publish comparison-shaped evidence others can cite, and re-run the same prompt set on a fixed cadence to confirm the change.

How long before Share of Model moves?

Retrieval-driven gains — new or corrected sources a model can read live — typically show in days to a few weeks. Gains in a model's trained knowledge arrive on refresh cycles measured in months. Judge trend over a quarter, not week to week.

What is the highest-leverage first move?

Correcting and expanding your presence on the specific third-party domains models already cite in your category. That list is short, it is measurable, and it changes answers faster than any amount of new owned content.

Does publishing more content raise Share of Model?

Only when the content gives independent sources something concrete to cite — original data, a clear methodology, explicit pricing, honest comparisons. Volume alone does not, because vendor-owned pages make up a very small share of what models cite.

How do you prioritize which gaps to fix?

By deal proximity, not by volume. A decision-stage question asked by fewer buyers is worth more than a high-volume definitional question, because the answer is closer to a purchase and usually names fewer vendors.

How do you know a change worked?

By holding the instrument still: same prompt set, same models, same number of runs, and enough samples per prompt that run-to-run variance averages out. Then attribute movement to the source or fix that shipped in that window.

Method

How to improve your Share of Model

Six steps, in order. The first three are diagnosis, the next two are the work, and the last is the only thing that proves any of it happened.

Updated: August 2026

The six steps

1. Identify recommendation gaps

Run your buyer-question set and list every answer where a competitor is named and you are not. Tag each by stage. This list — not a keyword report — is the backlog.

2. Identify competitor advantages

For each gap, open the sources the model cited. You are looking for the specific claim that earned the mention: a benchmark, a review score, a comparison table, a thread of real users.

3. Find influential sources

Aggregate the cited domains across your whole prompt set. A small number of domains decides most category answers. Rank them by citation frequency and by whether they currently mention you at all.

4. Fix narrative gaps

Correct wrong category placement, stale specs, outdated pricing, and missing differentiators everywhere a model reads them — review profiles, directories, reference entries, docs, and your own site. Use the same category language in all of them.

5. Build supporting content

Publish material an independent source can cite and a model can lift without hedging: original data, explicit methodology, honest comparisons, named limitations. Comparison-shaped formats get reused most.

6. Monitor changes

Re-run the same prompt set on the same cadence, with several runs per prompt, and attribute movement to what shipped. Without a stable baseline you cannot separate a win from variance.

Where to aim, by stage

The source types that decide an answer shift as the buyer moves. Aim step 3 at the mix that matches the stage you are losing.

Discovery

Analyst / research40.4%
Social21.6%
Editorial & press15.8%
Review sites12.0%
Community (Reddit, forums)2.9%

Evaluation

Editorial & press34.0%
Analyst / research19.4%
Review sites19.0%
Social13.6%
Unclassified4.3%

Decision

Review sites43.1%
Analyst / research19.6%
Editorial & press16.4%
Social10.9%
Community (Reddit, forums)5.1%

Domains cited most often in observed answers

01medium.com5,454
02gartner.com4,113
03linkedin.com3,646
04g2.com3,036
05forrester.com2,200
06capterra.com1,474
07youtube.com793
08trustpilot.com749
09reddit.com730
10clutch.co459

A realistic 90-day shape

Weeks 1–2: fix the prompt set, establish the baseline, build the cited-domain list.
Weeks 3–6: entity and accuracy cleanup on the sources models already read. Cheapest wins live here.
Weeks 5–10: earn coverage and comparisons on the top cited domains; publish the evidence they need.
Weeks 6–12: weekly re-measurement, attribute movement, drop what did not move.

Get your baseline free The 30-day playbook

Frequently asked questions

How do you improve Share of Model?: Find the buyer questions where competitors are named and you are not, read which sources the model cited instead, earn or correct coverage on those specific sources, fix inaccurate descriptions of your company wherever models read them, publish comparison-shaped evidence others can cite, and re-run the same prompt set on a fixed cadence to confirm the change.
How long before Share of Model moves?: Retrieval-driven gains — new or corrected sources a model can read live — typically show in days to a few weeks. Gains in a model's trained knowledge arrive on refresh cycles measured in months. Judge trend over a quarter, not week to week.
What is the highest-leverage first move?: Correcting and expanding your presence on the specific third-party domains models already cite in your category. That list is short, it is measurable, and it changes answers faster than any amount of new owned content.
Does publishing more content raise Share of Model?: Only when the content gives independent sources something concrete to cite — original data, a clear methodology, explicit pricing, honest comparisons. Volume alone does not, because vendor-owned pages make up a very small share of what models cite.
How do you prioritize which gaps to fix?: By deal proximity, not by volume. A decision-stage question asked by fewer buyers is worth more than a high-volume definitional question, because the answer is closer to a purchase and usually names fewer vendors.
How do you know a change worked?: By holding the instrument still: same prompt set, same models, same number of runs, and enough samples per prompt that run-to-run variance averages out. Then attribute movement to the source or fix that shipped in that window.