LLM Optimisation
Turning LLM Tracking Data into Brand Wins
Once you’ve got your LLM tracking data in hand, the question is simple: now what? If you stop at “we measured it,” you’re leaving influence and traffic on the table. Here’s how to turn raw tracking data into brand visibility in ChatGPT, Perplexity, Gemini, and other AI search tools.
23 August 2025
10 min read
1. Diagnose Where You’re Invisible
The first pass is a reality check. Look at the queries and answers where your brand should appear but doesn’t. Spot the competitors who are getting the mentions you want.
Key questions:
- Are competitors appearing more often?
- Are your owned pages showing up at all?
- Is the model citing outdated content when fresher material exists?
Are you “top cited” but missing from final answers? (This happens more often than you think.)
Common killers of AI visibility:
- Poor H1/H2/H3 structure or missing FAQ schema.
- Content that’s too thin or generic — low semantic density.
- Weak E-E-A-T signals (expertise, experience, authority, trust).
And stale pages that haven’t been updated in over a year....
2. Focus on the Right Source Types
Your LLM tracking will show you the types of sources that actually influence AI answers. These are the ones to double down on:
> Editorial & Digital PR — News outlets, industry blogs, high-trust publications.
> Advertorials / Affiliates — Pages that LLMs regularly surface in recommendations.
> UGC & Social — Reddit threads, X/Twitter posts, Quora answers.
> E-Commerce & Retail Media — Amazon listings, Shopify product pages.
> Reference & Knowledge Graph — Wikipedia, structured databases, schema-rich content.
> Corporate Partnerships — Mentions on partner or supplier sites.
The pattern is clear: if the model sees your brand in multiple, credible places, it’s more likely to use you in its answers.
3. Fix the Technical Weak Spots
The presentation data is blunt about what LLMs like:
- Clear heading structure (H1 + sub-headings).
- Bullet lists for scannability.
- FAQ schema for quick retrieval.
And what they don’t like?
- Pages with bloated, unfocused content (“semantic cluttering”).
- Thin brand pages without proof of expertise.
- Old URLs that haven’t been touched in years.
4. Keep It Fresh and Consistent
The median LLM source age in 2025 is just seven months. If your best content is older, update it, even small changes can push it into the “fresh” bucket.
Make sure your brand story and product facts are consistent across:
- Your own site.
- PR coverage.
- Social and UGC.
- Retail listings.
Discrepancies confuse retrieval systems, lowering your chance of being cited.
5. Track → Act → Track Again
LLM visibility is not “set and forget.” Review your tracking data every month:
> How’s your share of voice trending?
> Are your changes getting you into more answers?
> Are the sources citing you the ones you actually want?
When you see movement, double down. When you don’t, change the play. The brands that win in LLM search are the ones that iterate quickly.
Bottom line: Tracking is just the start. The real gains come when you treat LLM visibility like SEO - measured, analysed, and optimised on a loop.
The first pass is a reality check. Look at the queries and answers where your brand should appear but doesn’t. Spot the competitors who are getting the mentions you want.
Key questions:
- Are competitors appearing more often?
- Are your owned pages showing up at all?
- Is the model citing outdated content when fresher material exists?
Are you “top cited” but missing from final answers? (This happens more often than you think.)
Common killers of AI visibility:
- Poor H1/H2/H3 structure or missing FAQ schema.
- Content that’s too thin or generic — low semantic density.
- Weak E-E-A-T signals (expertise, experience, authority, trust).
And stale pages that haven’t been updated in over a year....
2. Focus on the Right Source Types
Your LLM tracking will show you the types of sources that actually influence AI answers. These are the ones to double down on:
> Editorial & Digital PR — News outlets, industry blogs, high-trust publications.
> Advertorials / Affiliates — Pages that LLMs regularly surface in recommendations.
> UGC & Social — Reddit threads, X/Twitter posts, Quora answers.
> E-Commerce & Retail Media — Amazon listings, Shopify product pages.
> Reference & Knowledge Graph — Wikipedia, structured databases, schema-rich content.
> Corporate Partnerships — Mentions on partner or supplier sites.
The pattern is clear: if the model sees your brand in multiple, credible places, it’s more likely to use you in its answers.
3. Fix the Technical Weak Spots
The presentation data is blunt about what LLMs like:
- Clear heading structure (H1 + sub-headings).
- Bullet lists for scannability.
- FAQ schema for quick retrieval.
And what they don’t like?
- Pages with bloated, unfocused content (“semantic cluttering”).
- Thin brand pages without proof of expertise.
- Old URLs that haven’t been touched in years.
4. Keep It Fresh and Consistent
The median LLM source age in 2025 is just seven months. If your best content is older, update it, even small changes can push it into the “fresh” bucket.
Make sure your brand story and product facts are consistent across:
- Your own site.
- PR coverage.
- Social and UGC.
- Retail listings.
Discrepancies confuse retrieval systems, lowering your chance of being cited.
5. Track → Act → Track Again
LLM visibility is not “set and forget.” Review your tracking data every month:
> How’s your share of voice trending?
> Are your changes getting you into more answers?
> Are the sources citing you the ones you actually want?
When you see movement, double down. When you don’t, change the play. The brands that win in LLM search are the ones that iterate quickly.
Bottom line: Tracking is just the start. The real gains come when you treat LLM visibility like SEO - measured, analysed, and optimised on a loop.