How to Run a GEO Competitor Gap Analysis in 60 Minutes

Most teams are measuring the wrong thing

A lot of GEO and AEO reporting is built around one question: did our brand appear?

That is useful, but it is not enough.

If ChatGPT mentions you on 6 prompts, that number means very little without context. Maybe your closest competitor shows up on 19. Maybe Perplexity cites their comparison page while Gemini cites an industry directory. Maybe Google AI Overviews ignores both brands and leans on review sites instead.

That is why the better workflow is a competitor gap analysis.

Instead of measuring your AI visibility in a vacuum, you compare your brand against the 2 to 4 competitors that keep showing up in the buyer journey. You look at prompt coverage, recommendation presence, citation share, cited domains, and page types by platform. Then you turn the gaps into a short list of fixes.

If you are new to the category, start with the foundations in our guide to Generative Engine Optimization. If you already have basic tracking in place, this is the next step.

What a GEO competitor gap analysis should measure

A useful gap analysis does not stop at brand mentions. It compares five things:

1. •Prompt coverage
   • •On how many high-intent prompts does your brand appear at all?
2. •Recommendation presence
   • •On how many prompts does the model actively suggest your brand, not just mention it?
3. •Citation share
   • •When answers include sources, how often do your URLs or third-party pages about your brand get cited?
4. •Source mix
   • •Are platforms relying on your site, review sites, Reddit, LinkedIn, directories, or earned media?
5. •Page-type pattern
   • •Which assets actually win: comparison pages, category pages, FAQs, documentation, review pages, or founder content?

That last piece matters more than most teams think. In AI retrieval, passages often beat whole pages, which means structure and answer formatting can change whether you get cited at all. We covered that in Passages Beat Pages.

The 60-minute workflow

You do not need a six-week research project to get signal. You need a disciplined one-hour pass.

Step 1: Build a 15 to 20 prompt set

Do not start with thousands of prompts. Start with the prompts closest to revenue.

Use the same logic we recommend in our guide on how to select prompts for LLM tracking:

• •high-intent category questions
• •comparison prompts
• •use-case prompts
• •problem-aware questions
• •brand-vs-brand prompts
• •review and trust prompts

For most teams, 15 to 20 prompts is enough for a first pass.

A simple mix looks like this:

• •5 category prompts
• •4 comparison prompts
• •4 use-case prompts
• •3 trust or review prompts
• •2 implementation prompts

If your company sells into multiple ICPs, build one set per ICP. Do not mash them together.

Step 2: Run the same prompts across the main AI surfaces

At minimum, check:

• •ChatGPT
• •Perplexity
• •Gemini
• •Google AI Overviews for the equivalent search query

If Bing matters in your market, add Copilot and use the new reporting inside Bing Webmaster Tools' AI citation data where possible.

The point is not to collect perfect data. The point is to compare like for like. Same prompt, same time window, same competitors, same scoring system.

Step 3: Log the four outcomes that matter

Create a sheet with one row per prompt-platform combination.

Use these fields:

• •Prompt: the exact question used
• •Platform: ChatGPT, Perplexity, Gemini, or Google AI Overviews
• •Our brand present?: yes or no
• •Competitor present?: which competitors appeared
• •Recommended?: yes or no
• •Cited URLs: exact URLs shown in the answer
• •Source domains: domain-level view of the citations
• •Winning page type: comparison page, directory, review, blog post, docs, forum, and so on
• •Notes: important wording, answer framing, or objections surfaced

Do not overcomplicate the first pass. You need decision-making data, not a lab experiment.

Step 4: Pull out the cited domains and page types

This is where most teams miss the real insight.

If a competitor wins, ask what asset actually won.

Not just the domain. The asset.

Examples:

• •a third-party review page
• •a vendor comparison article
• •a Reddit thread
• •a LinkedIn post from the founder or category expert
• •a product category page with clear specs
• •an FAQ block answering a narrow buyer question

This is often where the pattern snaps into focus. Your brand may not be losing because your product is weaker. You may be losing because you do not have the asset type the model keeps retrieving.

Step 5: Classify each gap before you start writing content

Once you have 15 to 20 prompts scored, classify the gaps into one of five buckets.

The five gap patterns that matter most

1. Source gap

Your competitor is present because AI platforms trust external sources that mention them more often than they mention you.

Signals:

• •review sites cite competitors more often
• •comparison articles mention competitors by name
• •industry lists exclude your brand
• •LinkedIn or Reddit discussions keep surfacing competitor examples

This is usually not a pure on-site problem. It is an authority and distribution problem.

2. Page-type gap

You have relevant content, but not in the form that the platform wants to cite.

Signals:

• •competitors win with pricing pages, comparison pages, or FAQ sections
• •your site has thought leadership, but not decision-stage assets
• •your content buries answers inside long pages with weak headings

If you have not read it yet, pair this with our breakdown of how AI platforms choose which sources to cite.

3. Entity gap

The platform understands competitors as established category entities, but your brand is weakly connected to the category or use case.

Signals:

• •competitor names appear unprompted in broad category queries
• •your brand only appears when named directly
• •third-party pages describe competitor strengths more clearly than yours

This usually means your category positioning is not reinforced enough across the web.

4. Intent gap

You are visible on informational prompts, but absent on commercial or implementation prompts.

Signals:

• •your blog posts show up on educational questions
• •competitors dominate comparison, migration, integration, pricing, or trust prompts
• •recommendation prompts consistently favor other brands

This is one of the clearest signs that your content program is not aligned to buyer-stage coverage.

5. Platform gap

Your visibility changes dramatically by platform.

Signals:

• •ChatGPT cites one set of sources
• •Perplexity leans on another
• •Google AI Overviews prefers different page types again

That is normal. Platform behavior is different by design, and citation sets drift over time. We covered that in Citation Drift.

How to prioritize fixes without wasting a sprint

Once the gaps are visible, score them.

Use a simple 1 to 5 scale across these four factors:

1. •Commercial intent
   • •How close is the prompt to pipeline or revenue?
2. •Competitor dominance
   • •How badly are you losing this prompt today?
3. •Execution speed
   • •How quickly can you create or improve the winning asset?
4. •Repeatability
   • •Will fixing this gap help across multiple prompts or just one?

A gap scoring 17 out of 20 should move before a gap scoring 9.

That sounds obvious, but many teams still chase the most interesting prompt instead of the most valuable one.

What to build after the analysis

The output of the audit should be a short action list, not a giant deck.

Typical fixes include:

• •create a comparison page for the competitor prompts you keep losing
• •add clear answer blocks for recurring buyer questions
• •tighten headings so answers can be retrieved at passage level
• •publish implementation pages for migration, onboarding, or integration questions
• •strengthen review and proof assets if trust prompts are weak
• •increase third-party coverage if external sources do not mention you enough

If B2B buyers keep encountering competitors through expert profiles or executive commentary, LinkedIn can matter more than your team expects. We wrote about that in LinkedIn Is the Second Most Cited Domain in AI Search.

The key is to respond to the winning asset type, not just the winning keyword.

The weekly scorecard your team should keep

After the first audit, keep a lightweight scorecard and update it weekly.

Track:

• •prompt coverage percentage
• •recommendation rate
• •citation share by platform
• •number of unique cited URLs for your brand
• •number of unique cited third-party URLs about your brand
• •top competitor wins by prompt cluster
• •major source changes week over week

This gives you a much cleaner operating rhythm than random spot checks.

Weekly monitoring is enough to catch movement. Monthly review is enough to spot patterns. Daily panic-checking is not a strategy.

Common mistakes that make the audit useless

Treating every mention like a win

A passing mention is not the same as a recommendation. Score those separately.

Mixing prompt intent levels

If you mix category, educational, trust, and implementation prompts without labeling them, the results become noisy fast.

Ignoring third-party assets

Many competitors win through pages they do not own. If you only look at your own site versus theirs, you miss the real source battle.

Running the audit once and calling it strategy

AI visibility changes. Refresh the audit weekly for tracking and monthly for deeper action planning.

Writing content before diagnosing the gap type

If the problem is a source gap, publishing another blog post may do nothing. Diagnose first. Build second.

FAQ

How many competitors should I include in a GEO competitor gap analysis?

Start with two to four real competitors. More than that usually creates noise in the first pass. You want enough comparison to find patterns, not a giant spreadsheet that nobody uses.

Which platforms matter most for this audit?

For most B2B teams, start with ChatGPT, Perplexity, Gemini, and Google AI Overviews. Add Copilot or other surfaces if they matter to your audience or your traffic mix.

Does this replace traditional SEO competitor analysis?

No. Traditional SEO analysis still matters for rankings, links, and page performance. GEO competitor analysis answers a different question: which brands and sources AI systems surface when buyers ask for help.

What if competitors are being cited from third-party pages, not their own site?

That is still competitor advantage. Treat it as a source gap. You may need better earned media, review coverage, expert commentary, or directory visibility, not just better on-site content.

The bottom line

A good GEO competitor gap analysis gives you something most teams still lack: context.

Not just whether your brand appeared. Not just whether one prompt went well. Real context on who is winning, where they are winning, which assets are doing the work, and what you should fix next.

Run the first pass in 60 minutes. You will leave with a clearer action plan than most teams get from months of abstract AI visibility discussion.

And if you want help turning the findings into a GEO roadmap, book a strategy call.