For months, you've seen GEO come across your LinkedIn timeline, crawl its way into your newsletter diet, or you’ve received “figure out GEO” as a directive from your leadership.
It’s clear that GEO is not a trend that we can expect to pass. It’s a must-do for maintaining brand visibility as website visits fluctuate.
As someone that’s constantly looking at GEO performance data– let me help you move beyond instinct.
The good news is if you haven’t started thinking about how to tweak your visibility strategy to reflect the changes in GEO, you're not as far behind as you may think.
Yes, GEO is fairly new, but there is already data brands can leverage to make more informed, strategic decisions about whether and how their PR and content strategies need to change.
That’s where Stacker’s audit framework comes in. This framework helps you establish your baseline and identify the specific signals that determine your brand presence in AI search. We provide four concrete signals to measure, the tools to measure them, and a way to interpret your findings, so you know exactly where to focus.
Signal 1: Understanding your publisher trust footprint
Even if you haven't intentionally optimized for AI search, you already have a baseline worth measuring (and likely more gaps than you realize). And just like you wouldn't overhaul an SEO strategy without a site audit first, GEO requires the same diagnostic discipline before you make any changes.
A publisher trust footprint measures the breadth and quality of third-party sources that mention, cite, or reference your brand in their content. LLMs learn what's credible by seeing what content authoritative sources repeatedly reference.
In practice, AI-generated citations are when you ask your AI “what's the best project management software?” and it responds with “tools like Asana and Monday.com are frequently recommended by productivity experts.”
But the question is: Does your brand make that list?
The publisher trust footprint is more about “we” than “me”. Instead of you claiming your brand is credible, it gauges how others consistently treat you as a credible source with citations as a reward. Remember, this is the biggest benefit of a scaled earned distribution strategy.
For instance, a fintech brand that is consistently mentioned in relevant contexts by reputable publishers like Fortune, TechCrunch, or niche CFO trade media outlets can build a richer association between your brand and your topic area, far more than a press release aggregator would.
Why does it matter?
Since AI models like ChatGPT and Perplexity don't crawl the web in real time, they weigh content that has been consistently cited across trusted third-party publishers.
A strong publisher trust footprint means your brand is more likely to be pulled into AI-generated answers (GEO), not just ranked in traditional search (SEO). If your brand lives only on owned channels, like your blog or site, AI systems have little third-party signal to trust you (the equivalent of having no backlinks in 2015).
Guiding audit question: Where is your brand being mentioned, and do those sources carry authority?
General tools: Use your favorite AI tool and manually query your brand, category, and key topics with prompts to see if and how you're being cited in AI-generated answers. Example prompts for a footwear company could include questions that touch on your brand’s value proposition, such as: “Which running shoe has the best support for high arches?” or “What running shoe brands can integrate with my fitness app?”
The next set of tools will give you more granular data to better understand the scope of your brand visibility.
Deeper data tools: Run your brand name through Ahrefs' Content Explorer or Semrush's Brand Monitoring tool. Filter for editorial pages only, and export the referring domains. The brand mention and referring domain reports will also help you map your publisher footprint and spot domain concentration.
Probe the data for detail:
- How many unique domains are there?
- What's the average authority? Check Domain Rating (Ahrefs) or Authority Score (Semrush) for each mentioned domain.
💡 Keep in mind that a mention in a niche trade publication with a domain rating of 50+ carries far more weight than ten mentions on low-quality aggregator sites.
- Are they all the same type of site?
With these answers, you have created a snapshot of your baseline trust footprint. From there, you can quickly see whether your publisher footprint lacks depth, diversity, or both.
Signal 2: Determine your domain distribution
Understanding your domain’s distribution in LLMs, or domain diversity, is important because distribution is a way for LLMs to reflect how humans assess credibility by weighing domains that have diverse attributions.
Why does it matter?
A narrow distribution of domains is a risk. If 80% of your brand mentions come from a single trade outlet and they pivot their editorial focus, or go behind a paywall, your AI footprint could shrink overnight — with no redirect or recovery path, like a broken backlink in SEO. And, on the positive side, there is significant impact from repeated links. Having multiple authoritative domains referring back to the same piece of content on your site, communicates to LLMs that that blog or whitepaper or whatever it’s pulling from, is relevant.
Those repeated links are a core benefit of earned distribution like what Stacker provides.
To understand how strong your brand's signal is for distribution, count how many unique referring domains mention your brand in editorial or informational content (this includes actual named mentions and links). Check whether those domains cluster in one category (e.g. all industry trade press) or span multiple verticals, because domain diversity signals credibility to AI systems.
Guiding audit question: Are you a one-publisher brand, or do you have a diverse spread?
Tools: Measure how many unique domains mention your brand in editorial content (not ads or directories) by using Ahrefs or Semrush’s “brand mention” reports (not just “backlink” reports, since unlinked mentions count in GEO, more than they did with SEO). SparkToro has a tool that shows what sources your audience actually reads and trusts, which is useful for identifying publisher gaps.
Use the tool to gather data on whether your brand mentions cluster within a single vertical or span multiple publishers.
💡A brand that is mentioned across tech media, business press, and industry publications looks more broadly credible to an AI model than one that only appears in a single niche.
Signal 3: Clarify the quality of your content structure
AI models favor content that is structured for extraction, with clear headers, concise, attributed claims and statistics, and direct answers to questions. Content that is meandering, vague, or brand-speak doesn’t count as content quality in GEO.
Here’s an example: "Our platform helps teams work better together through a suite of integrated tools designed around modern workflows” is hard for LLMs to extract and surface. Whereas a more AI-friendly version is: "[Brand] reduces onboarding time by 40% according to a 2024 study of 500 enterprise teams."
Why does it matter?
Brands will struggle to be cited in GEO when they have walls of prose, buried data, and brand claims without supporting evidence, because LLM algorithms prioritize structured, user-friendly content that matches user intent.
Guiding audit question: Review a sample of your existing content and ask: could an AI pull a clean, attributable answer from this page?
Signal 4: Recognize your recency velocity
AI systems are also increasingly sensitive to fresh content. Content that hasn't been updated or cited recently can lose visibility online, even if it performed well historically.
Why does it matter?
Recency is important in LLM mechanics because training data has cutoff dates, whereas some retrieval-augmented AI tools, like Perplexity, actively pull in recent content.
How recent your content needs to be to be preferred by AI depends on the system you’re optimizing for, but AI systems frequently change their parameters.
If your last meaningful third-party mention in the media was more than 60–90 days ago, you may already be losing ground in retrieval-based AI tools. To gauge your recency velocity, you’ll want to measure how frequently your brand is mentioned in newly published content across third-party sources within 3 months.
A brand with strong historical media coverage but low recency velocity can lose AI visibility without being regularly mentioned in third-party sources.
Guiding audit question: Is your publisher footprint growing, flat, or quietly shrinking?
Tools: Track the velocity of new brand mentions across the web using Google Alerts or Mention.com.
What now?
This audit gives you a clearer picture of where your brand stands in comparison to your competitors and pinpoints areas for improvement.
The gaps it surfaces, like thin publisher footprint, low domain diversity, unstructured content, and stalled recency, are fixable problems — but only once you’ve uncovered them.
