Ever notice how if i walk into a boardroom and tell the stakeholders that we have "better ai visibility," they will rightfully ask me to leave. I’ve spent nine years in the SEO trenches, moving from legacy keyword rankings to complex multi-market attribution, and the shift toward Generative Engine Optimization (GEO) is the most significant hurdle we’ve faced. When you are building a competitor set for GEO, you aren't just looking at who ranks higher in a search engine result page (SERP); you are looking at which models are recommending your brand, and why.
The first question I always ask when a client tells me they have a "GEO strategy" is: What would I show in a weekly report? If you https://stateofseo.com/what-are-crawlability-checks-for-geo-and-why-do-they-matter/ can’t answer that with hard data—citations, sentiment scores, and attribution pathing—you don’t have a strategy; you have a wish list.
The Shift: Why Traditional Benchmarking Fails in AI Search
Historically, we used tools like Semrush to track organic visibility. It’s an essential tool for traditional SERP landscape analysis, but traditional SEO tools were built for "ten blue links." They were not built to measure the hallucination-prone, citation-heavy world of LLMs. GEO requires a different architectural approach. You need to know exactly which engine is serving the answer, how many citations are being generated, and whether those citations are actually driving clicks in your GA4 or Adobe Analytics dashboard.
When building your competitor set, stop focusing on "share of voice" as a generic vanity metric. Instead, break it down into the three pillars of GEO performance:
- Brand Mentions: How often the brand is indexed in the training data/pre-prompted context. Citations: The actual URL-based attribution within an LLM response. Share of Voice: The frequency of a brand being mentioned as an authority in a given niche relative to competitors.
The Tooling Landscape: Who Covers What?
My biggest pet peeve in this industry is the claim that a tool "tracks everything." Nothing tracks everything. Before you buy, ask for their list of engines. Are they covering Perplexity? Google SGE/AI Overviews? OpenAI’s ChatGPT? Claude? If they can’t tell you the engine coverage, the database size, and the update cadence, their data is fundamentally unreliable.
When selecting your tech stack for GEO benchmarking, you need to integrate specialized players with your traditional analytics:
Addressing the Pricing Data Gap
One of the most common mistakes I see in client competitor sets is the assumption that because a tool can "scrape" content, it can accurately compare competitor pricing. Let’s be clear: it cannot.
Many clients complain that their competitive reports are missing pricing figures. The reality is that competitive pricing in the age of dynamic AI is rarely structured data. It is often obscured behind dynamic pages, gated content, or localized offer variations. If your agency is feeding you "estimated" prices from a scrape, they are inventing data. In my reporting, I mark pricing as "unstructured/dynamic" and rely on manual audits for core product sets. Do not trust an automated tool to give you a clean pricing table without verifying the source data depth.
Integrating with GA4 and Adobe Analytics
The only way to prove GEO value is through integration. If you are using GA4, you should be looking at "Referral Traffic" from AI search engines—a segment that most companies have failed to properly configure. If you use Adobe Analytics, use custom dimensions to track the query source if the referrer header is present.
The Workflow:
Identify the query set that drives conversion in your traditional analytics. Run that same query set through your GEO benchmarking tools (e.g., Peec AI). Map the "Citations" from the AI response to your landing page URLs. Analyze the conversion rate of those specific landing pages against your organic search baseline.
Conducting a GEO-Focused SWOT Analysis
When you present your competitor set to leadership, use a SWOT analysis specifically tailored for AI search. Do not use generic SWOT terms. Use the data you’ve gathered from your prompt database.
Strengths: The "Authority" Pillar
Identify which competitors have a high "citation rate." If a competitor is cited in 40% of queries for your industry, they have established high-quality, query-relevant content that the LLM trusts. This is not just "content"; it is semantic authority.
Weaknesses: The "Hallucination" Gap
Are your competitors failing to appear in the LLM response even when they have high domain authority? This is your window of opportunity. Analyze their content structure. Are they failing to use the schema required by the engine? Are they lacking the concise, factual summaries that AI models prioritize?
Opportunities: Prompt Databases
Use your Otterly AI data to identify what users are asking *around* your product. Build a prompt database that tests how your brand performs against competitors in long-form, comparative searches. If you aren't the one defining the competitive landscape in the prompt, the engine will do it for crawlability checks ai you—and it might be wrong.
Threats: The "Zero-Click" Reality
The ultimate threat is the AI answer that provides the information so completely that the user never clicks through to your site. Your SWOT analysis must quantify how much of your "competitor set" is actually losing traffic to the AI surface itself.
Final Thoughts: The Weekly Report Reality Check
I remember a project where wished they had known this beforehand.. When you sit down to build this report, keep it actionable. If I were the SEO lead for your brand, my weekly GEO report would contain exactly three things:
- Engine Coverage Table: A snapshot of our citation count across the 5 engines we care about vs. our top 3 competitors. Attribution Delta: A comparison of conversion rates between standard search traffic and AI-referred traffic (pulled from GA4/Adobe). Prompt Efficacy Score: A metric of how often our brand is recommended when a "neutral" user asks for a solution in our vertical.
Anything else—fluffy charts about "brand sentiment" or "AI visibility scores" without a cited source—is just noise. Stick to the metrics, insist on knowing the data source cadence, and stop chasing buzzwords. If the data isn't driving revenue, it's not a metric; it's a distraction.