How We're Approaching GEO Analysis at Cogny

When we started building Cogny, the primary focus was paid media optimization. AI agents that analyze Google Ads and Meta campaigns, find waste, and recommend improvements.

But we kept hearing the same question from customers:

"Can you help us understand what's happening with AI search?"

Marketers were watching their organic traffic shift. Some keywords were declining even though rankings held steady. Click-through rates were dropping on informational queries. And nobody had a clear picture of whether their content was showing up in ChatGPT, Perplexity, or Google's AI Overviews.

There was no good framework for this.

SEO tools track Google rankings. Analytics tools track website traffic. But very few approaches connect Search Console data with AI search citation patterns to give marketers a unified view of their search visibility.

So we started building an analytical approach to the problem. Here's how we're thinking about it.

The Problem We're Working to Solve

The core challenge: Marketers have no reliable way to answer a simple question: "Is my content being cited by AI search engines, and what can I do to improve it?"

Specifically, they need:

Visibility: Which of my pages are likely being cited by AI search engines, and for which queries?
Indicators: Which Search Console signals correlate with AI citation likelihood?
Optimization: What content changes tend to improve AI citation chances?
Measurement: How can I track whether my GEO efforts are working over time?

The existing landscape offers very little. Traditional SEO tools were built for a world where Google's ten blue links were the primary game. They weren't designed for a world where AI-generated answers increasingly replace those links.

Our Guiding Principles

Before diving into analysis, we established three principles:

Principle 1: Build on Data We Already Have

We didn't want to require customers to install new tracking pixels, integrate new data sources, or change their workflow. We needed to work with data they already had: Google Search Console, Google Ads, and Google Analytics 4 -- all flowing into BigQuery.

These three data sources, when combined intelligently, contain remarkably strong signals about content quality and search authority.

Principle 2: Correlation Before Causation

We can't directly measure whether ChatGPT or Perplexity is citing a specific page in real time at scale. What we can do is identify which Search Console metrics correlate with the kinds of content AI models tend to cite, and use those correlations to guide analysis.

This means intellectual honesty is critical. We're working with indicators, not certainties.

Principle 3: Actionable Over Academic

Every insight has to come with a clear action. "This page has strong authority signals" is interesting. "This page has strong authority signals -- and here are specific improvements that could make it even more citable" is useful.

We optimized our reporting templates for actionable output.

The Research: Which Search Console Metrics Matter

Before building our reporting approach, we needed to understand the relationship between Search Console metrics and the kind of content AI models tend to cite.

We studied how high-quality, frequently-cited content tends to perform in Search Console:

Analyzed pages across customer accounts, spanning various industries and content types
Examined their Search Console metrics: CTR, position, impression counts, query diversity, click patterns, position stability
Compared patterns between content that appears authoritative and well-structured versus content that doesn't

What we found:

Several Search Console metrics appear to be strong indicators of the kind of content AI models prefer:

Informational query CTR -- how well your content performs on question-based searches. Content that earns high CTR on informational queries tends to be the kind that AI models cite: clear, comprehensive, authoritative.
Position stability -- consistent rankings over time signal sustained authority. AI models appear to favor sources with established, stable presence.
Long-tail query diversity -- how many unique queries a page ranks for. High diversity suggests the page covers a topic comprehensively, which is exactly what AI models look for when selecting citations.
CTR outperformance -- earning more clicks than expected for a given position. This suggests compelling, trustworthy content that searchers prefer.
Topic cluster breadth -- how comprehensively a domain covers a topic. AI models tend to cite sources from domains that demonstrate deep expertise.

These observations form the foundation of our GEO analysis approach.

How We Use This in Cogny

Data Analysis Through BigQuery

Cogny connects to your marketing data in BigQuery, where your Search Console, Google Ads, and GA4 data lives. This gives us the ability to run analytical queries across data sources to surface GEO-relevant insights.

Rather than building a separate scoring engine, we leverage BigQuery as the analytical backbone. Our AI reporting templates query across your connected data sources to surface the metrics that matter for GEO.

AI-Powered Reporting Templates

This is where the research translates into practical value. We've built GEO-focused reporting templates that analyze your Search Console data and surface insights about your content's AI search readiness.

Our reporting templates examine:

Informational query performance: How your content performs on the question-based searches that AI models care about most
Content authority signals: Which pages show the strongest indicators of the kind of authority AI models favor
Improvement opportunities: Where small content changes (better structure, clearer definitions, more specific data) could meaningfully improve your content's citability
Cross-channel context: How your paid search data and analytics data add context to the organic picture

The AI Chat Interface

Beyond automated reports, Cogny's AI chat lets you ask questions about your data directly. You can ask things like:

"Which of our pages have the strongest informational query CTR?"
"Where are we seeing CTR decline despite stable rankings?"
"Which topic clusters have the most comprehensive coverage?"

The AI draws from your actual BigQuery data to give you answers grounded in your real performance, not generic advice.

Ongoing Monitoring

GEO isn't a one-time analysis. It's an ongoing process. Our scheduled reporting templates can:

Track metric changes over time for your key pages
Flag significant movements (positive or negative) in the signals that matter for GEO
Correlate changes with content updates to help you understand what's working
Report on overall content health and readiness for AI search

Key Insights from Our Analysis Work

Through analyzing data across customer accounts, we've observed some consistent patterns:

Content structure is the most common gap. Pages that rank well on Google are often not formatted in ways AI models prefer to cite. Missing clear definitions, lacking specific data, and poor FAQ structure are the most frequent issues.
Quick wins exist everywhere. Simple changes like adding specific data points, restructuring headings as questions, and including clear summary sections can improve content quality signals quickly.
Cross-channel data adds crucial context. Looking at Search Console data alone tells part of the story. Adding Google Ads conversion data and GA4 engagement metrics gives a much fuller picture of which content truly performs.

Why Data-Driven GEO Analysis Matters

There are two approaches to GEO:

The generic approach: Follow general best practices. Write clear content. Use structured data. Hope for the best.
The data-driven approach: Analyze your actual Search Console metrics. Identify which specific pages show the strongest signals. Understand which specific changes are most likely to help. Measure the results over time.

Approach 1 is better than nothing. But it's guesswork at scale.

Approach 2 is what we're building toward. Because GEO is too important and too new to rely on general advice. You need analysis that tells you, for your specific domain, your specific pages, and your specific competitive landscape, where to focus and what to improve.

What's Next

We're continuing to develop our GEO analysis capabilities in several directions:

Deeper Search Console analysis: Refining which metric combinations are the strongest indicators of AI citation readiness
More reporting templates: Building specialized GEO reports for different industries and content types
Trend tracking: Better longitudinal analysis to show how your GEO-relevant metrics are changing over time
Cross-channel GEO context: Understanding how content improvements for GEO affect paid search performance and overall marketing ROI

GEO is still early. The search landscape is shifting rapidly. But the foundation of data-driven analysis -- using your real Search Console data, combined with AI-powered insights -- provides a framework that will improve as the field matures.

See Where Your Content Stands

If you're curious about how your content measures up on the metrics that matter for AI search, Cogny can help. Our GEO and SEO analysis connects to your Search Console data in BigQuery and surfaces insights about your content's strengths and opportunities.

What you'll get:

Analysis of your Search Console metrics through a GEO-relevant lens
Insights into which pages show the strongest authority and citability signals
Specific areas for improvement based on your actual data
A clearer picture of where you stand relative to the content AI models tend to prefer

Explore Cogny's GEO and SEO Analysis to understand where your content stands in the new search landscape.