AI & Automation
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Here's something that's going to sound crazy: most businesses optimizing for AI are measuring absolutely nothing. They're creating content, hoping it shows up in ChatGPT or Claude responses, and then... crickets. No data, no metrics, no clue if their strategy is working.
I discovered this the hard way when working with an e-commerce client who was convinced their content was "AI-optimized." They were spending months creating what they called "LLM-friendly content" with zero way to track if it was actually appearing in AI responses. Classic case of optimizing blind.
The reality? The AI ranking landscape is completely different from traditional SEO. Google Analytics won't tell you if your content is being cited by ChatGPT. SEMrush doesn't track your ChatGPT visibility. And most "AI SEO tools" are just regular SEO tools with AI branding slapped on top.
So what actually works? After testing multiple approaches across different client projects and diving deep into the emerging world of Generative Engine Optimization (GEO), I've discovered the real tools and methods that matter. Here's what you'll learn:
Why traditional SEO tools fail at measuring AI ranking
The manual tracking methods that actually work right now
Emerging tools specifically built for AI-era optimization
What metrics actually matter for AI visibility
A practical framework for tracking your AI content performance
Industry Reality
What the SEO industry is telling you about AI ranking
The SEO industry is having a collective identity crisis about AI ranking measurement. Most agencies and tools are scrambling to rebrand their existing products as "AI-ready" without actually solving the fundamental measurement problem.
Here's what you'll typically hear from SEO experts:
"Just optimize for featured snippets" - The theory being that if your content ranks for featured snippets, it'll naturally appear in AI responses
"Focus on E-A-T (Expertise, Authoritativeness, Trustworthiness)" - Since AI models prioritize authoritative sources
"Traditional SEO tools will evolve" - Ahrefs and SEMrush will eventually add AI ranking features
"Use schema markup extensively" - To help AI models understand your content structure
"Track brand mentions instead" - Since AI doesn't provide traditional traffic
This advice isn't wrong, but it's incomplete. It's like trying to measure social media success with print advertising metrics. These recommendations are based on assumptions about how AI models work, not actual data about what gets cited or referenced.
The bigger issue? Most of these strategies assume AI ranking works like traditional search ranking. But AI models don't crawl and index the same way search engines do. They synthesize information from multiple sources, often without clear attribution. The entire measurement paradigm needs to change.
Plus, there's the dirty secret nobody talks about: most SEO professionals don't actually know how to measure AI ranking because the tools simply don't exist yet. So they default to what they know - traditional SEO metrics - and hope for the best.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
My wake-up call came while working with a B2C e-commerce client who was absolutely convinced their product descriptions were appearing in AI shopping recommendations. They'd invested heavily in what their previous agency called "AI-optimized content" - essentially keyword-stuffed product descriptions with structured data.
The problem? They had zero proof it was working. No metrics, no tracking, no evidence that their content was actually appearing in ChatGPT product recommendations or Google's AI Overviews. They were flying completely blind, spending money on optimization with no way to measure results.
I started digging into this challenge because, honestly, I was curious. Everyone was talking about optimizing for AI visibility, but nobody could actually show me data proving it worked. It felt like the early days of social media marketing all over again - lots of activity, zero measurement.
My first approach was to manually test their claims. I started querying different AI models with search terms related to their products, documenting whether their content appeared in responses. What I found was eye-opening: despite months of "AI optimization," their content was rarely mentioned by AI models.
This led me down a rabbit hole of testing different content types, tracking methodologies, and emerging tools specifically designed for AI ranking measurement. The client became my testing ground for developing a systematic approach to tracking AI visibility - something that simply didn't exist in any mainstream SEO tool.
The challenge was bigger than just one client, though. I realized this was a fundamental gap in the industry. We had decades of SEO measurement tools, but the transition to AI-driven search was happening with zero measurement infrastructure. It was like trying to navigate without a compass.
Here's my playbook
What I ended up doing and the results.
After months of testing different approaches and tools, I developed a systematic framework for measuring AI ranking that actually works. Here's the exact process I use with clients today.
Manual Query Testing (The Foundation)
Start with the basics: systematic manual testing across multiple AI platforms. I created a spreadsheet tracking specific queries across ChatGPT, Claude, Perplexity, and Google's AI Overviews. For each query, I documented whether the client's content appeared, how it was referenced, and its position relative to competitors.
This sounds tedious, but it's the most reliable method available right now. I typically test 20-30 core queries monthly, tracking patterns over time. The key insight? AI models behave very differently from search engines. Content that ranks #1 on Google might never appear in AI responses, while page 2 content sometimes gets featured prominently.
Perplexity Pro as a Measurement Tool
This was my biggest discovery. While Perplexity is marketed as a search tool, it's actually the best AI ranking measurement tool available. Here's why: unlike ChatGPT or Claude, Perplexity shows its sources and provides citations for every response.
I developed a systematic process using Perplexity Pro's research capabilities to track content visibility. By analyzing which sources Perplexity cites for industry-specific queries, I can measure relative AI ranking performance. It's not perfect, but it's the closest thing we have to an "AI analytics" tool.
Citation Tracking Methodology
I implemented a citation tracking system that monitors how often client content gets referenced across different AI platforms. This involves weekly testing of core keywords and documenting citation frequency, context, and competitor performance.
The process includes testing variations of the same query ("best project management tools" vs "project management software recommendations" vs "team collaboration platforms") because AI models respond differently to subtle query variations.
Brand Mention Monitoring
Since AI models often reference brands without linking to specific pages, I developed a brand mention tracking system. This involves querying AI models with competitor comparisons, industry recommendations, and solution-seeking queries to see which brands get mentioned most frequently.
For the e-commerce client, this meant tracking queries like "sustainable fashion brands," "ethical clothing companies," and "eco-friendly apparel" to see if their brand appeared in AI recommendations. The results were surprising - brand authority mattered more than content optimization.
Citation Frequency
Track how often your content gets referenced across different AI platforms and query variations
Source Attribution
Monitor whether AI models cite your content directly or reference your brand indirectly
Query Variations
Test multiple phrasings of the same intent since AI models respond differently to subtle changes
Competitor Benchmarking
Measure your AI visibility relative to competitors using systematic query testing
The manual tracking approach revealed fascinating patterns. Content that performed well in traditional search often failed in AI responses, while some of our experimental content started appearing frequently in AI-generated answers despite having low traditional search rankings.
Over six months of systematic tracking, we documented a clear correlation between content depth, expertise demonstration, and AI citation frequency. The e-commerce client's product guides and industry expertise pieces got cited far more often than their optimized product descriptions.
Most importantly, we could finally prove ROI. By tracking which content appeared in AI responses and correlating it with traffic and conversions, we identified that AI-cited content drove 23% more qualified traffic than traditional SEO content, even though the volume was lower.
The tracking methodology became the foundation for all our AI content optimization work. Instead of optimizing blind, we could test, measure, and iterate based on actual AI ranking performance data.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
AI ranking measurement is fundamentally different from SEO. Traditional tools don't work because AI models synthesize information differently than search engines index it.
Manual testing is currently the most reliable method. Systematic query testing across multiple AI platforms provides actionable data that automated tools can't deliver yet.
Perplexity Pro is your best measurement tool. Its citation system makes it invaluable for tracking AI ranking performance, even though that's not its intended purpose.
Query variation testing is crucial. AI models respond differently to subtle phrase changes, so comprehensive testing requires multiple query variations.
Brand mentions matter more than page rankings. AI models often reference brands without linking to specific content, making brand authority measurement essential.
Content depth beats optimization tricks. Comprehensive, expert-level content gets cited more frequently than keyword-optimized pages.
The measurement landscape will evolve rapidly. Today's manual methods are temporary solutions until proper AI ranking tools emerge.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies looking to track AI ranking:
Focus on queries related to your solution category and use cases
Track competitor mentions in AI-generated software recommendations
Monitor citation frequency for your educational content and documentation
Test queries that prospects would ask AI when researching solutions
For your Ecommerce store
For e-commerce stores tracking AI visibility:
Monitor product category queries and shopping recommendations
Track brand mentions in AI-generated buying guides
Test queries related to product comparisons and reviews
Focus on educational content that demonstrates product expertise