Most AI visibility tools offer a wealth of data that looks very impressive. But here's the uncomfortable truth: most of it doesn't help you grow your crypto project.
I've spent the last few months testing over 30 AI visibility tools and talking to crypto marketing teams who've tried to move the needle on AI recommendations. The reality is that only very short list of metrics are linked to business outcomes.
Let me walk you through what really matters and what you can ignore.
1. Conversions from AI assistants
This is the metric that matters most. And almost nobody measures it.
Here's why it's so hard: the user journey in crypto is messy.
Someone might discover your project through a conversation with ChatGPT, then spend weeks researching on their own. When they finally take action—buying your token or using your protocol—they might do it through Crypto Twitter or a direct link from Discord.
The attribution trail is then completely broken.
Developers might spend hours chatting about protocols and APIs in Cursor or code editors, then search for your documentation separately. None of this shows up in your analytics as "AI traffic."
In crypto specifically, the final conversion often happens through "trusted" channels. Users don't want to click random links from AI responses or even Google. They want to verify through sources they already trust.
The fix is surprisingly low-tech: just ask people how they found you.
Add "How did you first hear about us?" to your onboarding flow. Include "AI assistant (ChatGPT, Claude, Perplexity, etc.)" as an option. You'll be shocked how many people select it.
This self-reported data isn't perfect, but it's infinitely better than the zero attribution most projects have right now. And it captures the cases where AI planted the seed, even if the final click came from somewhere else.
2. Traffic from AI assistants
This one's more straightforward to measure but comes with a major caveat.
You can only track direct traffic that arrives from AI platforms using UTM parameters, referrer data, and tools like Google Analytics 4.
But this only attributes traffic where a user clicks through a sources by AI.
The nature of AI search is that many interactions end without any click at all. Users get their answer directly in the chat interface. They might learn about your project, form an opinion about it, even recommend it to others—all without ever visiting your site.
Some tools are trying to solve this. Atomic AGI's "zero-click" tracking attempts to identify pages that appear in AI responses without generating visits.
This matters because in crypto, brand impressions count. If ChatGPT consistently recommends your DEX when users ask about Solana trading, that's valuable even if nobody clicks through. The trust transfer happens in the conversation.
For now, track what you can. But understand you're seeing the tip of the iceberg.
3. Visibility for key prompts
This is where you should actually spend your energy.
Instead of trying to track "average visibility" across hundreds of prompts, focus on a short list of prompts that actually matter to your business:
"What's the best [your category] on [relevant chain]?"
"How do I [core use case your product solves]?"
"Compare [your product] vs [main competitor]"
"[Your product name]" (do AI assistants even know you exist?)
Track these specific prompts across ChatGPT, Claude, Perplexity, and Google's AI features. Note whether you're mentioned, how you're positioned relative to competitors, and what the AI says about you.
This focused approach tells you something actionable. If you're being mentioned for "best yield optimizer on Ethereum" but are absent from "beginner-friendly DeFi apps," you know exactly what content to create.
Lots of AI visibility tools can automate this tracking. But you can start with a spreadsheet and 15 minutes of manual checking each week. The discipline of asking the same prompts repeatedly can be more valuable than any fancy dashboard.
Vanity metrics that don't matter
Here's what you should stop looking at:
Average visibility across 50+ prompts. This high number of prompts is almost meaningless. When tools give you "32% AI visibility across your tracked prompts," what are you supposed to do with that? It averages together prompts that matter enormously to your business with prompts that hardly anyone uses.
Sentiment scores. "Your brand has 78% positive sentiment in AI responses" sounds nice in a report. But AI models are generally neutral in their descriptions. They're not writing reviews. A technically accurate description of your protocol's tradeoffs isn't "negative sentiment". It's exactly what you want informed users to see.
Total mention counts. Being mentioned 500 times sounds impressive. Until you that those mentions are basically due to the way your 50+ prompts are defined. High mention count without conversions is just noise.
Competitor comparison percentages. "You appear in 40% of prompts where [competitor] appears." Okay, but in what context? Are you the recommended alternative? The cautionary example? The "also exists" footnote? Raw share-of-voice numbers without context are a recipe for self-delusion.
Historical "AI authority" scores. Some tools generate proprietary scores meant to represent your overall "AI-readiness" or "LLM authority." These composite metrics can be useful for quick research. But they're almost never comparable across different projects or industries.
What to do instead
Focus on the three metrics that actually connect to outcomes:
Monitor a focused list of high-intent prompts that represent real user questions your product should answer. Care about whether you're mentioned and how you're positioned, not about aggregate percentages.
Track referral traffic from AI sources as best you can, understanding you're only seeing a fraction of your true exposure.
Ask users directly how they discovered you. Make "AI assistants" one of the answers. This is your ground truth for whether AI visibility is actually driving growth.
The AI SEO tools market is exploding. Everyone wants to sell you visibility dashboards. But tracking visibility without a strategy is a distraction.
The projects winning in AI search right now are the ones who've figured out what they want to be recommended for. Then they ruthlessly optimize for those specific use cases. They're creating the documentation, guides, and technical content that make AI assistants confident in citing them.
Get ahead now
The AI search optimization is where SEO was in the early days.
The competition is relatively low. Building a solid foundation now will set you up for success in the near term and in the more competitive future.
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