AI platforms are sending traffic to your website right now. Every major analytics tool on the planet classifies it as "direct" or "other." We built the model that actually measures it.
The entire marketing analytics industry is still operating on a pre-AI attribution model. The only channels that exist in today's tools are the ones with UTM parameters or cookies — and AI platforms use neither.
The largest analytics platforms categorize AI referral traffic as "direct" or "other." Some have the architecture to track it but haven't built AI source detection. Most don't track it at all. The result: the fastest-growing traffic channel in the history of the internet is invisible to every measurement tool businesses rely on.
AI Mix Modeling is the first attribution model that combines real-time AI visibility scoring with server-side session attribution and user-defined conversion economics — bridging the gap between brand awareness in AI and revenue in your CRM.
Real-time measurement of how often 7 AI platforms recommend your brand. Not keyword rankings — actual recommendation frequency across ChatGPT, Claude, Perplexity, Gemini, Grok, Copilot, and Google AI.
Server-side tracking that identifies exactly which AI platform sent each visitor. No cookies. No UTMs. Edge-level detection that tells ChatGPT traffic apart from Perplexity traffic — the distinction your analytics tool erases.
You define what a visit, a lead, and a conversion are worth in your business. Faneros maps AI visibility lift to actual revenue impact — connecting brand awareness in AI to dollars in your CRM.
Classical Marketing Mix Modeling decomposes revenue into channel contributions using regression. AI Mix Modeling extends this by adding AI platform coefficients:
Where each β coefficient represents the marginal revenue contribution of that channel. The AI platform terms (β5 through β11) capture recommendation-driven conversions that traditional models assign to other channels — or lose entirely.
Some AI platforms generate measurable traffic. Perplexity includes source links. ChatGPT's browsing mode creates referral visits. These are captured with server-side referral detection:
This layer captures 15–25% of AI-influenced conversions. The rest happen offline — a phone call, a direct Google search of your name, a saved-for-later mental note. No click, no cookie, no UTM.
When AI recommends your business, a measurable percentage of users will Google your name directly. This creates a branded search lift that can be isolated using difference-in-differences:
Where CVRbranded is the conversion rate on branded searches (typically 3–8× higher than non-branded) and AOV is average order or case value.
By measuring visibility scores across AI platforms over time, conversion changes can be attributed proportionally to each platform's contribution:
This weighted allocation distributes incremental conversions proportionally to each platform's visibility contribution. This is the model behind the Incremental Lift Waterfall — the chart that shows exactly how many net-new conversions each AI platform adds on top of your baseline.
Three models, each answering a different question about how AI drives conversions.
Which channel creates initial awareness? If someone first heard your name from ChatGPT, first-touch captures that origination value.
Which channel closes deals? Systematically undercounts AI because AI is rarely the last touch — it's the first or middle touch that drives everything downstream.
Balanced view of the full funnel. AI platforms get measured credit without over- or under-weighting. This is the default for AI attribution.
Traditional marketing mix modeling requires 12+ months of historical data and econometric regression. Fortune 500 companies pay management consultancies and research firms six figures to run it manually.
But that methodology was never designed for a channel that didn't exist 18 months ago. AI Mix Modeling applies lift-correlation methodology specifically to the AI channel — the one channel growing fastest and the one channel that none of those firms, tools, or platforms are measuring yet.
| Capability | Legacy Analytics | CRM Platforms | Consultancy MMM | Faneros |
|---|---|---|---|---|
| AI as distinct traffic source | ✗ | ✗ | ✗ | ✓ |
| Server-side AI attribution | ✗ | ✗ | ✗ | ✓ |
| AI visibility scoring (7 platforms) | ✗ | ✗ | ✗ | ✓ |
| User-defined conversion values | Partial | Partial | ✓ | ✓ |
| Lift-correlation to revenue | ✗ | ✗ | ✓ | ✓ |
| Applied to AI channel specifically | ✗ | ✗ | ✗ | ✓ |
Your first GEO scan is the entry point. AI Mix Modeling builds on every scan you run — the more data, the sharper the model.
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