Attribution that shows its working, including where it stops.
Trace follows the money from ad spend through a recorded conversation to a closed deal, on identity you collect yourself. Where a link cannot be made, it says which kind of gap it is rather than folding everything into one number that reads as failure.
The honest total
One unattributed number is a lie. Three are the truth.
Most tools show you attributed revenue and a large remainder, which reads as a matching failure. Usually it is not. Trace separates money that predates measurement from money still waiting to be tied to a sale, so the total says what it means.
- BoundTied to a sale, counted in ROAS
- Pre-trackingPredates measurement, not from your ads
- OrganicMeasured, not yet tied to an ad sale
bound + pre-tracking + organic equals the ledger total. Recovery paths are evidence-based and display-only, never a manual correction.
Two models, side by side
First touch and last non-direct disagree. Trace shows you both.
Attribution models are assumptions, not facts, and a single blended number hides which assumption you are buying. Trace renders both and marks where they disagree, because the disagreement is information.
Journeys
How this specific customer found you.
Every touchpoint for a real person, in order, from first anonymous visit to the conversation that closed. Recognition is by first-party evidence you collect on your own domain, not an identifier a platform can revoke.
Signal health
Whether the ad platform is getting what it needs to find your buyers.
Match quality is a real constraint on performance and mostly invisible. Trace surfaces the severity, names the specific gap, and only raises a recommendation when the evidence supports one.
Measurement you can hand to a sceptic.
Every module is part of one platform. You get all of it, and the tiers differ by scale rather than by which capabilities are switched on.