What your data has been hiding.

Most B2B revenue gets credited to whoever closed the visit. That's last-touch, and it lies, because it confuses presence with cause.

We use Data-Driven chains over your touchpoint paths and compute a removal effect: how much would conversion drop if this channel disappeared? That number is the credit. It's the same math that's been used in marketing science for a decade. We just made it run on your real data without a 12-week consulting engagement.

Attribution tells you who got credit. Incrementality tells you who actually moved the number.

We pair geo-holdouts with matched-pair synthetic controls. For each channel and period, we estimate the lift over a counterfactual where that channel didn't run. Attributed revenue and incremental revenue sit side-by-side in the channel table. The gap is the story.

Per channel, we fit a response curve: adstock for delayed effect, Hill saturation for diminishing returns. The same shape Robyn or LightweightMMM would fit, ours runs on your weekly spend ↔ attributed-revenue series and converges in seconds.

Then the allocator: given total budget, distribute it so marginal ROAS equates across active channels. That's a 1D root-find, not a black box. You move the slider; we tell you what bends and where.

Every morning, a one-page report explains what changed and why, referencing the prior week's reports so the narrative compounds.

Ask any question, get an answer with the underlying numbers cited. Then promote that question to an automation (Slack, email, or a line in tomorrow's daily report) so the next time the answer changes, you know without asking.