The same revenue, attributed six ways.
This is real attribution math running in your browser over 600 seeded customer journeys — no mocked numbers. Last-click hands all the credit to the closer. Headless Builds's data-driven model uses Markov removal effects to credit the channels that actually move conversions. Switch models and watch the ledger rewrite itself.
Data-driven — Markov removal-effect. Credit by how much each channel actually moves conversions.
Attributed revenue
$57,406
across all channels
Conversions
223
stitched journeys
Avg. path length
2.8
touches to convert
Top channel
Paid Social
21% of credit
Revenue by channel
credit reallocates as you switch models
Channel ledger
attributed revenue · share · touches
Misallocation vs. truth
data-driven is the baseline
You're looking at the ground truth. Switch to a rule-based model above to see how much revenue it puts on the wrong channel — and which budgets you'd cut by mistake.
Converting journeys
live// numbers are deterministic from a seed — refresh-stable, model-reactive