Data-driven attribution learns from converting and non-converting paths instead of applying a fixed equal-credit rule. The actual model varies by advertiser and key event.
The demo changes illustrative weights when the path changes. These values explain allocation, not a production model's estimates.
A model can learn only from observable touchpoints. Do not confuse assigned credit with causal lift; validate consequential decisions with experiments.
When to use
Use it to interpret allocated credit in multi-touch conversion reports.