Ask five marketers how a conversion happened and you’ll get five answers, one per attribution model. That’s not a data problem; it’s a method problem.
Attribution as we’ve practiced it is broken, and predictive AI offers a way out.
How we got here (last click → MTA → chaos)
Last click was simple and wrong: it credited the final touch and ignored everything that built demand. Multi touch attribution (MTA) tried to spread credit but introduced arbitrary rules. Then privacy changes and signal loss made the underlying tracking unreliable. The result: chaos.
Why every attribution model disagrees
Each model encodes a different assumption about how credit should be assigned. Since none can observe the true counterfactual (what would have happened without the touch) they all disagree, and none can prove it’s right.
The predictive alternative
Modeling contribution instead of assigning credit
Instead of dividing credit after the fact, predictive models estimate each channel’s contribution to outcomes using statistical relationships, closer to MMM than to click tracking.
Forecasting incremental impact
The key question isn’t “who touched the customer” but “what would happen if we changed this channel?” Predictive AI forecasts incremental impact, which is what actually informs budget decisions.
What a trustworthy full funnel view looks like
Upper funnel channels get credit for demand they create; lower funnel channels for demand they capture. The view is probabilistic and honest about uncertainty, but consistent and defensible.
Making decisions you can defend
When your CFO asks why a channel got more budget, “the model forecasts higher incremental return” beats “last click looked good.” Predictive attribution turns budget debates into evidence-based decisions.