Data-driven attribution (DDA)

Data-driven attribution assigns credit for a conversion across every touchpoint in a customer's path, with the split determined algorithmically from historical data rather than by a fixed rule. Instead of always crediting the first or last interaction, it compares converting paths to non-converting paths and estimates how much each channel or touchpoint actually increased the probability of conversion.

This makes it a form of multi-touch attribution, but a more statistically grounded one than simple rule-based splits such as linear or time-decay weighting. It differs from last-click attribution and first-click attribution, which are single-touch models that ignore everything except one point in the journey, and from marketing mix modeling, which works from aggregate, channel-level spend rather than individual user paths.

Data-driven attribution matters because it reallocates credit toward touchpoints that genuinely move people toward converting, which can shift budget away from channels that merely happen to appear late or first in most paths. It needs a reasonably large volume of conversion data to be reliable, performs worse for low-volume advertisers, and, like any attribution model built on observed paths rather than controlled experiments, can still confuse correlation with causation; pairing it with incrementality testing helps validate its conclusions.