Data-Driven Attribution

Data-driven attribution replaces fixed rules — like last-click or even-weighting — with a machine-learning model that distributes credit based on how touchpoints actually correlate with conversions. Instead of assuming the last click matters most, DDA looks at converting and non-converting paths and infers how much each touch contributed. Google Ads and GA4 now default to DDA, and in principle it's a real improvement: it can recognize that an early YouTube view or a mid-funnel search genuinely moved someone toward purchase. When the underlying data is rich and clean, DDA produces more defensible credit splits than any rule-based model. The catch is that DDA is a black box, and it's platform-specific. Google's DDA only sees Google's touchpoints and uses Google's proprietary model; Meta's equivalent only sees Meta's. The logic isn't disclosed, isn't auditable, and isn't portable — you can't take Google's credit splits and reconcile them against Meta's because they're modeling different data with different math. So DDA improves accuracy inside a single platform's walls while doing nothing to solve the cross-platform double-counting problem.