When Attribution Models Disagree, Trust Incrementality Over Last-Click
How do I know which attribution model to trust when different tools report conflicting revenue numbers? Run an incrementality test before shifting budget on the basis of any attribution model.
By InnovaAI ResearchPublished Updated
“How do I know which attribution model to trust when different tools report conflicting revenue numbers?”
Run an incrementality test before shifting budget on the basis of any attribution model.
Agencies often pick the attribution tool that flatters their own performance, then present it to the client as objective truth, ignoring the black-box nature of the model and skipping the incrementality test that would validate it.
Attribution models are estimates, not ground truth. Platforms like Ruler Analytics and Heeet tie touchpoints to CRM pipeline, while Prescient AI and Measured use media mix modeling and incrementality testing to isolate halo effects. When models disagree, incrementality testing provides the causal evidence needed to justify spend changes. A recent study found that 89% of brands are skipped in AI-driven buyer recommendations, underscoring that visibility in one model does not guarantee real-world impact.
- •Client reports show last-click and multi-touch models assigning revenue to different channels
- •Agency is deciding whether to reallocate budget based on attribution data
- •Client asks for proof that a specific channel drives incremental revenue
- •Multiple attribution tools are in use and produce divergent results