Data-driven Attribution

Definition

Data-driven attribution assigns credit based on observed conversion paths rather than a fixed rule, but it still relies on tracked data and assumptions.

Example

A platform model assigns more credit to a mid-funnel touch if it often precedes conversions.

How to use it

• Use it as one lens; validate with holdouts and blended MER where possible.
• Expect instability when data volume is low or tracking is incomplete.
• Keep conversion definitions stable so model outputs are comparable.
• Audit touchpoint windows regularly to keep model inputs consistent.

Common mistakes

• Treating model output as causal truth without experiments.
• Comparing results across periods with different tracking coverage.
• Ignoring consent loss or blocked signals that bias credit allocation.

Why this matters

This term matters because it affects how you interpret performance and make budget decisions. If you use inconsistent definitions or windows, ROAS/CPA can look "better" while profit gets worse.

Practical checklist

• Write a 1-line definition for "Data-driven Attribution" that your team will use consistently.
• Keep the time window consistent (weekly/monthly/quarterly) when comparing trends.
• Segment results (channel/plan/cohort) before drawing big conclusions from blended averages.
• Sanity-check with a related calculator from the same category on MetricKit.
• Read the related guide (e.g., Attribution vs incrementality: what to trust, when, and how to test) for context and common pitfalls.

Where to use this on MetricKit