Definition
An attribution model defines how you assign conversion credit across touchpoints (for example first-click, last-click, or multi-touch).
Example
A last-click model gives 100% credit to branded search, while a linear model splits credit across ads, email, and organic.
How to use it
- Keep model and window consistent when comparing campaigns.
- Use incrementality tests when attribution is heavily biased (especially retargeting).
- Document the model used in dashboards so teams do not compare apples to oranges.
Common mistakes
- Changing the model mid-quarter and breaking performance baselines.
- Treating attribution as causal without validation.
Measured as
Measure Attribution Model with a fixed attribution window, conversion event, and spend basis before comparing campaigns or creative tests.
Misused when
- Changing the model mid-quarter and breaking performance baselines.
- Treating attribution as causal without validation.
Operator takeaway
- Keep model and window consistent when comparing campaigns.
- Use incrementality tests when attribution is heavily biased (especially retargeting).
- Document the model used in dashboards so teams do not compare apples to oranges.
- Use Attribution Model only inside a stable attribution rule, conversion definition, and time window so campaign comparisons stay honest.
- If performance changes, check whether the metric moved for a real business reason or because the measurement setup changed underneath you.
Next decision
- Quantify the impact with Incrementality Lift Calculator if you need to turn the definition into an operating assumption.
- Read Attribution vs incrementality: what to trust, when, and how to test if the decision depends on interpretation, policy, or trade-offs beyond the raw formula.
Where to use this on MetricKit
Calculators
- Incrementality Lift Calculator: Estimate incremental conversions, incremental ROAS, and incremental profit from a holdout test.
- MER Calculator: Calculate MER (Marketing Efficiency Ratio / blended ROAS) and estimate break-even and target MER from margin assumptions.
Guides
- Attribution vs incrementality: what to trust, when, and how to test: A practical guide to attribution vs incrementality: common attribution models, window pitfalls, how MER/marginal ROAS fit in, and how to run holdout/geo tests.
- UTM + GA4 attribution: practical tracking for paid ads (without lying to yourself): A practical guide to UTMs and GA4: consistent source/medium/campaign tagging, conversion deduplication, and common attribution traps.