Definition
Linear attribution spreads conversion credit evenly across all recorded touchpoints in a conversion path.
Example
If a user has four touches, each gets 25% of the credit under linear attribution.
How to use it
- Helpful for multi-touch narratives, but it can over-credit low-impact touches.
- Use it for directional insights; validate decisions with tests.
- Keep the same lookback window when comparing periods.
Common mistakes
- Comparing linear to last-click without acknowledging different intents.
- Assuming linear credit is causal for every touchpoint.
Measured as
Measure Linear Attribution with a fixed attribution window, conversion event, and spend basis before comparing campaigns or creative tests.
Misused when
- Comparing linear to last-click without acknowledging different intents.
- Assuming linear credit is causal for every touchpoint.
Operator takeaway
- Helpful for multi-touch narratives, but it can over-credit low-impact touches.
- Use it for directional insights; validate decisions with tests.
- Keep the same lookback window when comparing periods.
- Use Linear Attribution 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
- 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.
- Decide which report owns Linear Attribution before comparing campaigns, channels, or creative tests.
Where to use this on MetricKit
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.