Definition
Time-decay attribution assigns more credit to touchpoints closer in time to the conversion.
Example
An ad clicked yesterday gets more credit than an ad clicked two weeks ago.
How to use it
- Often better than linear when buying cycles are short and tracking is clean.
- Still biased for retargeting and branded search in many accounts.
- Keep the same decay rule so trends remain comparable.
Common mistakes
- Using time-decay without checking conversion lag patterns.
- Comparing results across models without aligning windows.
Measured as
Measure Time-decay Attribution with a fixed attribution window, conversion event, and spend basis before comparing campaigns or creative tests.
Misused when
- Using time-decay without checking conversion lag patterns.
- Comparing results across models without aligning windows.
Operator takeaway
- Often better than linear when buying cycles are short and tracking is clean.
- Still biased for retargeting and branded search in many accounts.
- Keep the same decay rule so trends remain comparable.
- Use Time-decay 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 Time-decay 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.