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.
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 "Time-decay 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
Calculators
- A/B Test Sample Size Calculator: Estimate sample size per variant for a conversion rate A/B test given baseline CVR, MDE, significance, and power.
- CPL to CAC Calculator: Convert cost per lead (CPL) into CAC using lead-to-customer rate (and compute targets).
- Break-even CVR Calculator: Compute the CVR required to break even (and hit a target) given CPM, CTR, AOV, and contribution margin.
- Click-through Conversion Rate Calculator: Calculate click-through conversion rate (click-to-conversion CVR) and estimate required clicks for target conversions.
- ROAS Calculator: Calculate Return on Ad Spend (ROAS) and estimate contribution profit after ad spend.
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.