Definition
Time-decay attribution is a multi-touch attribution model that assigns increasing credit to email interactions as they get closer in time to the conversion event. The most recent campaign receives the largest share of credit, the one before it receives less and so on, following an exponential or linear decay curve.
This model acknowledges that while earlier campaigns may have introduced a subscriber to the product, the campaigns closest to the purchase likely had the strongest influence. It offers a middle ground between last-touch attribution, which ignores early touchpoints, and linear attribution, which treats all touchpoints as equally influential.
How the Decay Curve Works
A typical time-decay model divides the conversion value across touchpoints using a half-life formula. If the half-life is set to seven days, a touchpoint seven days before conversion receives half the credit of one on the day of conversion. The decay rate and half-life window are configurable per business.
Why It Matters
This matters because the choices you make here show up directly in your results. It offers a middle ground between last-touch attribution, which ignores early touchpoints, and linear attribution, which treats all touchpoints as equally influential. When this is handled well it supports engagement, delivery, and the trust subscribers place in your brand; when it is neglected, the effects tend to show up in declining performance and harder-to-fix problems further down the line.
Best Practices
- Use time-decay attribution when your sales cycle is short to medium length
- Calibrate the half-life based on actual observed time-to-conversion data rather than guesswork
- Combine time-decay data with qualitative input from surveys or customer interviews to validate assumptions
- Compare results against last-touch and first-touch models to understand how attribution choice affects your conclusions
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Related Glossary Terms
A/B Testing
A/B testing in email marketing is the practice of sending two variations of an email to a small sample of your list to determine which version performs better before sending the winner to the remaining subscribers.
ARPU (Average Revenue Per User)
ARPU (Average Revenue Per User) is a metric that measures the average revenue generated per email subscriber over a specific period, used to evaluate list value and campaign effectiveness.
Attention Rate
Attention rate is the percentage of email opens that last longer than 5 seconds, distinguishing genuine reads from passive opens, preview-pane views, or Apple MPP auto-loads.
Average Order Value in Email
Average order value in email is the average amount spent per transaction from recipients who clicked through from an email campaign.
Behavioral Segmentation
Behavioral segmentation is the practice of grouping subscribers based on their actions, such as opens, clicks, purchases, browsing and engagement patterns.
Bounce Rate
Email bounce rate is the percentage of emails that were rejected by the receiving server before reaching the recipient. It is a key indicator of list health and data quality.
Frequently Asked Questions
Good practice here means handling Email Attribution Time Decay in a way that is relevant, timely, and honest for your audience. Time-decay attribution gives progressively more credit to email touchpoints that occur closer to conversion. It balances simplicity with a more realistic view of how subscribers move through a purchase decision. Done well, it improves engagement and builds trust; done poorly, it creates friction that costs you results.
Because it touches the parts of email that drive outcomes: relevance, trust, and delivery. Small improvements compound, while repeated mistakes quietly erode the health of your programme.
The most common problems are treating Email Attribution Time Decay as a one-off task, ignoring what the data says, and copying competitors without testing. All three lead to effort that does not translate into better results.
Compare the metrics it should influence — engagement, conversions, and deliverability — before and after you make changes. Trends over time matter far more than any single send.
It supports the same goal as the rest of your email programme: the right message to the right person at the right time. Aligned with segmentation and automation, it reinforces everything else rather than competing with it.