Time Decay Attribution Model: How It Works and When to Use It

The time decay attribution model assumes the interactions closest to a conversion deserve the most credit, while earlier touches still matter but count for less.

Time decay attribution is one such model.

What the Time Decay Attribution Model Measures

Time decay attribution is a multi-touch model that assigns more credit to touchpoints closer to the conversion. The more recent the touchpoint, the greater the weight.

The model assigns credit based on a half-life you set. If your half-life is seven days, a touchpoint that occurred a week before conversion receives half the credit as one that occurred on the day of conversion. More than two weeks out, it receives a quarter.

An Example

Consider a hypothetical journey over three weeks:

• Day 1: Blog post about business financing (21 days out)
• Day 8: Email newsletter highlighting rates (14 days out)
• Day 15: Product comparison page visit (7 days out)
• Day 22: Direct return and application submission

Touchpoint	Days before conversion	Normalised weight	Attributed value
Blog post (organic search)	21	7.73%	$773
Email newsletter	14	15.45%	$1,545
Product comparison page	7	30.90%	$3,090
Application page (direct)	0	45.92%	$4,592

The application page and comparison page receive the largest share because they were closest to the decision. But the blog post and email also get credit.

Comparing Attribution Models

Model	Type	Credit distribution
Last-click	Single-touch	100% to final touchpoint
First-click	Single-touch	100% to first touchpoint
Linear	Multi-touch	Equal credit to all touchpoints
Position-based (U-shaped)	Multi-touch	40% first, 40% last, 20% split across middle
Time decay	Multi-touch	Weighted by recency
Algorithmic (data-driven)	Multi-touch	Weighted by statistical analysis

Last-Click Attribution

Assigns 100% to the final touchpoint. Simple to report on but ignores every earlier interaction. Best for short sales cycles.

First-Click Attribution

Assigns all credit to the first touchpoint. Spotlights awareness channels but ignores what happened afterwards. Best for brand awareness campaigns.

Linear Attribution

Distributes credit equally across all touchpoints. Balanced but treats a casual blog visit the same as a demo request. Best for understanding channel health in long nurture cycles.

Position-Based (U-Shaped) Attribution

Assigns 40% to first, 40% to last, 20% across the middle. Balances awareness and conversion. Best for B2B environments.

Algorithmic (Data-Driven) Attribution

Uses machine learning for the most nuanced view but requires large data volumes. Best for enterprises with high traffic and technical resources.

Benefits of Time Decay Attribution

Gives a Better Picture of the Customer Journey

It looks at the entire journey, weighting credit based on timing. This helps stakeholders see the whole picture.

Supports Long Sales Cycles

It handles long journeys well, crediting late-stage touchpoints without completely ignoring earlier ones.

Three Limitations

1. It Undervalues Early Interactions

Top-of-funnel activity receives the least credit, even if it was the reason a prospect found you.

2. It Is Difficult to Find the Optimal Half-Life

Most platforms default to seven days, but this is arbitrary. You need to adjust based on your sales cycles.

3. It Is Misaligned with Long-Term Strategy

It favours short-term optimisation and may cause over-investment in bottom-of-funnel channels.

Choosing the Right Model

Ask yourself:

• Does your sales cycle span multiple weeks?
• Are you optimising bottom-of-funnel performance?
• Do you need a middle-ground approach?
• Do you need to justify marketing spend to stakeholders?
• Are most conversions influenced by multiple channels?

Time decay attribution is also useful when combined with another model -- for instance, running first-click alongside it to see which channels introduce prospects versus which ones close them.