Linear Attribution Calculator
Distribute conversion credit equally across all touchpoints with the linear attribution model. Enter value and touchpoints to see per-touch credit.
Time-Decay Attribution Calculator
Calculate conversion credit using time-decay attribution. Give more credit to touchpoints closer to the conversion using exponential decay weighting.
days
days
Revenue per Conversion⧉
$200.00
100.00 conversions generating $20,000.00
Last Touch Credit⧉
46.1%
Most recent touchpoint receives the highest weight in decay models
First Touch Credit⧉
2.4%
Earliest touchpoint, furthest from conversion event
Decay Ratio⧉
19.21x
Ratio of last-touch to first-touch credit attribution
Recent Half Credit⧉
$17,119.81
85.6% of revenue credited to the most recent half of touchpoints
Highest Credit Touchpoint⧉
TP #6
46.1% credit ($9,220.07 total)
Touchpoint Contribution
TP #1 (30d before)2.4% — $472.72
TP #2 (24d before)4.3% — $856.31
TP #3 (18d before)7.8% — $1,551.16
TP #4 (12d before)14% — $2,809.85
TP #5 (6d before)25.4% — $5,089.89
TP #6 (0d before)46.1% — $9,220.07
Touchpoint Attribution Table
| Touchpoint | Days Before | Weight | Credit % | Credit/Conv | Total Credit |
|---|---|---|---|---|---|
| #1 | 30 | 0.0236 | 2.4% | $4.73 | $472.72 |
| #2 | 24 | 0.0428 | 4.3% | $8.56 | $856.31 |
| #3 | 18 | 0.0776 | 7.8% | $15.51 | $1,551.16 |
| #4 | 12 | 0.1405 | 14% | $28.10 | $2,809.85 |
| #5 | 6 | 0.2545 | 25.4% | $50.90 | $5,089.89 |
| #6 | 0 | 0.4610 | 46.1% | $92.20 | $9,220.07 |
Decay Model Comparison
| Touchpoint | Days Before | Exponential | Linear | Custom Half-Life |
|---|---|---|---|---|
| #1 | 30d | 2.4% | 0% | 2.4% |
| #2 | 24d | 4.3% | 6.7% | 4.3% |
| #3 | 18d | 7.8% | 13.3% | 7.8% |
| #4 | 12d | 14% | 20% | 14% |
| #5 | 6d | 25.4% | 26.7% | 25.4% |
| #6 | 0d | 46.1% | 33.3% | 46.1% |
Planning notes, formulas, and examples
About the Time-Decay Attribution Calculator
Time-decay attribution gives more credit to marketing touchpoints that occur closer to the conversion event. Using an exponential decay function with a configurable half-life, this model recognizes that recent interactions typically have more influence on the purchase decision than earlier ones.
The half-life parameter determines how quickly credit decays: a 7-day half-life means a touchpoint 7 days before conversion receives half the weight of the last touchpoint in the final day. Touchpoints 14 days out receive one-quarter the weight, and so on. This creates a smooth gradient of credit that still recognizes earlier interactions.
Time-decay is particularly effective for businesses with considered purchase cycles where customers research over days or weeks before buying. It balances the need to credit closing interactions more heavily while still acknowledging the awareness and consideration stages of the journey.
Understanding this metric in precise terms allows marketing professionals to set realistic goals, track progress effectively, and refine their approach based on real performance data.
When This Page Helps
When recency drives conversion probability, time-decay attribution provides the most accurate credit distribution. It's ideal for considered purchases where customers research over time and the most recent interactions have the most influence on the final decision.
How to Use the Inputs
- Enter the conversion value.
- Enter the number of touchpoints in the customer journey.
- Set the half-life (in days) for the decay function.
- Enter the days before conversion for each touchpoint.
- Review the credit each touchpoint receives based on its recency.
- Adjust the half-life to see how different decay rates change the distribution.
Formula used
Weightᵢ = 2^((tᵢ − t₀) / half-life)
Normalized Weightᵢ = Weightᵢ / ΣWeights
Creditᵢ = Normalized Weightᵢ × Conversion ValueExample Calculation
Result: Touch 1: $26.67 | Touch 2: $53.33 | Touch 3: $106.67 | Touch 4: $213.33
With a 7-day half-life and 4 touchpoints at 21, 14, 7, and 1 day(s) before conversion, the weights are approximately 1, 2, 4, and 8. After normalization, the last touch gets 53.3% of credit and the first touch gets 6.7%, reflecting the exponential decay pattern.
Tips & Best Practices
- A 7-day half-life works well for e-commerce; B2B may need 14–30 days.
- The shorter the half-life, the more the model resembles last-click attribution.
- A very long half-life makes time-decay approach linear attribution.
- Test different half-lives against actual conversion data to find the best fit.
- Time-decay is the best single model for mid-length considered purchase journeys.
- Combine with position-based for a model that values both recency and first touch.
The Mathematics of Time-Decay Attribution
Time-decay uses an exponential function to weight touchpoints by their proximity to conversion. The formula 2^(t/half-life) creates a smooth curve where credit doubles for every half-life period closer to conversion. This mathematical elegance makes it both intuitive and rigorous.
Choosing the Right Half-Life
The half-life is the most important parameter in time-decay attribution. Too short and you essentially replicate last-click attribution. Too long and you approach linear attribution. The ideal half-life reflects your actual customer journey dynamics — use conversion lag data to inform this choice.
Practical Considerations
Time-decay works best when combined with sufficient tracking data. You need timestamp information for each touchpoint to calculate accurate weights. Ensure your analytics platform captures interaction times, not just the channel. Cross-device tracking gaps can introduce errors in the timing data.
Sources & Methodology
Last updated:
Frequently Asked Questions
Time-decay attribution is a multi-touch model that assigns exponentially more credit to touchpoints closer to the conversion. It uses a half-life parameter to control the decay rate, ensuring recent interactions receive substantially more credit than earlier ones.
The half-life determines how quickly credit decays over time. With a 7-day half-life, a touchpoint 7 days before conversion gets half the weight of the most recent touchpoint. Shorter half-lives concentrate credit on recent touches; longer ones distribute it more evenly.
Use time-decay for considered purchases with multi-day research cycles, such as SaaS, automotive, financial services, and B2B products. It's particularly effective when your data shows that recent interactions are more correlated with conversion than earlier ones.
Time-decay emphasizes recency uniformly. Position-based specifically credits first and last touches 40% each. Time-decay may undervalue the first awareness touch, while position-based guarantees it 40% credit. Choose based on whether recency or funnel position matters more.
Start with 7 days for e-commerce, 14 days for high-consideration B2C, and 21–30 days for B2B. Then validate against your data: compare time-decay channel valuations with different half-lives to see which best matches observed conversion patterns.
Google Analytics 4's default data-driven attribution incorporates time-decay elements. The legacy Universal Analytics offered an explicit time-decay model. You can also calculate time-decay attribution using exported path data in a spreadsheet or data warehouse.
More in this topic
Position-Based Attribution Calculator
Calculate conversion credit using position-based (U-shaped) attribution. 40% to first touch, 40% to last touch, 20% split among middle touchpoints.