Attribution
Data-driven model
How TrustData's Data-Driven attribution distributes credit fairly.
Data-Driven attribution uses your actual conversion data to distribute credit fairly among marketing touchpoints.
How it works
Instead of applying arbitrary rules (like giving 100% to the last click), Data-Driven attribution analyzes your conversion paths to determine each channel's true contribution.
Example
A customer journey:
- Clicks Google ad (Day 1)
- Sees Meta retargeting ad (Day 5)
- Opens email, converts (Day 7)
Last-click gives Email 100% credit. But would they have converted without the Google ad that started the journey?
Data-Driven analyzes thousands of similar paths to calculate:
- Google: 35%
- Meta: 25%
- Email: 40%
Why it's better
| Model | How It Works | Problem |
|---|---|---|
| Last-click | 100% to final touchpoint | Ignores awareness channels |
| First-click | 100% to first touchpoint | Ignores converting channels |
| Linear | Equal split | Ignores actual impact |
| Time decay | More to recent | Arbitrary decay rate |
| Data-Driven | Based on your data | None—uses real patterns |
What TrustData considers
Conversion patterns
TrustData analyzes which channel combinations lead to conversions:
- Paths with Google + Meta convert at 45%
- Paths with only Meta convert at 15%
- Google's incremental impact: +30%
Position in journey
Where a touchpoint appears matters:
- First touch: Awareness and discovery
- Middle touches: Consideration and research
- Last touch: Decision and conversion
Time to conversion
Recent touchpoints often have more influence, but early touchpoints set the stage.
Comparing attribution models
| Model | Pros | Cons |
|---|---|---|
| Last-click | Simple | Ignores assists |
| First-click | Values awareness | Ignores closers |
| Linear | Everyone gets credit | Ignores impact |
| Time decay | Recency matters | Arbitrary weights |
| Position-based | Balances first/last | Still arbitrary |
| Data-Driven | Based on real data | Needs conversion volume |
Reading attribution reports
In TrustData dashboards:
| Metric | Meaning |
|---|---|
| Attributed Revenue | Sum of (conversion value × channel credit) |
| Attributed Conversions | Sum of channel credits (fractional) |
| ROAS | Attributed Revenue / Ad Spend |
| CPA | Ad Spend / Attributed Conversions |
Example
- Conversion value: $100
- Google credit: 60%
- Meta credit: 40%
Result:
- Google's attributed revenue: $60
- Meta's attributed revenue: $40