Definition
Email blended attribution is an approach to credit assignment that combines multiple attribution models into a single, weighted result. Instead of relying on one method — such as last-click — it averages or weights the outputs of several models to produce a more balanced view of email's contribution to revenue. Blended attribution is used to reduce the bias inherent in any single attribution model.
How It Works
Every attribution model has blind spots. Last-click credit overstates the final touchpoint, first-click credit overstates discovery, and linear credit spreads value evenly regardless of true influence. Blended attribution smooths these biases.
- Model selection — the practitioner chooses several models, such as last-click, first-click, linear, and position-based.
- Weighting — each model's output is assigned a weight reflecting confidence in its accuracy.
- Combination — the weighted outputs are summed to produce a single, blended credit figure for email.
Blended attribution is closely related to email-attribution and provides a practical middle ground when experimental measurement is unavailable, though it remains an estimate rather than a measurement.
How to Calculate
Build a blended attribution model in four steps:
- Run multiple models — compute email's credited revenue under each chosen model.
- Assign weights — give each model a weight, with weights summing to 100%.
- Multiply — multiply each model's credit by its weight.
- Sum — add the weighted credits to get the blended figure.
Blended Credit = Sum of (Model Credit x Model Weight)
| Variable | Description |
|---|---|
| Model Credit | Revenue credited to email by a single model |
| Model Weight | Assigned importance of that model (sums to 1) |
Example
A marketer runs three models on a campaign: last-click credits email £30,000, linear credits £45,000, and position-based credits £40,000. Weighting them at 30%, 40%, and 30% respectively yields a blended credit of £39,000, a balanced figure used for internal reporting.
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Frequently Asked Questions
No single model is correct for every journey. Blending reduces the bias of any one model and produces a more defensible, balanced estimate of email's contribution.
Weights should reflect confidence in each model, ideally informed by experimental lift data. In the absence of experiments, equal weighting is a common and defensible starting point.
It is an estimate. True incrementality requires control groups or experiments, which blended attribution approximates but cannot replace. It is most valuable where experimentation is impractical.
Multi-touch attribution is a family of models that distribute credit across touchpoints, while blended attribution combines the outputs of several such models into one weighted result. Blending is applied on top of multi-touch models.