When building an attribution report, you have the below attribution models to choose from.
This guide will illustrate all models using the example: Google Ads Touchpoint → Facebook Ads Touchpoint → Google Ads Touchpoint → SDR Touchpoint → Opportunity Created ($10k)
First Touch: All value is assigned to the first touch.
Example above:
Last Touch: All value is assigned to the last touch.
Example above:
Linear: The total value is divided and distributed equally among all touches.
Example above:
Position-Based: The first and last touches get 40% of the total value each, and the remaining 20% is distributed among the remaining touches.
Example above:
Uniform: The total value is assigned to each touch.
Example above:
Note: The total attributed value doesn’t add up to the total actual value when using the Uniform attribution model.
Time Decay: The time decay model is another model where the total value attributed doesn’t add up to the total actual value. It is designed to attribute less value to a touchpoint the further away it’s from the conversion action. Specifically, the value halves every 7 days.
For fellow nerds, the formula is $v \times 2^{-{i\over7}}$ (where $v$ is total value and $i$ is days from the conversion action).
Note: The total attributed value doesn’t add up to the total actual value when using the Time Decay attribution model.
Predictive: HockeyStack’s proprietary machine learning attribution model.
Linear is a credited attribution model. It’s used to signify a comparison between the level of influence that each touchpoint has on the final conversion action.
Uniform is not a credited attribution model. It’s used to signify that the influence exists, regardless of its level.
Uniform is most commonly used when looking at:
“Qualified touchpoints” depend on the report breakdown you select.