# Atribution models

Here are the attribution models offered by default:
Last Touch Point: The conversion is attributed to the last paid or natural touchpoint in the 30 days before conversion. In the example below, the conversion is attributed to “SEO Google” with the Last Touch Point model, provided the “SEO Google” touchpoint was in a 30-hour attribution window: First Touch Point: The conversion is attributed to the first paid or natural touchpoint in the 30 days before conversion. In the example below, the conversion is attributed to “SEM Google” with the First Paid Click model, provided the “SEM Google” touchpoint was in a 30-day attribution window: Last Touch (Paid over Natural): The conversion is attributed to the last paid touchpoint in the 30 days before conversion. If the customer journey contains no paid clicks or impressions, the conversion is attributed to the last natural touchpoint in the 30 days before conversion. In the example below, the conversion is attributed to “Affiliation Tradedoubler” with the Last Touch (Paid over Natural) model, provided the “Affiliation Tradedoubler” touchpoint was in a 30-day attribution window: In the second example, the conversion is attributed to “SEO Google” with the Last Touch (Paid over Natural) model, provided the ” SEO Google ” touchpoint was in a 30-day attribution window: Linear flat: The conversion is attributed to all the touchpoints in the customer journey.  Each touchpoint receives an equitable percentage of the conversion.
First and Last 50/50 : the first touchpoint and the last one share1 sale (or the amount of the sale). U model : • Mathematical U model based on mathematical formulas (parabolic, elliptic …).100% of the conversion needs to be allocated U MODEL – 4 channels U MODEL – 2 channels
U model personnalized : The conversion is attributed to all the touchpoints in the customer journey.
This model purpose is to encourage simultaneously to increase sales and to increase traffic. We define how much will earn the first and last touch (it can be different) with a scoring in percent and all touchpoints in the middles earn a proportional part of the rest. Same example with 35% for the first touch and 45% for the last one. Note: if there is only 2 touch, the percent in the middle are equally divided between each touch. If there is only one touch, it gets 100% of the sale   Linear increasing/decreasing : user defines a static value increasing the importance of touchpoint accordingly to its position into the conversion path. For example: we define that each touchpoint will earn 1.2 points of the sale according to its position and there are 6 touchpoints. • That model uses the following function: y=ax, where y = value, x = touchpoint and a = the growing coefficient. The absolute value determines the importance of close touchpoints or remote touchpoints. • Then the scoring is applicated regarding the share of each touchpoint against the value total.  Linear Increasing Linear Decreasing
Exponential increasing (or logarithmic decreasing) exactly the same as previously but instead of defining a static growth coefficient, we use an exponential formula (exp(x) =ex). The same example as before:  Exponential Increasing Exponential Decreasing
Custom : Possible to define the number of touch point to take into account and the weight to assign to each. Interface configuration example: 