In order for you to succeed with your attribution, you must know of all interaction points your customer may encounter before they are weighed and evaluated. You can do this with a variety of rule-based or data-driven attribution models.
The Most Common Attribution Models
This attribution model allocates all value to the channel and ad that the customer last clicked on before conversion. No account is taken of the customer’s previous interactions with the company, which may have paved the way for the last click.
Here, the value of the conversion is distributed to the first interaction the customer took in the customer journey. The attribution model does not take into account subsequent interactions that may have affected the customer’s purchase.
Here, all the interactions a visitor had throughout their customer journey is given the same value.
This attribution model adds more value to the steps closest to the conversion in time.
In this attribution model, the first and last interactions are given 40% each of the value and the remaining 20% is distributed over the other interactions.
A data-driven attribution model, in contrast to the others we have discussed above, is not based on one or more predetermined rules. Machine learning analyzes interactions and customer journeys from your historical data and uses this to create a model tailored to you.