With OWOX BI Attribution, you can build a Funnel-Based attribution model. The main principle of this model is: the more difficult a funnel step to pass the more valuable the efforts you make to help a user to pass this step.
Applying the Funnel-Based model, you can evaluate each step of your users on their way to the conversion into a customer, understand how the funnel steps affect each other, and evaluate the efforts that you can track using standard Google Analytics tools.
In this article, we show how OWOX BI Attribution deals with evaluating nonlinear conversion funnels and repeated funnel actions.
Evaluating nonlinear funnels
OWOX BI calculates the probability of a visitor to move to a funnel step not only from the previous step, but from any step in the funnel.
For example, a user moved from the 1st step straight to the 3rd one. To calculate the value of the 3rd step, we use the probability of this particular transition, not the transition from the previous step.
This approach is used with the transitions in the opposite direction through the funnel as well, for example, when a user moves to the 2nd step from the 3rd one.
The plenty of different funnels also means that it's not necessary to pass through all the funnel steps. Therefore, one action should not get credit for the contribution of another action.
Following that principle, we distribute all the conversion value only among the steps present in the user's path.
For example, if a user skips some of the first steps and starts with the 4th step, and goes to the 5th, then the conversion value will be distributed between the 1st, the 4th, and the 5th steps.
The logic behind the steps value calculation is described in the article “Set up funnel steps”
Evaluating repeated funnel actions
Users often repeat the same action multiple times. For example, they view multiple product pages or read various blog posts.
Every repeated action influences probability of the customer’s journey through the conversion funnel. The closer action to the conversion, the more influence it has.
We attribute value to all the repeated actions with time decay logic.
First, we measure the value of the step itself. Then we count all the actions of the step and distribute the value of the step among the actions considering how close to conversion they were.