If you check the engagement policies at the action level, notice that there are additional suitability conditions. When the Suitability group-level condition is applied, 215 customers qualify for the offers out of the 344 customers. The output population of the applicability level, 344 customers, is passed on as the input to the suitability level. Note, since there are no specific action-level applicability conditions, the result of the individual group-level condition is the same as that of the overall applicability component level, 344. When the applicability condition is applied, 344 customers qualify for at least one offer out of the 979 customers. The input population considered for the applicability condition is therefore 979. The output population of the eligibility level is passed on as the input to the next level. If there are, the final result is the result of the eligibility conditions at the group level, combined with the results at the action level. In this case, there are no eligibility conditions defined at the action level. Thus, the intersection of these two conditions, 979, is the final number of customers who pass through to the eligibility level. When the second condition is applied, only 979 customers pass through. You can still pass the TOEFL whether youre an expert or not. Within the eligibility criteria, the first condition does not filter any customers, as all qualify. The TOEFL exam is taken by more than 30 million people.
When the eligibility condition is applied, 979 customers qualify for the offers. This simulation run also provides details of the audience filtration that happens with each engagement policy condition. For this audience, the number of customers who received offers is 182.
In this case, the total number of customers in the audience is 1000. However, within an engagement policy condition type, each of the criterion is applied separately to the corresponding input audience. The filtering process that happens in this simulation is similar to a funnel filtration for every engagement policy condition type. For example, if the result of a criterion that checks if the action is active returns 100%, the component did not filter out any audience members. Robotic Process Automation Design Patternsįor each component of the engagement policy, the simulation test shows a numerical or percentage value of the audience that will receive the action based on current criteria.