Beyond Effect Sizes — Using BayesiaLab's Target Dynamic Profile
Common sense suggests that we always choose the activity with the strongest positive effect on the desired outcome as our top priority for action. For instance, in analyzing call center performance, a statistical model may suggest that the average wait time has the strongest effect on callers' overall satisfaction. With that, and ignoring cost for now, we would want to reduce the wait time as our top priority, right? Maybe not.
The critical concept to consider here is joint probability. Unfortunately, in many modeling frameworks, this quantity does not even appear. Hence, any optimization effort would not be able to utilize the joint probability in determining the order of priorities.
Modeling a problem domain with Bayesian networks and BayesiaLab, however, one can calculate the joint probability and use it for optimization purposes. BayesiaLab performs this particular type of optimization with its Target Dynamic Profile function.
Example: Key Drivers Analysis
In this webinar, we illustrate how we can use Target Dynamic Profile for identifying the sequential order in which the key drivers should be improved for maximizing overall customer satisfaction.