Webinar: Beyond Effect Sizes — Using BayesiaLab's Target Dynamic Profile
Stefan Conrady, Bayesia USA
Context
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 the cost, for now, we would want to reduce the wait time as our top priority, right? Maybe not.
Joint Probability
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 to identify the sequential order in which the key drivers should be improved to maximize overall customer satisfaction.
Presentation Video
Presentation Materials
2019-11-13-Target-Dynamic-Profile.pdf
About the Presenter
Stefan Conrady has over 20 years of experience in decision analysis, analytics, market research, and product strategy with Mercedes-Benz, BMW Group, Rolls-Royce Motor Cars, and Nissan, which included assignments in North America, Europe, and Asia.
Today, in his role as Managing Partner of Bayesia USA and Bayesia Singapore, he is recognized as a thought leader in applying Bayesian networks for research, analytics, and reasoning.
Recently, Stefan and his colleague Dr. Lionel Jouffe co-authored Bayesian Networks & BayesiaLab — A Practical Introduction for Researchers, which is now available as an e-book.
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