Hello Dr. Jay,
I enjoyed your recent post on predictive analytics that mentioned Bayesian Networks.
Could you explain Bayesian Networks in the context of survey research? I believe a Bayes Net says something about probability distribution for a given data set, but I am curious about how we can use Bayesian Networks to prioritize drivers, e.g. drivers of NPS or drivers of a customer satisfaction metric.
Driver modeling is an interesting challenge. There are 2 possible reasons why folks do driver modeling. The first is to prioritize a set of attributes that a company might address to improve a key metric (like NPS). In this case, a simple importance ranking is all you need. The second reason is to determine the incremental change in your dependent variable (DV) as you improve any given independent variable by X. In this case, we’re looking for a set of coefficients that can be used to predict the dependent variable.
Why do I distinguish between these two things? Much of our customer experience and brand ratings work is confounded by multi-collinearity. What often happens in driver modeling is that 2 attributes that are highly correlated with each other might end up with 2 very different scores—one highly positive and the other 0, or worse yet, negative. In the case of getting a model to accurately predict the DV, I really don’t care about the magnitude of the coefficient or even the sign. I just need a robust equation to predict the value. In fact, this is seldom the case. Most clients would want these highly correlated attributes to yield the same importance score.
So, if we’re not interested in an equation to predict our DV, but do want importances, Bayes Nets can be a useful tool. There are a variety of useful outputs that come from Bayes Nets. Mutual information and Node Force are two such items. Mutual information is essentially the reduction in uncertainty about one variable given what we know about the value of another. We can think of Node Force as a correlation between any 2 items in the network. The more certain the relationship (higher correlation), the greater the Node Force.
The one thing that is relatively unique to Bayes Nets is the ability to see if the attributes are directly connected to your key measure or if they are moderated through another attribute. This information is often useful in understanding possible changes to other measures in the network. So, if the main goal is to help your client understand the structure in your data and what items are most important, Bayes Nets is quite useful.
Dr. Jay Weiner is CMB’s senior methodologist and VP of Advanced Analytics. Jay earned his Ph.D. in Marketing/Research from the University of Texas at Arlington and regularly publishes and presents on topics, including conjoint, choice, and pricing.