The node representing “Will Not Stay” draws our focus toward the left side of the following undirected graph. Customers of a health care insurance provider were asked about their intentions to renew at the next sign-up period. We focus on those indicating the greatest potential for defection by creating a binary indicator separating those who say they will not stay from everyone else. In addition, before telling us whether or not they intended to switch health care providers, these customers were given a checklist and instructed to check all the events that recently occurred (e.g., price increases, higher prescription costs, provider not covering all expenses, hospital and doctor visits, and customer service contacts).
To be clear, all we have are customer perceptions. There is no electronic record of price increases, claim rejections, direct billings by MDs or hospitals, a customer service contact, or doctor and hospital visits. That is, we do not have measures of the event occurrences that are independent of defection intention. Consequently, we have no justification for drawing an arrow from Premiums Increases to Will Not Stay because the decision to churn impacts the willingness to check the Premium Up box. For example, everyone in the United States is likely to see some increase in their premiums, yet your willingness to check “yes” may depend on what else has occurred in your relationship with the insurance provider. Those wanting to remain dismiss the price increase as inflation or reframe it as essentially the same price, while those thinking of flight are more likely to take notice and affront. It might help to think of this as a form of cognitive dissonance or simply selective attention. Regardless of the specifics of the cognitive and affective processes, the result is an undirected graph with every node is both an outcome and a predictor.
The thickness of the lines indicate the strength of the connections. These edges represent the relationship between nodes controlling for all the other nodes in the graph. A checklist was provided so that all we have is a data matrix with either yes (=1) or no (=0). As I explained above, the only rating scale was dichotomized into Will Not Stay versus any other response. The data are proprietary so that all I can tell you is that there were more than a thousand customers, and each row was a profile of 11 binary variables coded zero or one. On the other hand, I can share the four lines of R code needed to run the analysis using the IsingFit R package and a data frame called “events2” with 11 columns and lots of rows containing only zeros and ones (see the end of this post). In addition, I can provide the link to an comprehensive overview of the methodology, A New Method for Constructing Networks from Binary Data. Those seeking more will find the notes from Sacha Epskamp workshop very helpful.
Getting back to our network, it seems that when Premiums go up, so do Deductibles and Co-pays. Cost increases form a clique near the bottom of the graph with edges suggesting that anticipated defection co-varies with price increases. A similar effects can be seen for prescription costs near the top. However, nothing seems to encourage exit more than a provider’s failure to pay. Or, at least those who will not stay checked the box associated with the provider not paying. Moreover, we can observe some separation and independence in this undirected graph. Visiting your doctor, a specialist or going to the hospital have positive connections to customer churn only through the receipt of a bill or a customer service contact.
Hopefully, this example demonstrates that a lot can be learned from a undirected graphical representation of dichotomous survey data. Bayesian networks, more correctly called directed graphs, seem to attract a good deal of attention in marketing (e.g., BayesiaLab), as do structural equations models (see my previous post on Undirected Graphs When the Causality Is Mutual). In fact, my first post in this blog, Network Visualization of Key Driver Analysis, demonstrates how much can be summarizes quickly and clearly in an undirected graph. Another post, Metaphors Matters, compares factor analysis and correlation network maps.
To be clear, a graph displays an adjacency matrix that can contain any measure, often an index of association, affiliation or affinity. Any similarity or distance matrix can be graphed. Thus, we need to be careful when we interpret the resulting graphs. In this case, the adjacency matrix contained the averaged coefficients from a sparse logistic regression with each node as the dependent variable and all the remaining nodes as predictors. This means that our graph is not a correlation network because the adjacency matrix does not contain correlations. It is more like a partial correlation network, except that the adjacency matrix does not contain partial correlations but something that can be interpreted like a partial correlation. Fortunately, you can work with the graph as representing the relationship between two nodes controlling for the rest while you learn the details of Ising discrete data graphing.