Key driver analysis is a broad term used to cover a variety of analytical techniques. It always involves at least one dependent or criterion variable and one or (typically) multiple independent or predictor variables whose effect on the dependent variable needs to be understood. The dependent variable is usually a measure on which the manager is trying to improve the organization’s performance. Examples include overall satisfaction, loyalty, value, and likelihood to recommend.

When conducting a key driver analysis, there is a very important question that needs to be considered: Is the objective of the analysis explanation or prediction? Answering this question before starting the analysis is very useful because it helps in choosing not only the analytical method to be used but also, to some extent, the choice of variables. When the objective of the analysis is explanation, we try to identify a group of independent variables that can explain variations in the dependent variable and that are actionable. For example, overall satisfaction with a firm can be explained by attribute satisfaction scores. By improving the performance on those attributes identified as key drivers, overall satisfaction can be improved. If the predictors used are not actionable, then the purpose of the analysis is defeated.

In the case of prediction, we try to identify variables that can best predict an outcome. This is different from explanation because the independent variables here do not have to be actionable, since we are not trying to change the dependent variable. As long as the independent variables can be measured, predictions can be made. For example, in the financial services industry, it is important to be able to predict (rather than change) the creditworthiness of a prospective customer from the customer’s profile. 

Beyond the issue of explanation versus prediction, there are two other questions that help in the choice of analytical technique to be used:

1. Is there one, or more than one, dependent variable?

2. Is the relationship being modeled linear or nonlinear?

In the remainder of this article we will discuss analytical methods that would be appropriate if one or both of these questions is answered in the affirmative.

Questions

1. What is key driver analysis? What role does regression analysis play in this type of analysis?

2. How can you use the results from key driver to improve, say, customer satisfaction? Explain.