Editor's note: Michael S. Garver is a market research consultant and professor of marketing at Central Michigan University, Mt. Pleasant, Mich. 

Identifying attribute importance is a critical objective of customer experience (CX) research, and key driver analysis (KDA) is often conducted to achieve this objective. To implement KDA, researchers statistically infer the importance of product and service attributes that “drive” overall customer satisfaction. KDA results help prioritize the most important attributes to customers, which will influence strategic plans and the priority of improvement efforts. If KDA results are biased, then practitioners may invest scarce resources to strategically leverage or improve the wrong attribute.

There are a number of KDA research issues and limitations that can bias KDA results, yet many CX researchers ignore them or are simply not aware of their existence. The purpose of this article is to put forth KDA best practices to help CX researchers obtain more accurate and valid KDA results. 

To accomplish this, key aspects of defining a CX model are put forth, which includes defining attributes at different levels of abstraction as well as identifying unique and relevant dependent variables for KDA. Then, recommended statistical techniques (i.e., relative weight analysis, dominance analysis and correlated components regression) are introduced that overcome the problems of multicollinearity and deliver more accurate and valid KDA results. Then, a multistep KDA approach designed to overcome the problems of attribute redundancy is introduced, along with a KDA process to incorporate multiple dependent variables. Finally, the importance of segmentation in the KDA process is addressed. 

Typically, CX research objectives are to identify attribute importance, satisfaction and improvement opportunities. To fulfill these objectives, most CX surveys start by asking survey re...