Utilizing relative weight analysis in customer satisfaction research

Michael S. Garver, Zachary Williams

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


For customer satisfaction researchers, key driver analysis is a common practice to understand what product and service attributes are most important in driving the overall customer experience, typically measured by overall satisfaction or the Net Promoter question. To implement key driver analysis, market research practitioners often use statistical techniques such as bivariate correlation analysis or multiple regression, yet these statistical techniques have severe limitations for conducting key driver analysis. As a viable alternative, this article puts forth relative weight analysis (RWA) as an appropriate statistical technique for conducting key driver analysis. To empirically demonstrate this technique, key driver analysis was conducted using data from a B2B software provider. The analysis implemented RWA, correlation analysis, and multiple regression, and the results are compared and contrasted. RWA limitations and best practices are discussed as well as future research. Overall, this research advocates for more use of RWA in marketing research.

Original languageEnglish
Pages (from-to)158-175
Number of pages18
JournalInternational Journal of Market Research
Issue number2
StatePublished - Mar 1 2020


  • customer satisfaction
  • key driver analysis
  • relative weight analysis


Dive into the research topics of 'Utilizing relative weight analysis in customer satisfaction research'. Together they form a unique fingerprint.

Cite this