Complementing eNPS with cluster analysis using multiple dimensions

Abstract

The NPS (Net Promoter Score) is a marketing research metric that asks respondents to rate the likelihood of them recommending a given product or service to others. Employee loyalty is assessed by how often they recommend their workplace to others, and the Employee Net Promoter Score (eNPS) is used to track this. eNPS is built on the same principle as NPS, which was developed by Fred Reichheld, Bain & Company, and Satmetrix in 2003 (PeoplePulse Marketing, 2019). eNPS being a unidimensional score, there is a limited scope of analysis for the sake of improvement. Hence, there is a need for additional dimensions to aid in analyzing employee perception and address the concerns accordingly. We intend to research and discover the numerous factors that influence employee loyalty. Instead of using eNPS’s unidimensional data, multi-dimensional metrics can be used to analyze the score more effectively. Clusters will be identified using the new metrics. After that, we will correlate the clusters with eNPS data which will aid in deciphering the finer nuances of employee loyalty, and provide a comprehensive picture.