Understanding Segment-specific Key Drivers to Guide Strategy: The Impact of Value on Key Drivers of Relative Satisfaction

Abstract

The purpose of this research is to establish a procedure for identifying the distinct key drivers of relative satisfaction for customer segments defined in terms of value. A survey approach was used to elicit 3,793 brand ratings from 693 customers who were representative of a bank’s customer base. Segment-specific key drivers of relative satisfaction were determined for both user-defined segments and segments determined through latent class analysis (LCA) to compare the adequacy of both methods. Subjective approaches to value-based segmentation provide an adequate, but suboptimal basis for determining segment-specific key drivers of relative satisfaction. Segments derived from LCA provide an optimized basis for identifying a customer base’s heterogeneous key drivers of relative satisfaction. Firms would benefit from segmenting customers on the basis of customer value when trying to identify key drivers of relative satisfaction. Thus, resources can be strategically allocated to affect the drivers of relative satisfaction that are unique to a firm’s most valuable customers. While LCA provides an optimized basis for this segmentation, utilizing easier-to-apply user-defined value-based segments is preferable to performing key drivers analysis (KDA) without segmentation. This work makes several important substantive and theoretical contributions. We highlight the need to apply segmentation to KDA by showing that value-based segments have distinct key drivers of relative satisfaction. We establish that oft-used practices for identifying value-based segments are useful in KDA, but that LCA provides the optimized basis for segmentation. Finally, we add to a growing literature encouraging the application of KDA to relative metrics.