Measuring Customer Experience Management And Its Impact On Financial Performance

Purpose With the considerable attention given to customer experience (CX) today, customer experience management (CXM) has been touted as one of the most promising management approaches for organizations. The purpose of this paper is threefold: develop a scale to measure the CXM construct, investigate the financial outcomes of CXM and assess the impact of moderator variables (e.g. market turbulence) on these financial outcomes while accounting for the effects of control variables (e.g. firm size). Design/methodology/approach The study involves a survey of 233 firms (across 10 industries) involved in CXM. Confirmatory factor analysis (CFA), structural equation modeling (SEM), instrumental variables and moderated regression analyzes are used to test four hypotheses. Findings The results support treating CXM as a second-order construct comprising three dimensions: cultural mindset toward CXs, strategic directions for designing CXs and firm capabilities of continually renewing CXs. Furthermore, CXM is positively related to financial performance; this effect increases as market turbulence, competitive intensity and technological turbulence increases. Research limitations/implications With our CXM measure, future research can advance CXM theory by examining other outcome variables (e.g. employee satisfaction) and moderators (e.g. culture), as well as introduce antecedents to CXM (e.g. company heritage). Limitations include the concerns normally associated with using self-reported measures of performance, convenience samples and cross-sectional designs. Practical implications This research provides managerial prescriptions of when to invest in CXM initiatives to enhance financial performance. It also provides managers a CXM diagnostic to help assess their level of CXM maturity. Originality/value This paper develops CXM theory by advancing a measure of the CXM construct, relating the construct to an outcome variable (main effect) and introducing moderating variables to shed light on the generalizability of the main effect.