Profiting from customer relationship management

Abstract

Purpose – This study aims to examine the direct and moderating effects of generative learning on customer performance. Design/methodology/approach – The authors test the relationships between customer relationship management (CRM) capabilities, generative learning, customer performance, and financial performance with a cross industry survey of CEOs and senior marketing executives from 199 firms. Partial least squares are used to estimate the parameters of the resulting model. Findings – The results reveal that generative learning affects customer performance directly. Moreover, the interaction of CRM capabilities and generative learning contributes to customer performance. This finding suggests that firms need a well-developed generative learning orientation to fully benefit from translating new insights resulting from CRM capabilities into establishing, maintaining, and enhancing long-term associations with customers, and vice versa. Research limitations/implications – The main limitations are those that typically apply to cross-sectional surveys. Although several steps were taken to reduce the concern of key informant bias and common method variance, dependent and independent variables were collected from the same source at a single moment in time. Practical implications – Ceteris paribus, an increase of generative learning orientation by one unit (seven-point scale) can command an increase of up to 7 percent of the average customer performance due to its direct and interaction effect. Because even small changes in customer performance have a strong impact on financial performance, this finding indicates a remarkable and substantial result for managers. Originality/value – Though previous research provides evidence of the adaptive learning consequences of CRM, a review of the literature reveals a lack of studies that analyze the importance of generative learning orientation for successful CRM.