Strategic B2B customer experience management: the importance of outcomes-based measures

Purpose: The purpose of this paper is to critique the adequacy of efforts to capture the complexities of customer experience in a business-to-business (B2B) context using input–output measures. The paper introduces a strategic customer experience management framework to capture the complexity of B2B service interactions and discusses the value of outcomes-based measurement. Design/methodology/approach: This is a theoretical paper that reviews extant literature related to B2B customer experience and asks fresh questions regarding B2B customer experience at a more strategic network level. Findings: The paper offers a reconceptualisation of B2B customer experience, proposes a strategic customer experience management framework and outlines a future research agenda. Research limitations/implications: This paper is conceptual and seeks to raise questions surrounding the under-examined area of B2B customer experience. As a consequence, it has inevitable limitations resulting from the lack of empirical evidence to support the reconceptualisation. Practical implications: Existing measures of customer experience are problematic when applied in a B2B (services) context. Rather than adopting input- and output-based measures, widely used in a business-to-consumer (B2C) context, a B2B context requires a more strategic approach to capturing and managing customer experience. Focussing on strategically important issues should generate opportunities for value co-creation and are more likely to involve outcomes-based measures. Social implications: Improving the understanding of customer experience in a B2B context should allow organisations to design better services and consequently enhance the experiences of their employees, their customers and other connected actors. Originality/value: This paper critiques the current approach to measuring customer experience in a B2B context, drawing on contemporary ideas of value-in-use, outcomes-based measures and “Big Data” to offer potential solutions to the measurement problems identified.