问责
市场调研
营销
市场营销管理
过程(计算)
数字营销
清晰
市场营销学
业务
营销有效性
市场营销策略
定量营销研究
营销投资回报率
过程管理
关系营销
计算机科学
政治学
生物化学
操作系统
化学
法学
作者
Neil A. Morgan,Satish Jayachandran,John Hulland,B. Ravi Kumar,Costas Katsikeas,Ágnes Somosi
标识
DOI:10.1016/j.ijresmar.2021.10.008
摘要
Marketing accountability, and how it may be achieved via performance assessment and metrics, have been central topics in both the marketing literature and practice (Katsikeas et al. 2016). Recent developments in digital channels, the accompanying explosion of data and emergence of marketing automation, the globalization of markets, and the rise of customer experience as a key firm priority have further magnified interest in and the importance of understanding how potential marketing outcomes are and can be achieved (CMO Survey, 2021; Mintz et al., 2021). As a result, gaining clarity on how to design and manage performance assessment systems to deal with these issues has never been more important. This paper argues that further progress in this research domain requires a deep understanding of the marketing performance assessment (MPA) process to provide both a catalyst and foundation for the next generation of research. Although there has been considerable research in the areas of marketing metrics and marketing accountability, much less attention has been paid to the MPA process that links them. Yet, the MPA process is essential to successful marketing management. To address this, we first review past research in this broad domain to answer the “Where have we been?” question that identifies theneed for a new conceptual model. Second, drawing on research findings both within the broad MPA domain and allied areas within and outside of marketing, we develop and detail a new conceptual model of the MPA process and use it to identify what really needs to be known but is currently unclear in this domain (i.e., “Where do we need to go?”). Third, we suggest how these areas of needed inquiry may best be investigated (i.e., “How do we get there?”) by identifying new perspectives, theories, data sources, and analysis approaches that may be productively employed in future research.
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