We describe the history and the state-of-the-art in marketing analytics and provide future research directions in the context of big data for personalization, marketing mix, and privacy and security.
The research was motivated by a Marketing Science Institute (MSI) initiative to explore the research Frontiers in Marketing, where the co-authors focused on the MSI Research Priorities in the area of marketing analytics in data-rich environment. Because developments in technology have led to an explosion in the availability of data, we examine the gaps in analytics techniques to handle such data and propose a road-map for future research.
The paper starts with an examination of the history of developments in marketing analytics and describes the state-of-the-art techniques for modeling structured and unstructured data. The paper then outlines the developments in the specific areas of analytics for marketing mix, analytics for personalization, and analytics for customers’ privacy and security and describes specific areas for future research. We also focus on marketing analytics implementation and education.
Figure. The Diagnostic Breadth of Big Data Marketing Analytics
The frontiers of marketing analytics increasingly involve unstructured big data that requires techniques that expand beyond conventional statistical and econometric methods. Developments in machine learning, deep learning, and computational techniques, when combined with developments in statistical and econometric models, will provide the impetus for the next-generation marketing analytics solution. Analytics education involving both academics and practitioners are necessary for development of the field.
“Marketing Analytics for Data-Rich Environments,” by Michel Wedel & P.K. Kannan
The findings of the paper are more focused on firms that are developers and users of marketing analytics. The scale of research applications necessitated by big data are beyond the capabilities of just academics and practitioners. It is necessary for both to collaborate on such applications to move the field forward and derive business benefits from it. The findings are common to all firms and verticals that depend on big data, whether it is online, offline, mobile, or a combination of all these environments.
Questions for the Classroom
What are the key developments and state-of-the-art techniques to handle structured and unstructured data in a data-rich environment to support marketing decision making?
What are the future research opportunities in the areas of analytics for marketing mix optimization and personalization?
How can marketing researchers develop analytics to tackle issues of privacy and security in a data-rich environment?
Michel Wedel and P.K. Kannan (2016), “Marketing Analytics for Data-Rich Environments,” Journal of Marketing, 80 (6), 97-121.
Michel Wedel is PepsiCo Chaired Professor of Consumer Science and Distinguished University Professor, Robert H. Smith School of Business, University of Maryland, College Park (e-mail: firstname.lastname@example.org).
P.K. Kannan is Ralph J. Tyser Professor of Marketing Science, Robert H. Smith School of Business, University of Maryland, College Park (e-mail: email@example.com).
The AMA / MSI Special Issue of Journal of Marketing
Volume 80, Issue 6, November 2016
V. Kumar, Kevin Lane Keller, & Katherine N. Lemon
Christine Moorman and George S. Day
V. Kumar and Werner Reinartz
Katherine N. Lemon and Peter C. Verhoef
Michel Wedel and P.K. Kannan
Rajeev Batra and Kevin Lane Keller
Cait Lamberton and Andrew T. Stephen
Dominique M. Hanssens and Koen H. Pauwels