Marketing Analytics for Data-Rich Environments

JM Insights in the Classroom

Teaching Insight:

Industry practice has demonstrated that big data opportunities are enormous, but there is a need to (a) reconcile academic ‘big stats on small data’ with practitioner ‘small stats on big data’ and (b) combine deep learning methods with statistical modeling and theory-based models to establish causal effects. Privacy and security concerns will limit collection/retention of data. So there is a need to focus on the development of analytics for anonymized and minimized data and proactive development of methods for protection of customer privacy.

Related Marketing Courses: ​
Marketing Analytics​​​​

Full Citation: ​
Wedel, Michel and P.K. Kannan (2016), “Marketing Analytics for Data-Rich Environments.” Journal of Marketing, Vol. 80 (6), 97-121.

Article Abstract: 
The authors provide a critical examination of marketing analytics methods by tracing their historical development, examining their applications to structured and unstructured data generated within or external to a firm, and reviewing their potential to support marketing decisions. The authors identify directions for new analytical research methods, addressing (1) analytics for optimizing marketing-mix spending in a data-rich environment, (2) analytics for personalization, and (3) analytics in the context of customers’ privacy and data security. They review the implications for organizations that intend to implement big data analytics. Finally, turning to the future, the authors identify trends that will shape marketing analytics as a discipline as well as marketing analytics education.

Special thanks to Kelley Gullo and Holly Howe, Ph.D. candidates at Duke University, for her support in working with authors on submissions to this program.

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