Right Metric for the Right Decision: A Behavioral Model to Infer Metric Effectiveness in Managerial Marketing-Mix Decision-Making
Ofer Mintz, Timothy J. Gilbride, Peter Lenk, and Imran S. Currim, 2019, 19-107-01
While there has been progress on the development of metrics to improve managerial decision quality, little or no research uses performance data on specific marketing-mix decisions to assess the use and infer the effectiveness of individual metrics (i.e., their association with decision outcomes). Thus, managers and researchers face uncertainty about which metrics are most effective for different marketing-mix decisions.
Here, Ofer Mintz, Timothy Gilbride, Peter Lenk, and Imran Currim address the question: which metrics perform best for different marketing-mix decisions in the context of managerial, firm, and industry characteristics?
They develop a generalized theoretical framework and augment this conceptual framework with a statistical model that estimates the specific relationships among the effectiveness of individual metrics, their use, and the outcome of marketing-mix decisions.
They empirically test their framework by calibrating models with data from 1,287 specific marketing-mix decisions made by 439 U.S. managers, including which (of 24) metrics they used for a given decision as well as the outcome of that decision. Importantly, the unit of analysis is at the individual manager and decision level.
After controlling for covariates and correcting for endogeneity bias due to selection effects, the authors can infer which metrics are most effective and ineffective across different marketing-mix decision settings.
Among the 24 metrics, awareness and willingness to recommend stand out as “silver bullets”, that is, these two metrics tend to be associated with better performance across the majority of marketing-mix decision.
Three metrics -- total customers, target volume, and net present value -- are “lead bullets”, associated with worse performance for most types of marketing-mix decisions.
Other metrics are better suited for some decisions and disadvantageous for others. For example, for price promotion decisions, the use of awareness, customer loyalty, and consideration set are significantly associated with improved performance, while the use of net present value and share of voice are significantly associated with worse performance.
Overall, they find that marketing metrics on average are more effective than financial metrics for managers making different marketing-mix decisions. At the same time, managers in their study are more uncertain in their assessments of the ex-ante effectiveness of marketing metrics, and more hesitant to use such metrics when they thought that they were effective.
Ofer Mintz is Senior Lecturer, UTS Business School, University of Technology Sydney. Timothy J. Gilbride is the late Steve and Anne Odland Associate Professor of Marketing, Mendoza College of Business, University of Notre Dame. Peter Lenk is Professor of Technology & Operations and Marketing, Ross School of Business, University of Michigan. Imran S. Currim is Chancellor’s Professor at the Paul Merage School of Business, University of California, Irvine.
The authors thank the UCI Paul Merage School of Business Dean’s Office for financial support and seminar participants at Hebrew University, HEC Paris, Technion – Israel Institute of Technology, Tulane University, University of Alabama, the 2015 Marketing Strategy Meets Wall Street IV Conference at Singapore Management University, the 2015 Theory and Practice in Marketing (TPM) Conference at Georgia State University, the 2016 Marketing Advanced Research Techniques (ART) Forum in Boston, and the 2017 Marketing Science Conference at the University of Southern California for their useful feedback.
Drivers of Metric Use in Marketing Mix Decisions: An Investigation across the G7, BRIC, and MIST Countries
Ofer Mintz, Jan-Benedict E.M. Steenkamp, Martijn de Jong, and Imran S. Currim (2017) [Report]
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