Journal of Marketing Behavior > Vol 2 > Issue 2–3

Selecting Predictive Metrics for Marketing Dashboards - An Analytical Approach

Koen Pauwels, Ozyegin University, Turkey, Koen.Pauwels@ozyegin.edu.tr Amit Joshi, University of Central Florida, USA, amit.joshi@ucf.edu
 
Suggested Citation
Koen Pauwels and Amit Joshi (2016), "Selecting Predictive Metrics for Marketing Dashboards - An Analytical Approach", Journal of Marketing Behavior: Vol. 2: No. 2–3, pp 195-224. http://dx.doi.org/10.1561/107.00000035

Published: 29 Dec 2016
© 2016 K. Pauwels and A. Joshi
 
Subjects
 
Keywords
MetricsLeading indicatorsMarketing dashboardsPredictive analyticsGranger causalityStepwise regressionReduced-rank regressionVector autoregression
 

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In this article:
Research Background
Proposed Analytical Approach to Metric Reduction in Marketing Dashboards
Data
Empirical Results
Conclusions
Technical Appendix
References

Abstract

Managers often track metrics they believe can potentially predict performance outcomes and help them improve decisions. However, it is unclear how to best select such predictive metrics out of a wide range of candidate metrics. This study develops and demonstrates an analytical approach to metric selection. First, delete metrics that show too little or too much variation in univariate tests. Second, reveal leading performance indicators with pairwise tests. Third, quantify how much each leading indicator explains performance with econometric models, preferably from different research traditions. Fourth, select the best set of key leading performance indicators by assessing their predictive validity in a holdout sample. Finally, use the selected set of metrics and estimation model to perform what-if analyses for proposed courses of action. The authors demonstrate this analytical approach for the leading national brand and the composite of store-brands in a fast-moving consumer good category.

DOI:10.1561/107.00000035

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