Selecting Predictive Metrics for Marketing Dashboards - An Analytical Approach

Koen Pauwels and Amit Joshi

Journal of Marketing Behavior, 2016, vol. 2, issue 2-3, 195-224

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.

Keywords: Metrics; Leading indicators; Marketing dashboards; Predictive analytics; Granger causality; Stepwise regression; Reduced-rank regression; Vector autoregression (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (1)

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