Drowning in Metrics: How Managers Select and Trade-off Metrics for Making Marketing Budgetary Decisions
Ofer Mintz, Yakov Bart, Peter Lenk, and David J. Reibstein, 2019, 19-135
With the proliferation of big data and quantitative information generating the rapidly growing number of metrics managers can use for their marketing decisions, many managers are increasingly concerned about metric “overload.” Managers continually feel like they are “drowning in metrics” as they select which metrics to employ for budgetary decisions. At the same time, they face increasing pressure to justify their marketing decisions via the use of such metrics.
However, we know little about managerial metrics preferences when making budgetary decisions in this environment. Which metrics do marketers use for their decisions? How do marketers make trade-offs between metrics for making decisions in various marketing contexts?
To answer these questions, Ofer Mintz, Yakov Bart, Peter Lenk, and David Reibstein develop a new conceptual model on the use and trade-off of metrics by managers making marketing budgetary decisions, based on more than 200 interviews and a multi-disciplinary literature review of managerial metric preferences. Their model focuses on how decision type and task influence managerial metric preferences and trade-offs.
They obtain responses from 563 managers with authority on over $1 million marketing budgets who selected metrics to include in 1,126 idealized build-your-own conjoint choice marketing budget dashboards, and rank-ordered these metrics for 2,252 decision-making and approval-seeking tasks.
They build a statistical model that fuses this data into a single integrated model in order to estimate managerial metric preferences based on BYO selections and rank-order tasks, while accounting for various characteristics of the manager, firm, industry, data quality, and marketing function that could impact preferences.
The results on managerial preferences for metrics in certain decision types and tasks should improve managerial marketing knowledge and benchmarks. For example, the authors find that metrics such as satisfaction and total customers are most preferred by managers, while Tobin’s q and consideration set are the two metrics managers least prefer.
They also find substantial heterogeneity in preferences among managers: those who are more concerned about measurement issues tend to prefer metrics that are based on accounting or financial information over metrics that are based on psychometrics.
By utilizing these empirical results, customized dashboards can be created tailored to firm, industry, and manager contexts to improve managerial decision quality.
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