Predicting Revenues With the Multiplier Heuristic

21 Pages Posted: 26 Mar 2020

See all articles by Florian M. Artinger

Florian M. Artinger

Max Planck Institute for Human Development

Nikita Kozodoi

affiliation not provided to SSRN

Julian Runge

Humboldt University of Berlin - School of Business and Economics; Facebook, Marketing Science Research

Date Written: February 28, 2020

Abstract

Using machine learning to forecast revenues per customer, product, or store has become a major industry. It contrasts with a simple, intuitive managerial heuristic: multiply the revenue observed in the first t days by a constant. We test the predictive accuracy of this multiplier heuristic in 20 data sets. Surprisingly, the heuristic performs overall on par with machine learning models. We identify three central drivers when the heuristic can even outperform these models: limited sample size, time-to-prediction, and changes across time. The results provide insights when to rely on heuristics and managerial intuition or when on machine learning.

Suggested Citation

Artinger, Florian M. and Kozodoi, Nikita and Runge, Julian and Runge, Julian, Predicting Revenues With the Multiplier Heuristic (February 28, 2020). Available at SSRN: https://ssrn.com/abstract=3546017 or http://dx.doi.org/10.2139/ssrn.3546017

Florian M. Artinger (Contact Author)

Max Planck Institute for Human Development ( email )

Lentzeallee 94
D-14195 Berlin, 14195
Germany

Nikita Kozodoi

affiliation not provided to SSRN

Julian Runge

Humboldt University of Berlin - School of Business and Economics ( email )

Spandauer Str. 1
Berlin, D-10099
Germany

Facebook, Marketing Science Research ( email )

Menlo Park, CA 94025
United States

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