The Value of Field Experiments

Jimmy Q. Li (), Paat Rusmevichientong (), Duncan Simester (), John N. Tsitsiklis () and Spyros I. Zoumpoulis ()
Additional contact information
Jimmy Q. Li: Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Paat Rusmevichientong: Marshall School of Business, University of Southern California, Los Angeles, California 90089
Duncan Simester: MIT Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
John N. Tsitsiklis: Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Spyros I. Zoumpoulis: Decision Sciences, INSEAD, 77305 Fontainebleau Cedex, France

Management Science, 2015, vol. 61, issue 7, 1722-1740

Abstract: The feasibility of using field experiments to optimize marketing decisions remains relatively unstudied. We investigate category pricing decisions that require estimating a large matrix of cross-product demand elasticities and ask the following question: How many experiments are required as the number of products in the category grows? Our main result demonstrates that if the categories have a favorable structure, we can learn faster and reduce the number of experiments that are required: the number of experiments required may grow just logarithmically with the number of products. These findings potentially have important implications for the application of field experiments. Firms may be able to obtain meaningful estimates using a practically feasible number of experiments, even in categories with a large number of products. We also provide a relatively simple mechanism that firms can use to evaluate whether a category has a structure that makes it feasible to use field experiments to set prices. We illustrate how to accomplish this using either a sample of historical data or a pilot set of experiments. We also discuss how to evaluate whether field experiments can help optimize other marketing decisions, such as selecting which products to advertise or promote.Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2014.2066 . This paper was accepted by Pradeep Chintagunta, marketing .

Keywords: field experiments; promotions; big data; sparsity (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (10)

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