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On the Near Impossibility of Measuring the Returns to Advertising

Randall A Lewis

Google, Inc.

Justin M. Rao

Microsoft Research

December 12, 2013

Classical theories assume the firm has access to reliable signals to measure the causal impact of choice variables on profit. For advertising expenditure we show, using twenty-five online field experiments with major U.S. retailers and brokerages ($2.8 million expenditure), that this assumption typically does not hold. Evidence from the randomized trials is very weak because individual-level sales are incredibly volatile relative to the per capita cost of a campaign -- a "small'' impact on a noisy dependent variable can generate positive returns. A calibrated statistical argument shows that the required sample size for an experiment to generate informative confidence intervals is typically in excess of ten million person-weeks. This also implies that selection bias unaccounted for by observational methods only needs to explain a tiny fraction of sales variation to severely bias observational estimates. We discuss how weak informational feedback has shaped the current marketplace and the impact of technological advances moving forward.

Number of Pages in PDF File: 42

Keywords: advertising, field experiments, causal inference, electronic commerce, return on investment, information

JEL Classification: L10, M37, C93

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Date posted: December 14, 2013  

Suggested Citation

Lewis, Randall A and Rao, Justin M., On the Near Impossibility of Measuring the Returns to Advertising (December 12, 2013). Available at SSRN: http://ssrn.com/abstract=2367103 or http://dx.doi.org/10.2139/ssrn.2367103

Contact Information

Randall A Lewis
Google, Inc. ( email )
1600 Amphitheatre Parkway
Mountain View, CA 94043
United States
312-RA-LEWIS (Phone)
HOME PAGE: http://www.econinformatics.com/
Justin M. Rao (Contact Author)
Microsoft Research ( email )
641 Avenue of Americas
7th Floor
New York, NY 11249
United States
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