JSM 2011 Online Program

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Abstract Details

Activity Number: 448
Type: Invited
Date/Time: Wednesday, August 3, 2011 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics and Marketing
Abstract - #300051
Title: The Effectiveness of Display Ads
Author(s): Tim Hesterberg*+ and Diane Lambert and David X. Chan and Or Gershony and Rong Ge
Companies: Google Inc. and Google Inc. and Google Inc. and Google Inc. and Google Inc.
Address: 651 N. 34th Street, Seattle, WA, 98103, United States
Keywords: ad effectiveness ; display ads ; causal modeling ; irrelevant outcomes ; observational data ; selection bias
Abstract:

Display ads proliferate on the web, but are they effective? Or are they irrelevant in light of all the other advertising that people see? We describe a way to answer these questions, quickly and accurately, without randomized experiments, surveys, focus groups or expert data analysts. Causal modeling protects against the selection bias that is inherent in observational data, and a nonparametric test that is based on decoy outcomes provides further defense.  Computations are fast enough that all processing, from data retrieval through estimation, testing, validation and report generation, proceeds in an automated pipeline, without anyone needing to see the raw data.


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