Quantitative Marketing and Economics

, Volume 12, Issue 2, pp 209–233 | Cite as

Meta-analyses using information reweighting: An application to online advertising

  • Pengyuan Wang
  • Eric T. Bradlow
  • Edward I. George
Article

Abstract

Because technology-enabled marketing research has led to information arriving at a rapid pace, methods in marketing that allow for coherent, sequential and fast information integration are needed. We propose in this research a new approach to information integration: Information Reweighted Priors (IRPs). It is a sample reweighting approach which utilizes the output from a Bayesian model fit using Markov Chain Monte Carlo, with no restrictions on the likelihood, prior distributions, or data structure; hence a general purpose tool. We demonstrate the approach with simulated datasets and an online advertising dataset with external information obtained from i) previous advertising studies in the industry from a major online advertising portal, ii) past academic studies of online adverting and iii) out-of-sample summaries of the dataset.

Keywords

Information integration Prior reweighting Advertising Informative priors Statistical computing 

JEL Classifications

C11 - Bayesian Analysis: General M31 - Marketing M37 - Advertising 

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Pengyuan Wang
    • 1
  • Eric T. Bradlow
    • 1
  • Edward I. George
    • 1
  1. 1.University of PennsylvaniaPhiladelphiaPennsylvania

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