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Random effects model for estimating effectiveness of advertising in online marketplaces

Cited 3 time in Web of Science Cited 3 time in Scopus
Authors
Kim, Cookhwan; Park, Sungsik; Chang, Yongseok; Chang, Woojin
Issue Date
2011-08-01
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Citation
EXPERT SYSTEMS WITH APPLICATIONS; Vol.38 8; 9867-9878
Keywords
Online marketplaceAdvertising effect modelPoisson-gamma modelOnline advertisingClick-through dataRandom effects modelHierarchical Bayes model
Abstract
This paper presents an application of the Bayesian Markov Chain Monte Carlo (MCMC) used to select cost-effective ad spots in online marketplaces. Due to the rise of electronic commerce, the online advertising industry which is highly complex undergoes rapid changes. And there are plenty of studies that keep coming up with the similar methodologies to predict click-through rates for ad spots. Previous research has mainly considered the following models: a logistic regression model and a binomial model connected by a linear link function. However, it is problematic to directly apply the existing online advertising effect models to the click-through data of online marketplaces. Because generally a click-through rate is fairly low so that a small change in its rate might give a somewhat larger prediction error in terms of click-throughs. We propose a Bayesian Poisson-gamma model to predict click-throughs instead of their rates and further extend to incorporate random effects in order to account for heterogeneity of variance between keywords. Our results may help guide online advertisers in decision-making. (C) 2011 Elsevier Ltd. All rights reserved.
ISSN
0957-4174
Language
English
URI
https://hdl.handle.net/10371/74918
DOI
https://doi.org/10.1016/j.eswa.2011.02.044
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Appears in Collections:
College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Industrial Engineering (산업공학과)Journal Papers (저널논문_산업공학과)
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