Random effects model for estimating effectiveness of advertising in online marketplaces

Authors:
Cookhwan Kim;Sungsik Park;Yongseok Chang;Woojin Chang
Affiliations:
Department of Industrial Engineering, Seoul National University, 599, Kwanak Street, Kwanak-Gu, Seoul, Republic of Korea;Department of Industrial Engineering, Seoul National University, 599, Kwanak Street, Kwanak-Gu, Seoul, Republic of Korea;Investment Banking I, Korea Development Bank Capital, KDB Capital Building, 16 Yeouido-dong, Youngdeungpo-gu, Seoul, Republic of Korea;Department of Industrial Engineering, Seoul National University, 599, Kwanak Street, Kwanak-Gu, Seoul, Republic of Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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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.