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Competitive targeted online advertising
Institution:1. School of Economics, Renmin University of China; Center for Digital Economy Research, Renmin University of China; Research Institute of State-owned Economy, Renmin University of China, Beijing, 100872, China;2. Institute for Social Governance and Development Research, Tsinghua University, 14F, #2 Shuangqing Building, 77 Shuangqing Road, Beijing, 100085, China;3. School of Economics, Shanghai University of Finance and Economics; Key Laboratory of Mathematical Economics (SUFE), Ministry of Education, 777 Guoding Road, Shanghai 200433, China;1. Dipartimento di Economia e Finanza, Catholic University of Milan, Via Necchi 5, 20123 Milano, Italy;2. Dipartimento di Matematica per le Scienze Economiche, Finanziarie ed Attuariali, Catholic University of Milan, Largo Gemelli 1, Milano 20123, Italy;1. Office of Communications UK;2. Dipartimento di Scienze Economiche ed Aziendali “M. Fanno”, Università di Padova, Italy;3. School of Economics and Centre for Competition Policy, University of East Anglia, UK;1. School of Management, Fudan University, 670 Guoshun Road, Shanghai 200433, China;2. Department of Economics, University of Southern California, 3620 South Vermont Ave., Los Angeles, CA 90089, United States;3. Uber Technologies Inc., United States;1. Compass Lexecon, Spain;2. Graduate Institute of International and Development Studies, Geneva, CEPR and Compass Lexecon, Belgium;3. Bergamo University and Compass Lexecon, Italy;1. Department of Economics, University of Alberta, Canada;2. Territorial and Sectoral Analysis Laboratory - LATES, Brazil;3. Institute for Applied Economic Research (Ipea), Brazil;4. Department of Economics, University of Brasilia, Brazil;5. National Institute of Science and Technology for Complex Systems (INCT-SC), Brazil;6. Machine Learning Laboratory in Finance and Organizations (LAMFO), Brazil;1. Department of Economics and Social Sciences, Telecom Paris, Institut Polytechnique de Paris, France;2. CESifo, Munich, Germany;3. Dipartimento di Scienze Economiche ed Aziendali “M. Fanno”, Università di Padova, Padova, Italy
Abstract:This paper examines how an online publisher utilizes its information about consumer preference to target advertising. In our model, two firms first bid for a prominent ad position in a publisher-organized position auction, and then compete on price in the subsequent product marketplace. We consider two dimensions of consumer heterogeneity. First, consumers are heterogeneous in product preference. Based on their tastes, some consumers prefer one product over the other, whereas other consumers may rank the products in an opposite order. Second, consumers differ in search preference, i.e., “nonshoppers” only consider the advertised product, while “shoppers” always search both firms’ products before buying. We show that targeted advertising based on product preference will mitigate price competition in product markets as well as competition in position auctions, the latter to the detriment of the publisher. In contrast, targeted advertising based on search preference always benefits the publisher, as the winning firm can charge monopoly prices to nonshoppers. We show that the publisher’s optimal choice is to utilize only the information about consumer search preference. We also explore the welfare implications of targeted advertising based on different types of consumer preference.
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