Competitive targeted online advertising

Keywords Online shopping platforms; Targeted advertising; Position auction; Price discrimination Highlights * This paper incorporates targeted advertising into the traditional position auction model. * Targeted advertising based on product preference (TP) mitigates competition in both product markets and position auctions, and may lower the publisher's auction payoff. * In contrast, targeted advertising based on search preference (TS) increases the prominent position's valuation and always benefits the publisher. * Consumer surplus is higher under TS, but lower under TP. * Total surplus is higher under TP, but not affected by TS. 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. Author Affiliation: (a) 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 (b) Institute for Social Governance and Development Research, Tsinghua University, 14F, #2 Shuangqing Building, 77 Shuangqing Road, Beijing, 100085, China (c) School of Economics, Shanghai University of Finance and Economics; Key Laboratory of Mathematical Economics (SUFE), Ministry of Education, 777 Guoding Road, Shanghai 200433, China * Corresponding author. Article History: Received 4 August 2020; Revised 16 January 2023; Accepted 23 January 2023 (footnote)[white star] We are grateful for valuable comments from the Editor Jose L. Moraga-Gonzalez, two anonymous reviewers, Yijuan Chen and participants in seminars at Southwestern University of Finance and Economics and Fudan University. Li thanks the financial support by National Natural Science Foundation of China (grant nos. 71773131; 71922021; 72192801), Beijing Natural Science Foundation (Z220001), and fund for building world-class universities (disciplines) of Renmin University of China; Yu thanks the financial support by National Natural Science Foundation of China (grant no. 72273078), and support from the SUFE Theoretical Economics Gaofeng II Discipline Innovation Project (2018110721). Byline: Sanxi Li [sanxi@ruc.edu.cn] (a), Hailin Sun [sunhailin@mail.tsinghua.edu.cn] (b), Jun Yu [yu.jun@mail.shufe.edu.cn] (*,c)