Not Just a Fad: Optimal Sequencing in Mobile In-App Advertising

Zhen Sun (), Milind Dawande (), Ganesh Janakiraman () and Vijay Mookerjee ()
Additional contact information
Zhen Sun: School of Business, George Washington University, Washington, DC 20052
Milind Dawande: Naveen Jindal School of Management, University of Texas at Dallas, Richardson, Texas 75080
Ganesh Janakiraman: Naveen Jindal School of Management, University of Texas at Dallas, Richardson, Texas 75080
Vijay Mookerjee: Naveen Jindal School of Management, University of Texas at Dallas, Richardson, Texas 75080

Information Systems Research, 2017, vol. 28, issue 3, 511-528

Abstract: In this paper, we address the challenge faced by ad networks in managing the fading ads (or fads ) shown to an end user during a session of a mobile application (app). A fad is an ad that disappears if the user does not interact with it for some length of time. The withdrawn ad could be replaced by another ad. The goal of the ad network is to determine the sequence of fads shown to the user in an ad space to maximize the expected revenue generated over the user’s app session. Mobile in-app advertising is uniquely suited for the sequencing of fads because user sessions are typically longer (than web sessions), and a single ad is displayed at any given point in time. We consider two factors that affect the probability of a click on an ad during a session: (i) the sojourn effect , the influence of the passage of time, and (ii) the exposure effect , the influence of the number of prior exposures of the ad to the user during that session. We provide simple and optimal policies for the ad-sequencing problem when either of these two effects dominates. For the general case in which both effects are significant, we offer a provably near-optimal heuristic policy. The following two enhancements to the basic sequencing problem are also analyzed: (a) consideration of both click ads (which generate revenue for the ad network only through clicks) and display ads (which generate revenue only through exposures) and (b) the presence of a constraint imposed by the publisher (i.e., the owner of the app) that the expected revenue in each time slot exceeds a certain threshold.

Keywords: in-app ads; fading ads; sequencing under uncertainty; optimal policies (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (12)

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