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Expertise, Attribution, and Ad Blocking in the World of Online Marketing

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Expertise, Attribution, and Ad Blocking in the World of Online Marketing Expertise, Attribution, and Ad Blocking in the World of Online Marketing by Stylianos Despotakis A dissertation submitted to the Tepper School of Business in partial fulfillment of the requirements for the degree of Doctor of Philosophy at Carnegie Mellon University May 2018 Dissertation Committee: R. Ravi (Chair) Kannan Srinivasan Vibhanshu Abhishek Isa Hafalir Kaifu Zhang Abstract In this dissertation, we model and provide insights to some of the main challenges the world of online marketing currently faces. In the first chapter, we study the role of information asymmetry introduced by the presence of experts in online marketplaces and how it affects the strategic decisions of different parties in these markets. In the second chapter, we study the attribution problem in online advertising and examine optimal ways for advertisers to allocate their marketing budget across channels. In the third chapter, we explore the effects of modern ad blockers on users and online platforms. In the first chapter, we examine the effect of the presence of expert buyers on other buyers, the platform, and the sellers in online markets. We model buyer expertise as the ability to accurately predict the quality, or condition, of an item, modeled as its common value. We show that nonexperts may bid more aggressively, even above their expected valuation, to compensate for their lack of information. As a consequence, we obtain two interesting implications. First, auctions with a “hard close” may generate higher revenue than those with a “soft close”. Second, contrary to the linkage principle, an auction platform may obtain a higher revenue by hiding the item’s common-value information from the buyers. We also consider markets where both auctions and posted prices are available and show that the presence of experts allows the sellers of high quality items to signal their quality by choosing to sell via auctions. In the second chapter, we study the problem of attributing credit for customer acquisi- tion to different components of a digital marketing campaign using an analytical model. We investigate attribution contracts through which an advertiser tries to incentivize two pub- lishers that affect customer acquisition. We situate such contracts in a two-stage marketing funnel, where the publishers should coordinate their efforts to drive conversions. First, we analyze the popular class of multi-touch contracts where the principal splits the attribution among publishers using fixed weights depending on their position. Our first result shows the following counterintuitive property of optimal multi-touch contracts: higher credit is given to the portion of the funnel where the existing baseline conversion rate is higher. Next, we show that social welfare maximizing contracts can sometimes have even higher conversion rate than optimal multi-touch contracts, highlighting a prisoners’ dilemma effect in the equi- librium for the multi-touch contract. While multi-touch attribution is not globally optimal, there are linear contracts that “coordinate the funnel” to achieve optimal revenue. However, such optimal-revenue contracts require knowledge of the baseline conversion rates by the principal. When this information is not available, we propose a new class of ‘reinforcement’ contracts and show that for a large range of model parameters these contracts yield better revenue than multi-touch. In the third chapter, we study the effects of ad blockers in online advertising. While online advertising is the lifeline of many internet content platforms, the usage of ad blockers has surged in recent years presenting a challenge to platforms dependent on ad revenue. In this chapter, using a simple analytical model with two competing platforms, we show that the presence of ad blockers can actually benefit platforms. In particular, there are conditions under which the optimal equilibrium strategy for the platforms is to allow the use of ad blockers (rather than using an adblock wall, or charging a fee for viewing ad-free i ii content). The key insight is that allowing ad blockers serves to differentiate platform users based on their disutility to viewing ads. This allows platforms to increase their ad intensity on those that do not use the ad blockers and achieve higher returns than in a world without ad blockers. We show robustness of these results when we allow a larger combination of platform strategies, as well as by explaining how ad whitelisting schemes offered by modern ad blockers can add value. Our study provides general guidelines for what strategy a platform should follow based on the heterogeneity in the ad sensitivity of their user base. Keywords: competitive strategy; segmentation; pricing; sniping; online auctions; signaling; attribution; advertising; Shapley value; multi-touch; game theory; marketing funnel; ad blocking; ad sensitivity; ad intensity Acknowledgements I would like to start by expressing my deepest gratitude to R. Ravi for his immense help throughout my time at CMU. I am truly grateful for the invaluable advice and support that he generously offered to me all these years. I would also like to thank Isa Hafalir, Amin Sayedi, Vibs Abhishek, and Kannan Srini- vasan for their tremendous help and contributions to the projects of this dissertation. The discussions we had and their feedback motivated me to work harder and encouraged me to be more passionate about my work. I also thank Kaifu Zhang for being part of my dissertation committee and providing some insightful comments and suggestions. I also want to express my appreciation to Lawrence Rapp and Laila Lee for taking care of all the administrative needs at Tepper and making life for PhD students easier. Special thanks go to all my friends and fellow students at CMU. I learned a lot from our work-related discussions, while our non-work related activities helped in making this journey more fun and enjoyable. Most of the things I know today are thanks to my many teachers throughout my life, before and during my studies at CMU. I will be forever indebted to them for everything they provided to me. Last but not least, I would like to thank my family. Their constant and unconditional love and support has benefited me enormously and I feel truly blessed for having them in my life. iii iv Contents Abstract.........................................i Acknowledgements................................... iii Contents v 1 Expertise in Online Markets1 1.1 Introduction....................................1 1.1.1 Our Contributions............................2 1.1.2 Related Literature............................4 1.2 Model.......................................6 1.3 Effect of Experts on Buyer Strategies......................8 1.3.1 Experts Induce Sniping..........................9 1.3.2 Impact of Experts on Nonexperts’ Strategy...............9 1.4 Effect of Experts on Platform Strategies.................... 10 1.4.1 An Auction with a Soft Close...................... 10 1.4.2 Effect of Experts on Platform Revenue................. 11 1.4.3 Experts and the Breakdown of the Linkage Principle......... 12 1.5 Effect of Experts on Seller Strategies...................... 13 1.6 Conclusion..................................... 16 1.A Appendix..................................... 17 1.A.1 Analyses and Proofs of Section 1.3................... 18 1.A.2 Analyses and Proofs of Section 1.4................... 20 1.A.3 Upper-bound Condition on δ ....................... 23 1.A.4 Analyses and Proofs of Section 1.5................... 24 1.B Additional Appendix............................... 26 1.B.1 Proof of Lemma 1.2............................ 27 1.B.2 More on δ ................................. 34 1.B.3 Choice of Tie-breaking Rule....................... 37 1.B.4 Distribution of Bidders’ Private Value................. 42 1.B.5 Signaling Using Closing Format: Hard vs. Soft Close......... 46 2 Multi-Channel Attribution: The Blind Spot of Online Advertising 49 2.1 Introduction.................................... 49 2.2 Literature Review................................. 52 2.3 Background.................................... 54 2.3.1 Purchase Funnel and Common Attribution Rules........... 54 v vi 2.3.2 Fairness and Shapley Value....................... 55 2.4 Model....................................... 55 2.4.1 Overview................................. 55 2.4.2 Consumer Model............................. 56 2.4.3 Firm’s Problem.............................. 57 2.4.4 Valid Contracts and Publishers’ Problem................ 58 2.4.5 Benchmarks................................ 59 2.5 Multi-Touch Attribution............................. 60 2.5.1 Definition of f-contract.......................... 60 2.5.2 Optimal f-contract............................ 61 2.6 Beyond Multi-touch Contracts.......................... 65 2.6.1 Optimal Contract............................. 65 2.6.2 Reinforcement Contracts......................... 66 2.7 Extensions..................................... 67 2.7.1 Effect of Cost Parameters of Publishers in Optimal f-contracts.... 68 2.7.2 Implementing the f-contract Under Uncertainty............ 69 2.7.3 Competition in Each Stage........................ 70 2.8 Conclusion..................................... 72 2.A Appendix..................................... 74 2.A.1 Analyses and Proofs........................... 74 2.A.2 A More General Model and Equivalence................ 80 2.A.3 Definition of Shapley Value....................... 84 3 The Beneficial Effects of Ad Blockers 87 3.1 Introduction.................................... 87 3.1.1 Background...............................
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