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Customer Acquisition, Engagement, and Retention in Online Advertising The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Els, Michael. 2020. Customer Acquisition, Engagement, and Retention in Online Advertising. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences. Citable link https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37365116 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA Customer Acquisition, Engagement, and Retention in Online Advertising A dissertation presented by Michael Samuel David Els to The Harvard Business School in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Business Administration Harvard University Cambridge, Massachusetts October 2019 ©2019 Michael Samuel David Els All rights reserved. Dissertation Advisor: Author: Professor Sunil Gupta Michael Samuel David Els Customer Acquisition, Engagement, and Retention in Online Advertising Abstract Online advertising continues to evolve at a rapid as the internet and the digital marketing landscape mature. Firms face new challenges in acquiring, engaging and retaining customers. Entire new markets and technologies have grown out of the race digital marketing dominance. This dissertation aims to examine some of these advances and offer practical insights for today’s firms that need to navigate this new world. In the first essay, I explore the effects of user attention to online display advertising. Using two observational studies, I show that attention is highly heterogeneous and predictable during the user browsing session. The implications are that publishers should be more selective in ad placement and that advertisers should be more selective in ad purchases. The second essay examines how programmatic advertising firms should efficiently allocate ads in real-time bidding environments on behalf of their client advertisers. I introduce the demand side platform problem which is related to both the adwords and publisher problems, but distinct in that the supply of ad space assumed unlearnable. I provide a real-time mechanism for efficient ad allocation in this setting and demonstrate efficacy using real-time bidding data. In the final essay, I examine cross-merchant spillovers in coalition loyalty programs. I examine a natural experiment where a large grocery store joined a large loyalty program coalition. Using a iii quasi-difference-in-difference approach and Bayesian Structural Time Series for causal inference, I find that adding a large complementary merchant into a coalition loyalty program increases sales and purchase frequency of existing customers at existing merchants. iv Contents Abstract .............................................................................................................................. iii Acknowledgements ........................................................................................................... vii Introduction ......................................................................................................................... 1 1 Online Task Progression and Display Ad Engagement................................................ 3 1.1 Introduction ........................................................................................................... 3 1.2 Literature Review .................................................................................................. 5 1.3 Attention to Tasks ................................................................................................. 7 1.4 Conceptual Model ................................................................................................. 9 1.5 Measurement ....................................................................................................... 11 1.6 Design and Identification .................................................................................... 13 1.7 Data Set 1 – Single News Tasks.......................................................................... 18 1.7.1 Data Description ............................................................................................ 18 1.7.2 Empirical Model ............................................................................................ 20 1.7.3 Results ............................................................................................................ 22 1.7.4 Propensity Score Matching Design ................................................................ 24 1.7.5 Results of Propensity Score Matching ........................................................... 25 1.8 Data Set 2 – Browsing Sessions with Multiple Tasks ......................................... 31 1.8.1 Data description ............................................................................................. 32 1.8.2 Propensity Score Matching Design ................................................................ 34 1.8.3 Results ............................................................................................................ 35 1.9 Validation Simulation for Advertisers ................................................................ 38 1.10 Conclusion ........................................................................................................... 44 1.10.1 Caveats ........................................................................................................... 45 1.10.2 Implications of Findings ................................................................................ 45 2 Real-time Digital Ad Allocation: A Fair Streaming Allocation Mechanism ............. 47 2.1 Introduction ......................................................................................................... 47 2.2 Literature Review ................................................................................................ 49 2.3 Fairness................................................................................................................ 53 2.4 Algorithm Design ................................................................................................ 55 2.4.1 Environment ................................................................................................... 55 v 2.4.2 Proposed Solution .......................................................................................... 58 2.4.3 Part 1 - Agent level optimization ................................................................... 58 2.4.4 Market level allocation .................................................................................. 65 2.5 Evaluation............................................................................................................ 69 2.5.1 Single-Advertiser Simulation......................................................................... 70 2.5.2 Multiple Advertisers (Market-level evaluation) ............................................ 75 2.6 Conclusion ........................................................................................................... 80 3 Cross-Merchant Spillovers in Coalition Loyalty Programs ....................................... 82 3.1 Introduction ......................................................................................................... 82 3.2 Related literature ................................................................................................. 85 3.3 Background, data, and preliminary evidence ...................................................... 87 3.4 Matching Analysis............................................................................................... 92 3.5 Bayesian Structural Time Series ....................................................................... 102 3.6 Extension to other merchants ............................................................................ 112 3.7 Conclusion, Discussion, and Limitations .......................................................... 115 References ....................................................................................................................... 118 Appendix ......................................................................................................................... 124 A Online Task Progression and Display Ad Engagement............................................ 124 A.1 Propensity Score Matching Details ................................................................... 124 A.2 SEC DSP Cost Estimates .................................................................................. 125 B Real-time Digital Ad Allocation: A Fair Streaming Allocation Mechanism ........... 125 B.1 PID results without PSO ................................................................................... 125 B.2 Market level DH formulation ............................................................................ 127 C Cross-Merchant Spillovers in Coalition Loyalty Programs ..................................... 128 C.1 Variable Definitions .......................................................................................... 128 C.2 Matching Analysis............................................................................................. 130 C.3 Other merchant entries .....................................................................................

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