Essays in Online Advertising

148 Pages·2014·0.76 MB·English

by  Vibhanshu Abhishek

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Publicly Accessible Penn Dissertations
2012
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Vibhanshu Abhishek
University of Pennsylvania, [email protected]
Follow this and additional works at: https://repository.upenn.edu/edissertations
Part of the Other Economics Commons
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Abhishek, Vibhanshu, "Essays in Online Advertising" (2012). Publicly Accessible Penn Dissertations. 603.
https://repository.upenn.edu/edissertations/603
This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/603
For more information, please contact [email protected].
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The last several years have seen a dramatic increase in the amount of time and money consumers spend
online. As a consequence, the Internet has become an important channel that firms can use to reach out
and connect to consumers which has lead to the emergence of online advertising.Given the scale and
novelty of online advertising, there is a growing need to understand how consumers respond to online ads
and how firms should advertise using this medium. In my dissertation, I study different aspects of
sponsored search and display ads which constitute the bulk of online advertising. In the first essay, I
focus on the issues related to the use of aggregate data in sponsored search. I demonstrate that models
commonly used in sponsored search research suffer from aggregation bias and present the implications
of this aggregation bias. In the second essay, I focus on the advertiser's problem of bidding optimally in
sponsored search auctions. In the third essay, I study the interactions between various forms of online
advertising like banner ads, display ads and sponsored search ads and address the problem of
attribution.
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Dissertation
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Doctor of Philosophy (PhD)
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Operations & Information Management
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Kartik Hosanagar
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Peter S. Fader
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Attribution, Auctions, Consumer behavior, Game theory, Online advertising, Sponsored search
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Economics | Other Economics
This dissertation is available at ScholarlyCommons: https://repository.upenn.edu/edissertations/603
ESSAYS ON ONLINE ADVERTISING
Vibhanshu Abhishek
A DISSERTATION
in
Operations and Information Management
For the Graduate Group in Managerial Science and Applied Economics
Presented to the Faculties of the University of Pennsylvania
in
Partial Fulfillment of the Requirements for the
Degree of Doctor of Philosophy
2012
Supervisor of Dissertation Co-Supervisor of Dissertation
Kartik Hosanagar, Peter S. Fader,
Associate Professor of OPIM Professor of Marketing
Graduate Group Chairperson
Eric Bradlow, Professor of Marketing,
Statistics, and Education
Dissertation Committee
Kartik Hosanagar, Associate Professor of Operations and Information Management
Peter S. Fader, Frances and Pei-Yuan Chia Professor, Professor of Marketing
Lorin M. Hitt, Class of 1942 Professor of Operations and Information Management
Z. John Zhang, Murrel J. Ades Professor, Professor of Marketing
ESSAYS ON ONLINE ADVERTISING
' COPYRIGHT
2012
Vibhanshu Abhishek
To my grandfather Dr. Raman and my mother Anshu,
who have been a tremendous influence in my life.
iii
ACKNOWLEDGEMENT
The last five years have been the most challenging and exhilarating time of my life. I feel
privileged to have wonderful advisors and caring friends who have all stood by me during
this time and made this dissertation a reality.
First of all, I would like to thank Profs. Kartik Hosanagar, Pete Fader and John Zhang
for being excellent mentors during the PhD programm. I feel extremely fortunate to be
blessed with advisors who are extremely considerate, supportive and above all, accessible
when I needed their help. I could not have hoped for better role models for my career.
I still continue to learn from them everyday and sincerely wish that their support and
encouragement will extend beyond Wharton. I would like to thank Lorin Hitt for being a
part of my committee and having an early influence on my research agenda. I would also
like to thank Kinshuk Jerath, Noah Gans, G´erard Cachon, Oded Netzer, Eric Clemons,
Vadim Cherepanov, Eric Bradlow, Raghuram Iyengar and Elea Feit for many an insightful
discussion.
I would like to thank the Mack Center for Technological Innovation, the Baker Retailing
Center, the Wharton Customer Analytics Initiative and the Marketing Science Institute
for generous financial and data support that was instrumental in the completion of this
dissertation. Many thanks go to Mallory Hiatt, Kim Wartford, Andrea Nurse, Rochelle
Hambeau-Miller and Karen Ressler for all the administrative help and Stan Liu and Jamie
Walter for excellent IT support.
The last five years would not have been possible without the help and support my friends
at Wharton. Many special thanks go out to Dan Fleder, Pnina Feldman, Jaelynn Oh, Fazil
Pac¸, Amit Bhattacharya, Eric Schwartz and in particular, the OPIM Purification Band –
JoseGuajardo, JunLi, ToniMoreno, AlessandroArlotto, Andre´sCatalan-Cardenas, Necati
Tereya˘go˘glu and Fangyun Tan. I hope our friendship continues to grow beyond the confines
of Huntsman Hall.
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I would also like to say a special thanks to my family for their unflinching support, frequent
words of encouragement and understanding my physical absence during these years. Last
but not the least, I would like to thank my wife Ankita for bearing with my erratic schedule
and diligently proofreading all my papers, but most of all, for being by my side through
this tumultuous process. This dissertation is as much a fruit of her dedication as it is of my
labor and words can do little to express the gratitude I feel towards her.
v
ABSTRACT
ESSAYS ON ONLINE ADVERTISING
Vibhanshu Abhishek
Kartik Hosanagar
Peter S. Fader
In this dissertation, we study different dimensions of online advertising by focusing on re-
searchquestionswhicharemotivatedbymarketingandmanagerialconsiderations. Thefirst
essay highlights the inadequacies of data standards that are commonly used in sponsored
search. We show that these datasets result in aggregation bias and propose alternate data
standards that do not suffer from this bias. An equilibrium analysis is performed to analyze
the effect of this bias on the search engine and advertisers and it is shown that the search
engine tend to lose the most from this bias. The second essay focuses on an advertiser’s
problem of bidding optimally in sponsored search. Uncertainty in the decision-making envi-
ronment, budgetconstraintsandthepresenceofalargeportfolioofkeywordsmakesthebid
optimization problem non-trivial. We formulate this problem mathematically and propose
a “myopic” policy for one-period optimization. This policy is extended by incorporating
interactions between keywords, in the form of positive spillovers from generic keywords into
branded keywords. This multi-period “forward-looking” policy uses a Nerlove-Arrow model
to capture the long-term interactions between these keywords. The spillovers are estimated
using a dynamic linear model and used to jointly optimize the bids of the keywords using
an approximate dynamic programming approach. In the third essay we discuss the prob-
lem of attribution in the context of online ads. We formulate a dynamic Hidden Markov
Model to capture a consumer’s behavior during the purchase process and how this process
is affected by ads. This model is subsequently used to evaluate the role that each ad plays
in a consumer’s eventual conversion in order to solve the attribution problem.
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TABLE OF CONTENTS
ACKNOWLEDGEMENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii
LIST OF ILLUSTRATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
CHAPTER 1 : Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
CHAPTER 2 : Aggregation Bias in Sponsored Search Data: The Curse and The Cure 6
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.3 Aggregation Bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.4 Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.5 Managerial Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.6 Suggested Cures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
CHAPTER 3 : Optimal Bidding in Multi-Item Multi-Slot Sponsored Search Auctions 51
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.2 Literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.3 Analytical Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
3.4 Data Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
3.5 Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
3.6 Incorporating Interdependence between Keywords . . . . . . . . . . . . . . 79
3.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
3.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
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CHAPTER 4 : The Long Road to Online Conversion: A Model of Multi-Touch At-
tribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
4.2 Prior Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
4.3 Data Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
4.4 Model of Multi-Touch Attribution . . . . . . . . . . . . . . . . . . . . . . . 106
4.5 Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
4.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
4.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
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