Three Essays on Analyzing and Managing Online Consumer Behavior Inaugural-Dissertation zur Erlangung des Doktorgrades des Fachbereichs Wirtschaftswissenschaften an der Wirtschaftswissenschaftlichen Fakultät der Universität Passau vorgelegt von Eva Maria Anderl M.A. geboren am 21. Mai 1981 in München Prinzregentenstr. 83 81675 München Dissertation an der Wirtschaftswissenschaftlichen Fakultät der Universität Passau Erstgutachter: Prof. Dr. Jan H. Schumann Lehrstuhl für Betriebswirtschaftslehre mit Schwerpunkt Marketing und Innovation Universität Passau Zweitgutachter: Prof. Dr. Florian von Wangenheim Professur für Technologiemarketing ETH Zürich Datum der Disputation: 20. November 2014 Summary V Summary Summary Over the last two decades, the Internet has fundamentally changed the ways firms and consumers interact. The ongoing evolution of the Internet-enabled market environment entails new challenges for marketing research and practice, including the emergence of innovative business models, a proliferation of marketing channels, and an unknown wealth of data. This dissertation addresses these issues in three individual essays. Study 1 focuses on business models offering services for free, which have become increasingly prevalent in the online sector. Offering services for free raises new questions for service providers as well as marketing researchers: How do customers of free e-services contribute value without paying? What are the nature and dynamics of nonmonetary value contributions by nonpaying customers? Based on a literature review and depth interviews with senior executives of free e-service providers, Study 1 presents a comprehensive overview of nonmonetary value contributions in the free e-service sector, including not only word of mouth, co- production, and network effects but also attention and data as two new dimensions, which have been disregarded in marketing research. By putting their findings in the context of existing literature on customer value and customer engagement, the authors do not only shed light on the complex processes of value creation in the emerging e-service industry but also advance marketing and service research in general. Studies 2 and 3 investigate the analysis of online multichannel consumer behavior in times of big data. Firms can choose from a plethora of channels to reach consumers on the Internet, such that consumers often use a number of different channels along the customer journey. While the unprecedented availability of individual-level data enables new insights into multichannel consumer behavior, it also makes high demands on the efficiency and scalability of research approaches. Study 2 addresses the challenge of attributing credit to different channels along the customer journey. Because advertisers often do not know to what degree each channel actually contributes to their marketing success, this attribution challenge is of great managerial interest, yet academic approaches to it have not found wide application in practice. To increase practical acceptance, Study 2 introduces a graph-based framework to analyze multichannel online customer path data as first- and higher-order Markov walks. According to a comprehensive set of Summary VI criteria for attribution models, embracing both scientific rigor and practical applicability, four model variations are evaluated on four, large, real-world data sets from different industries. Results indicate substantial differences to existing heuristics such as “last click wins” and demonstrate that insights into channel effectiveness cannot be generalized from single data sets. The proposed framework offers support to practitioners by facilitating objective budget allocation and improving team decisions and allows for future applications such as real-time bidding. Study 3 investigates how channel usage along the customer journey facilitates inferences on underlying purchase decision processes. To handle increasing complexity and sparse data in online multichannel environments, the author presents a new categorization of online channels and tests the approach on two large clickstream data sets using a proportional hazard model with time-varying covariates. By categorizing channels along the dimensions of contact origin and branded versus generic usage, Study 3 finds meaningful interaction effects between contacts across channel types, corresponding to the theory of choice sets. Including interactions based on the proposed categorization significantly improves model fit and outperforms alternative specifications. The results will help retailers gain a better understanding of customers’ decision-making progress in an online multichannel environment and help them develop individualized targeting approaches for real-time bidding. Using a variety of methods including qualitative interviews, Markov graphs, and survival models, this dissertation does not only advance knowledge on analyzing and managing online consumer behavior but also adds new perspectives to marketing and service research in general. Acknowledgments VII Acknowledgments Acknowledgments This project would not have been possible without the support of many. First and foremost, I want to express my deepest gratitude to my advisor Jan Schumann for his continuous support, encouragement, and inspiration. I count myself incredibly lucky to have you as a mentor. Many thanks also go to my second advisor Florian von Wangenheim for his invaluable guidance and for immediately welcoming me into the DTM “family.” Without my colleagues in Munich and Passau this dissertation would not be the same—and writing it would have been much less fun. Thank you so much to all of you! Armin März deserves a special mention for being a great partner in crime and roommate from day one. I would also like to thank my other co-authors Ingo Becker, Werner Kunz, and Sebastian Klapdor, as well as my cooperation partners at intelliAd, Feld M, and ValueClick. It was a pleasure to work with you. I gratefully acknowledge the institutional support I have received during my dissertation. My study was conducted in the context of the project “Fre(E)S: Produktivität kostenfreier E-Services” (FKZ: 01FL10038), which was funded by the German Federal Ministry of Education and Research (BMBF) and supported by the German Aerospace Center (DLR). A big thank you also goes to Passau University for providing me with the means to finish this dissertation and to Technische Universität München, the University of Massachusetts Boston, and the Cambridge Public Library for giving me shelter. Special thanks to Werner Kunz for inviting me to Boston and being a great mentor during my time in the US and beyond! I sincerely thank my friends and family for pushing me to take on this challenge and keeping my spirit up along the way. Finally, thank you, Sebastian, for accompanying me on this journey—and for all the rest. Short Table of Contents IX Short Table of Contents Short Table of Contents Summary ................................................................................................................. V Acknowledgments .................................................................................................. VII Short Table of Contents .......................................................................................... IX Table of Contents ................................................................................................... XI List of Figures ....................................................................................................... XIV List of Tables ......................................................................................................... XV List of Abbreviations.............................................................................................. XVI 1 Introduction ........................................................................................................... 1 2 There Is No Such Thing as a Free Lunch: Nonmonetary Customer Value Contributions in Free E-Services ......................................................................... 11 3 Mapping the Customer Journey: A Graph-Based Framework for Online Attribution Modeling ............................................................................................. 33 4 Analyzing Multichannel Online Customer Journeys for an Online Retailer: A Categorization Approach ..................................................................................... 65 5 Conclusion .......................................................................................................... 99 6 References ........................................................................................................ 105