Essays on the digital consumer
Subtitle: models of engagement, updgrade, and referral behaviors
By: a dissertation presented by Clarence Lee to Sunil Gupta, Vineet Kumar, Elie Ofek, Thomas J. Steenburgh in partial fulfillment of the requirements for the degree of Doctor in Business Administration in the subject of marketing
Contributors: Harvard Business School
Publisher: ProQuest, UMI Dissertation Publishing
Published: 2014
Place of publication: Ann Arbor, MI
Language: eng
Number of pages: 132
Copyright date: 2014
Subjects: Consumer behavior; Teleshopping; Electronic commerce; Marketing
Description: In this dissertation, I present three essays on two empirical models of consumers using Web services. Business-to-consumer (B2C) Web services, such as Facebook, Dropbox and Pandora, have become a major part of the economy. Due to the low cost of digital distribution, these firms can provide their services for free, with the goal to attract a large customer base, while earning revenue by relying on advertisements or charging a small subset of customers for premium features. My goal is to characterize the various stages that a consumer faces when using a Web service: from adopting the service, to using it for personal and social needs, and to paying for the service. In addition, I also model the customer referral process, where the customer becomes a marketing instrument to encourage other customers to adopt. In the first essay, I explore the relationship between how customers find out about a service and how active they are when using the service. I estimate a hidden Markov model (HMM) of consumer behavior, and I characterize how the firms' social media efforts may encourage customers to be more active. In the second essay, I examine the relationships among usage, payment (upgrades), and referrals. I estimate a single agent dynamic structural model to capture these consumer decisions. Lastly, I conclude with an essay that presents the computational challenges in estimating the HMM and the dynamic structural model in a Bayesian fashion, and I also discuss how I use various estimation techniques, parallelization, and Amazon Elastic Compute Cloud to address these issues.
Notes: UMI Number: 3624380.

D.B.A. Harvard Business School 2014 Clarence Lee's thesis.

Includes bibliographical references (pages 127-132).

Photocopy. Ann Arbor, MI : UMI Dissertation Services, 2014.