We study how opinion leadership and social contagion within social networks affect the adoption of a new product. In contrast to earlier studies, we find evidence of contagion operating over network ties, even after controlling for marketing effort and arbitrary systemwide changes. More importantly, we also find that the amount of contagion is moderated by both the recipients' perception of their opinion leadership and the sources' volume of product usage. The other key finding is that sociometric and self-reported measures of leadership are weakly correlated and associated with different kinds of adoption-related behaviors, which suggests that they probably capture different constructs. We discuss the implications of these novel findings for diffusion theory and research and for marketing practice.diffusion of innovations, opinion leadership, social contagion, social networks

Consumers share word of mouth face to face, over social media, and through a host of other communication channels. But do these channels affect what people talk about and, if so, how? Laboratory experiments, as well as analysis of almost 20,000 everyday conversations, demonstrate that communicating via oral versus written communication affects the products and brands consumers discuss. Compared to oral communication, written communication leads people to mention more interesting products and brands. Further, this effect is driven by communication asynchrony and self-enhancement concerns. Written communication gives people more time to construct and refine what to say, and self-enhancement motives lead people to use this opportunity to mention more interesting things. These findings shed light on how communication channels shape interpersonal communication and the psychological drivers of word of mouth more broadly.C onsumers communicate through a variety of different channels. They talk face to face, chat over the phone, and text back and forth. Further, the advent of social media has led more and more people to communicate with others via Facebook, Twitter, blogs, and other online channels.But while it is clear that people communicate frequently with others, and that such communication has important implications for consumer behavior, less is known about how the medium might shape the message. Does the particular channel consumers communicate through affect what they talk about and share, and if so, how?

The tailoring of a firm's marketing mix to the individual customer is the essence of one-to-one marketing. In this paper, we distinguish between two forms of one-to-one marketing: personalization and customization. Personalization occurs when the firm decides what marketing mix is suitable for the individual. It is usually based on previously collected customer data. Customization occurs when the customer proactively specifies one or more elements of his or her marketing mix. We summarize key challenges and knowledge gaps in understanding both firm and customer choices in one-to-one markets. We conclude with a summary of research opportunities.

This article is based on the first author's doctoral dissertation at Columbia University. The two coauthors are his dissertation advisors. The authors thank the other dissertation committee members (Kamel Jedidi, Yuxin Chen, and K. Sudhir) for their extensive comments and suggestions. The first author thanks Z. John Zhang for insightful discussions. The authors also thank Carl Mela and the Teradata Center at Duke University for providing the data. Finally, the authors thank the Marketing Science Institute for providing financial support for this project.

Omnichannel marketing is often viewed as the panacea for one-to-one marketing, but this strategic path is mired with obstacles. This article investigates three challenges in realizing the full potential of omnichannel marketing: 1) data access and integration; 2) marketing attribution; and 3) protecting consumer privacy. While these challenges predate omnichannel marketing, they are exacerbated in a digital omnichannel environment. This article argues that advances in machine learning (ML) and blockchain offer some promising solutions. In turn, these technologies present new challenges and opportunities for firms, which warrant future academic research. We identify both recent developments in practice and promising avenues for future research.