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Although email marketing is highly profitable and widely used by marketers, it has received limited attention in the marketing literature. Extant research has focused on either customers’ email responses or the “average” effect of emails on purchases. In this article, the authors use data from a U.S. home improvement retailer to study customers’ email open and purchase behaviors by using a unified hidden Markov and copula framework. Contrary to conventional wisdom, the authors find that email-active customers are not necessarily active in purchases, and vice versa. Furthermore, the number of emails sent by the retailer has a nonlinear effect on both the retailer’s short- and long-term profitability. Through a counterfactual study, the authors provide a decision support system to guide retailers in making optimal email contact decisions. This study shows that sending the right number of emails is vital for long-term profitability. For example, sending four (ten) emails instead of the optimal number of seven emails can cause the retailer to lose 32%(16%) of its lifetime profit per customer. [ABSTRACT FROM AUTHOR]

The rise of new media is helping marketers evolve from digital to interactive marketing, which facilitates a two-way communication between marketers and customers without intruding on their privacy. However, while research has examined the drivers of customers’ opt-in and opt-out decisions, it has investigated neither the timing of the two decisions nor the influence of transactional activity on the length of time a customer stays with an e-mail program. In this study, the authors adopt a multivariate copula model using a pair-copula construction method to jointly model opt-in time (from a customer’s first purchase to the opt-in decision), opt-out time (from the opt-in decision to the opt-out decision), and average transaction amount. Through such multivariate dependences, this model significantly improves the predictive performance of the opt-out time in comparison with several benchmark models. The study offers several important findings: (1) marketing intensity affects opt-in and opt-out times, (2) customers with certain characteristics are more or less likely to opt in or opt out, and (3) firms can extend customer opt-out time and increase customer spending level by strategically allocating resources. [ABSTRACT FROM AUTHOR]