Bayesian Inference for Assessing Effects of Email Marketing Campaigns
提出一种基于计数过程的贝叶斯方法，量化电子邮件营销活动的效果，并分析客户特征的影响，发现邮件促销能提升短期和长期购买率，且近期不活跃的客户更易响应促销。
Email marketing has been an increasingly important tool for today’s businesses. In this article, we propose a counting-process-based Bayesian method for quantifying the effectiveness of email marketing campaigns in conjunction with customer characteristics. Our model explicitly addresses the seasonality of data, accounts for the impact of customer characteristics on their purchasing behavior, and evaluates effects of email offers as well as their interactions with customer characteristics. Using the proposed method, together with a propensity-score-based unit-matching technique for alleviating potential confounding, we analyze a large email marketing dataset of an online ticket marketplace to evaluate the short- and long-term effectiveness of their email campaigns. It is shown that email offers can increase customer purchase rate both immediately and during a longer term. Customers’ characteristics such as length of shopping history, purchase recency, average ticket price, average ticket count, and number of genres purchased also affect customers’ purchase rate. A strong positive interaction is uncovered between email offer and purchase recency, suggesting that customers who have been inactive recently are more likely to take advantage of promotional offers. Supplementary materials for this article are available online.