Estimating Promotion Effects in Email Marketing Using a Large-Scale Cross-Classified Bayesian Joint Model

• Activity Number: 477 - Statistical Methods for New Age Marketing Problems
• Type: Topic Contributed
• Date/Time: Wednesday, August 10, 2022 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Marketing
• Author(s): Gourab Mukherjee* and Sabyasachi Mukhopadhyay and Wreetabrata Kar
• Companies: University of Southern California and Indian Institute of Management and Purdue University
• Keywords: promotion email; marketing; joint modeling; logistic regression; bayesian hierarchical model

Abstract

We consider a large-scale, cross-classified nested joint model for modeling customer responses to opening, clicking, and purchasing from promotion emails. Our logistic regression-based joint model contains crossing of promotions and customer effects, and allows estimation of the heterogeneous effects of different promotion emails after adjusting for customer preferences, attributes, and historical behaviors. Using data from an email marketing campaign of an apparel retailer, we exhibit the varying effects of promotions not only based on the contents of the email but also across the three different stages of the conversion funnel. We conduct Bayesian estimation of the parameters in the joint model by using a block Metropolis-Hastings algorithm that not only incorporates nested subsampling to tackle the severe imbalance between conversions and no conversions, but also uses additive transformation-based modifications of random walk Metropolis to scale estimation for large numbers of customers. Based on the promotion estimates from the model, we demonstrate how marketers can use promotions to increase brand awareness or increase purchases.