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Most of today’s digital marketing campaigns which are sent through email and mobile messaging are bulk campaigns which deliver the same message at the same time to all customers, regardless of their needs and preferences. The outcomes are bad customer experience, low engagement and low conversion rates. Modern marketing automation tools aim to facilitate personalized communications, such as scheduling of individual marketing messages based on each individual subscriber’s profile. This research focuses on the problem of automatically deciding on the optimal date and time for sending consent-based personalized marketing messages. We specifically focus on the case of repeat consumers of consumer packaged goods (CPG) which require regular replacement or replenishment. The objective is to timely anticipate the needs of consumers in order to increase their level of engagement as well as the rate at which they repurchase products. The proposed solution is based on a regression model trained with transactional data and instant messaging metadata. We describe the way such a model can be created and deployed to a scalable high-performance environment and provide pilot evaluation results that suggest a significant improvement in marketing effectiveness.

Predicting the Optimal Date and Time to Send Personalized Marketing Messages to Repeat Buyers