The revolution in the digital economy has rapidly changed the way companies manage, execute and measure the effectiveness of marketing strategies and the delivery of marketing products and services. The widespread growth in digital marketing tools and platforms have led to diverse sources of marketing, advertising and consumer behavioral data, often available in real time. This opens the door to the application of a wide range of AI techniques in areas traditionally considered parts of the marketing science (MS). For example, this includes the use of machine learning, deep learning, sequential decision making, bandits and sequential testing, recommendation systems, game theory, knowledge representation, market design and optimization, and so on for the purpose of marketing resource optimization, managerial decision making, competitive behavior modeling, deconstruction of consumer behavior, and campaign automation and optimization.Research in this field has been largely carried in separate communities until now. Within the AI and machine learning community, the focus has been on developing new and more efficient computational models and techniques, geared towards specific tasks. Within the MS community, the focus has been on exploiting machine learning methods and scalable data methods for addressing important business problems that marketers face. Consequently, researchers publish in separate journals and conferences. It is our conviction that these two separate communities have a lot to benefit from each other’s work, problems and insights.