With the growth of online commerce and the development of sophisticated data collection methods, organizations have more data than ever before about their customers. At the same time, computational power and sophisticated modeling is becoming increasingly cheap. Analytics performed on this data, if used well, have the potential to revolutionize marketing by providing firms with the right knowledge at exactly the right moment to gain a new customer, complete a sale to an existing customer, or stop a customer from churning. It is time, therefore, to connect the dots between the marketing and information systems, bringing customer data and marketing efforts together with advanced computational methods and robust modeling techniques to provide easy-to-use but powerful analytical methods. Such a combination will enable the creation of data scientists who are able to study, learn from and predict the effect of marketing actions. However, in order for advanced analytics to be useful they also have to be understandable and automatic, giving managers the ability to find the right piece of information at a moment’s notice. Developing digital marketing analytics that take advantage of the rich data in existence and at the same time are functional is a complex and interesting challenge.