## 1 Introduction  
Massive open online courses (MOOCs) are known for their high dropout rates [1; 2; 3; 4], the meaning of which has prompted debate and commentary [1; 5; 6; 7]. Yet even online courses offered as part of degree-granting programs have lower completion rates than their face-to-face counterparts [8; 9; 10; 11; 12]. Importantly, online courses can have substantial costs of development and delivery [13; 14]. In such an environment, commentators from across the educational community have pondered the cost, revenue,and profit structure of online education [7, 15, 16]. Few doubt, though, that distance education is here to stay. What might be done to enhance performance, satisfaction, or completion rates of its customers? A recent review of six major MOOC platforms found that students in online courses typically receive only rudimentary feedback on their performance and progress [17], suggesting that there is room for improvement in the amount and quality of feedback given to students in eLearning environments. This article advocates microtargeting of students in online courses for the purpose of improving performance, satisfaction, or retention. Education is an industry that cares acutely about the _individual_: a student's preparation for academic work, performance in courses, completion of programs, employment after graduation, and satisfaction with faculty and their courses, to name a few. Researchers such as [PERSON] [18] have long argued that different students benefit from different instructional methods, suggesting that instructors, administrators, and students would benefit from wider application of microtargeting technologies to educational settings. Indeed, although microtargeting has enjoyed great successes in diverse industries such as customer relationship management, fraud and terrorism detection, and even politics [19, 20, 21], education has only begun to embrace the full potential of predictive analytics [22]. We propose that microtargeting of students in online courses be accomplished using an information delivery system based on statistical predictive models. Even though analytics are now finding their way into online course delivery platforms [23], adoption of these methods is not widespread. Perhaps technologists who develop learning management systems (LMSs) aren't aware of the power of predictive analytics or don't grasp how to conduct and implement a predictive analytics project. This article hopes to address the gap. Consider these examples of messages to students that might be delivered via the information system. Suppose that students who begin courses later than others also have lower completion rates. An information system might deliver daily messages to absent students until they begin the course. The message, though, would not be something like, \"time to get started\". Instead, the message would be framed numerically--e.g., that a student's probability of completing the course has now dropped from 73% to 58%. The numerical nature of the message is intended to be striking and, thus, impactful. As another example, perhaps the level or quality of a student's participation in a threaded discussion predicts performance on a graded assignment [24]; the system could explain that link and encourage students to change behavior or seek help as needed. Or perhaps a message would advise whether a student will learn better working collaboratively or individually [25]. As another example, suppose that a midterm assessment suggests that a student has forgotten foundational concepts from earlier in the course. The system might urge the student to relearn those materials with a message such as: \"without revisiting the Chapter 2 materials, your predicted grade in the course will be no better than C-. Relerning these materials improves your projected grade to B+\". The substance of the message would be aimed at the expected audience.