Leveraging machine learning to meet goals, serve prospective and current students at ASU

The EdPlus team at ASU leverages various machine learning models to meet the goals of different departments while serving prospective and current students. Our comprehensive web behavior data is used to power enrollment propensity models that output a lead score for each prospect who expresses an interest in ASUOnline. Qualitative data from surveys, calls, SMS and chats from the enrollment and student advising departments are used to build sentiment analysis models. These models help us understand the web activities that lead to a high propensity to enroll, predict the possible next funnel steps, prioritize the call and advising center operations, and evaluate the current perceptions prospects and students share about ASU. Finally, the models are used to optimize marketing strategies and meet enrollment and retention goals. Join us for a presentation on how we process institutional data and build, deploy and leverage models daily.


  • Tanner Bradshaw — Arizona State University
  • Anastasia Serebryakova — Arizona State University

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