Description
Organizations know that retaining a customer is more cost effective than recruitment. Data analytics provide mechanisms for explaining and predicting customer retention. Challenges inherent to customer retention are even more pervasive within higher education, with freshman attrition being a significant problem. However, an untapped opportunity exists within most institutions’ existing systems infrastructure: identification card systems used to access campus facilities and campus services. Using knowledge from information systems, this research examines the potential of card swipe data to predict student retention. These ‘card swipes’, bound to a single student primary key in the database, provide a profile of student interaction in the campus environment that is rarely investigated by institutional researchers. This provides a new opportunity for furthering organizational intelligence within higher education by providing new dimensions of a student’s behavior. This research examines retention by social factors in addition to traditional academic factors.
Recommended Citation
Bradberry, Caleb; Ray, Andrew; Wayman, Matt; Dhami, Jaget; Charnock, Jonathan; and Pittges, Jeff, "Explaining and Predicting First Year Student Retention via Card Swipe Systems" (2017). AMCIS 2017 Proceedings. 3.
https://aisel.aisnet.org/amcis2017/SemanticsIS/Presentations/3
Explaining and Predicting First Year Student Retention via Card Swipe Systems
Organizations know that retaining a customer is more cost effective than recruitment. Data analytics provide mechanisms for explaining and predicting customer retention. Challenges inherent to customer retention are even more pervasive within higher education, with freshman attrition being a significant problem. However, an untapped opportunity exists within most institutions’ existing systems infrastructure: identification card systems used to access campus facilities and campus services. Using knowledge from information systems, this research examines the potential of card swipe data to predict student retention. These ‘card swipes’, bound to a single student primary key in the database, provide a profile of student interaction in the campus environment that is rarely investigated by institutional researchers. This provides a new opportunity for furthering organizational intelligence within higher education by providing new dimensions of a student’s behavior. This research examines retention by social factors in addition to traditional academic factors.