Recognizing Drexel’s commitment to promoting and supporting student success, along with the University’s need to make data-driven strategic decisions that are as effective and efficient as possible, the Enrollment Analytics team works to empower stakeholders to make strategic decisions using advanced analytic information. Providing strong knowledge of higher education and strategic enrollment management, supplemented by top-level skills in data management, data visualization, and predictive modeling, Enrollment Analytics allows the University to make data-driven strategic decisions regarding enrollment and financial projections that are as effective and efficient as possible.

Mission

Analyzing, visualizing and building predictive models with data to manage enrollment strategically from the moment students consider applying to Drexel through graduation.

Vision

Empowering University stakeholders to make data-driven, strategic decisions in a dynamic and competitive higher-education marketplace. Using our expertise to formulate, propose, and implement optimal enrollment strategies.

Strategic Initiatives

• Enhance existing data infrastructure and reporting systems to provide actionable business intelligence. There is often a disconnect between raw data that exists within a data system and actionable information. We seek to bridge that divide by providing estimates of the risks and benefits associated with making decisions, by connecting any analysis we conduct to the mission of EMSS and Drexel University, and by learning as much as possible about the underlying business processes that generate the data in the first place.
• Lead the industry in the management and modeling of prospect populations. Nearly all universities rely on purchasing contact information of high school students from vendors to build a list of potential prospects to which to communicate and generate applications. The extent to which universities use data analytics in this critical business process is minimal. We have an opportunity to use predictive modeling to identify the likelihood that individual prospects may apply and help inform the creation of tailored communications and marketing plans to increase applications at lower costs.
• Assist with transition to Responsibility-Center Management. Having recently transitioned from a centrally managed budget model to responsibility-center management, significant opportunities exist for our team to assist the schools and colleges manage their budgets by providing tools to forecast enrollment and revenue at the program level.
• Improve admissions modeling capabilities. Forecasting new student enrollment is one of our team's core responsibilities. We seek to improve our capabilities in this crucial area by incorporating cutting-edge statistical and machine-learning techniques and employing an ensemble approach to determining forecast uncertainty. For our team, admissions modeling is a year-long endeavor, as we are constantly attempting to learn as much as possible from new data as the University continues to implement its new recruitment and enrollment strategies.
• Grow the team's technical skills. As data become increasingly prevalent, the technical tools used to synthesize operational information from data are rapidly evolving. Our team's skills must grow and improve in parallel to take advantage of the best possible methods and techniques available to achieve our mission.