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November 11, 2019 | Institutional data analytics

The Value of Student Segmentation

Rising competition for college and university students will only continue to grow as the number of high school graduates shrink over the next decade, student demographics shift and other financial pressures persist. For many higher education institutions, understanding that each student has unique needs, attitudes, behaviors and motivations may be critical to recruitment and retention.

Traditionally, segmentation has been used for marketing purposes, not for analyzing student success. But collecting, combining and analyzing student data – for example, persistence rates, employment status and whether undergraduates are veterans or active-duty military – can help institutions understand the various services and outreach programs needed to support students during the college journey.

 Not just a one-time exercise, once finalized, student segmentation can be tracked and built into analyses moving forward for improving prospect targeting, yield rates, retention and completion – cutting across the student lifecycle.

For example, University A was experiencing drops in enrollment, retention and completion rates. University leaders wanted to better understand enrollment trends and, at the same time, create a plan to retain existing students. The first step was to identify student segments. Keeping in mind that the most effective segments are those that are easily understandable with characteristics varying enough to force each segment into a distinct bucket, universities can approach identification a number of ways:

  • Conduct an exploratory analysis of all known variables to determine differences in student behaviors, preferences and performance;
  • Interview student affairs practitioners, advisors or those who work directly with students to establish unique cohorts of students as a basis for segmentation;
  • Identify segments through various machine learning algorithms.

University A identified five segments of students by analyzing pre-determined variables for each. The segments included:

  • First time, full-time freshman
  • Community college transfers
  • Adult student - online
  • Part-time commuter students
  • Honors college

Unique characteristics

University A discovered its community college transfers have the highest retention and graduation rates, while first time, full-time freshman struggle in their first few semesters. The findings led to the creation of a streamlined process for local community college students to easily transfer into University A by creating an articulation agreement with automatic admission criteria and giving first time, full-time freshman the opportunity to receive extra support from a student mentor. The data also showed that honors college students, while the most academically prepared for the rigors of freshman year, have very high transfer rates after their freshman year. Through further analysis University A found that financial variables are the primary predictor of this behavior, moving the university to take a close look at its scholarship programs and how it calculates merit aid.

Finally, to institutionalize and operationalize university data in real time across the student life cycle, University A decided to tag each student as belonging to a specific segment at the time of enrollment in order to streamline efforts to create tailored strategies for supporting all student segments. Taking advantage of existing data to embark on segmentation, rather than creating a single strategy for all students, can solidify an institution’s path forward in multiple areas – enrollment, retention, finances and more. 

Darren Catalano is the CEO of HelioCampus, leading company strategy and operations. Prior to joining HelioCampus, Darren was the Vice President of Analytics at the University of Maryland Global Campus from 2011 to 2015, where he helped develop a culture of data-driven decision making. 

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