by Sarah Seigle, Academic Impressions
More institutions are using small and big data sources across the prospective and current student lifecycle to inform key decisions related to enrollment and retention. To take a look at how Strategic Enrollment Management (SEM) is changing and to get ideas for how institutions can better leverage their data, we reached out to a panel of three prominent experts:
- John Dysart (president of the Dysart Group)
- Randall Langston (assistant vice president for enrollment management at the College at Brockport)
- Brian Williams (vice president for enrollment and institutional analytics at John Carroll University).
These three, joined by Laura Jensen (associate provost for planning and effectiveness at Colorado State University), will also be discussing this topic in more depth at our upcoming conference Effectively Leveraging Data in Enrollment Management.
Here is what they shared with us today.
What Advances are We Making? What are the Opportunities?
Sarah Seigle. Real-time data and analytics have become more important across all stages of the student lifecycle. Looking at three of the biggest functions of a strategic enrollment management plan—student recruitment, financial aid, and retention—where have you seen many institutions make recent advances in incorporating data into their decision-making?
John Dysart. I have seen more colleges bring analytics into financial aid. In recent years, financial aid leveraging has become nearly commonplace. It seems that the majority of colleges and universities are now using some type of leveraging formula, awarding grid or preferential packaging model.
Brian Williams. With areas such as admission yield, financial aid awards, or identifying at-risk students, schools are becoming really good at finding patterns in their historic data and then searching the present applicant pool for “like” students and implementing strategies accordingly based on behaviors and trends that emerge from prior cycles.
Randall Langston. The most significant advances that higher education institutions are making appear to be in “big data” analytics. One important area of Enrollment Management that is being analyzed by colleges and universities is that of admit to retain or admit to graduation. It is one thing to do all the right things to recruit an academically talented and diverse class, but another thing altogether to advocate, post-admissions, for student success and degree completion – outcomes that fall traditionally under the purview of Student Affairs. There needs to be an increasing acknowledgement that the success of EM and Student Affairs is tied together, and that these two functions are tied invariably by a common purpose. By working closely with Student Affairs and retention staff, EM leaders can better understand what variables predict not only student enrollment behavior but also degree completion.
John Dysart. There are so many untapped possibilities with retention. Extraordinary technology is now available to help colleges monitor the important indicators for student success—and the technology is affordable. Given the large number of students who start but do not complete degrees, the opportunities to impact lives and the economy are great. But it is important to understand that advances in critical data collection are meaningless unless colleges and universities also invest in the personnel necessary for active intervention.
Sarah Seigle. Where else do you see significant opportunities for institutions to rework or rethink their strategies for student recruitment, financial aid, or retention, to become more data-informed?
Brian Williams. I think big challenges still exist in a number of places. First, consider the realm of social media. Many of the analytics of the recruitment process have become highly anonymous. Online ads, videos watched, and traffic to our websites are very rarely aligned with the names of specific students. Our profession is still very dependent on knowing who we are talking to so that we know what high schools to visit, what fairs were successful, etc. Yet when we move to web analytics, we need to trust impressions and clicks as indicators of the student behaviors we seek. The cost/benefit understanding of web analytics is still in its infancy for many in our profession.
Randall Langston. Institutions need to be especially aware of the “how” and “why” of data collection and analysis. Possessing and handling data and making surface-level assumptions simply does not go far enough. SEM today is multi-faceted and complex, which places a much higher level of demands on EM leaders.
Concrete EM planning is paramount. This requires clearly defining the mission and purpose of your school’s recruitment, cultivation, matriculation, and retention efforts. By working closely with frontline offices such as Financial Aid, Academic Advising, Retention and Student Affairs units, EM leaders can effectively galvanize institutional support for true and authentic wholesale EM change.
Another thing that’s key is the implementation of software that allows the EM professional to gain high-level intelligence through the distillation of data, while also being flexible enough that the EM can see the “big picture” of how this high-level data informs more operational, “in the weeds” data. Specifically, EM leaders need to have the insight to discern how data such as “heat maps,” patterns in student activities, and progression from point to point on the recruitment funnel play into the enrollment behavior for a school’s specific student population. Successful recruitment is all about discovery, emerging student pattern identification, data, and how that school can successfully extract this into a credible action plan across all parts of both the admissions and EM funnel.
Examples of Success
Sarah Seigle. What is one thing that you yourself have accomplished at your own institution that you are particularly proud of and that has helped make your SEM strategies more data-informed?
Brian Williams. I am proud of work we did a number of years ago to improve our summer enrollment. We pulled a big data set on the courses our currently enrolled students petitioned as transfer work over the summer. That data set told us a great deal about what courses students were taking, at what school. We learned so much; in some cases, it showed us where we were not offering courses that our students wanted, cases where they were in Cleveland and taking courses at other local schools. Also, in cases where students were taking courses further away (our out-of-state students), the courses they wanted helped inform some of our online offerings. While not a sophisticated predictive analytics project, the simple pulling of data right in front of us led to great result.
John Dysart. At one client institution, the introduction of data-informed decision-making in the recruitment process produced record enrollments. New student enrollments grew by 70% in a single cycle. The university did not change its location, did not initiate any new academic programs or co-curricular activities that year, and did not enhance the physical plant. The only change was the design and implementation of a number of new comparative weekly reports and accountability metrics along with the willingness to adjust strategies and tactics on a weekly basis in response to the trends illustrated by these reports.
Randall Langston. At our institution we are especially gratified to have successfully integrated predictive analytics into the approaches for CRM. No longer do we have two separate systems working autonomously from each other. Our EM planning and approaches integrate these two powerful technologies in order to leverage recruitment and statistical data analytics into one method that is exclusively data-driven. We are now able to engage in empirical statistical data analytics related to predictive likelihoods of students at the individual level at all parts of the admissions funnel, with the CRM intuitively pushing out messages. This approach has completely revolutionized the way we recruit students and is one major factor that has contributed to some of our highest enrollment numbers in over thirty-five years on our campus.
Practical Advice for Other Institutions
Sarah Seigle. What are 1-2 pieces of practical advice that you would offer to institutions that are currently striving to become more data-informed in their approach to SEM?
Brian Williams. First, start general. Just start gathering and using data. When you are dealing with real-time data sets, it will be messy; there will be anomalies. You can spend a lot of time obsessing about data quality, or you can start visualizing data to get a general sense of trends and patterns, and then gather and clean more precise data later to affirm your hunches and instincts.
Related to that, the most important part of working with data is asking and framing good questions. I have found it essential to frame good questions at every step of the analytic process. A good question helps gather the right data, involve the right people, and provide a direction for analysis. When you do not start with a good question, it is easy to cross the fine line between necessary exploration and data “wandering.”
John Dysart. Data-informed decision-making in enrollment management is possible even for smaller colleges and universities with limited resources. While you may not have the capability of a fully staffed IT department, this does not mean that you will be unable to utilize data to improve outcomes.
Be realistic. Start with the basics by tracking elements readily available:
- Admission operations should keep an eye on comparative totals for inquiries, applications, acceptances and deposits on a weekly basis for the last five years. Calculate and compare year-to-date conversion and yield rates. Be prepared to take immediate action if the comparative analysis reveals shortfalls or disappointing results at any stage in the recruitment funnel.
- Financial aid officers should produce weekly processing reports to track the number and percentage of admission applicants and currently enrolled students who have applied for financial aid, the number and percentage who have been packaged, and the number and percentage with completed folders ready for disbursement. Set monthly goals for each stage in the financial aid process and be prepared to take action if the institution is struggling to meet those goals at any of these stages.
- The majority of colleges and universities have early alert systems in place, but make sure that your institution is adequately staffed to intervene appropriately when challenges arise. Noting that a student is experiencing academic difficulties is not useful unless professionals are available to provide proactive assistance.
Randall Langston. I would recommend that the college first establish a “vision document” that maps out the direction the school is seeking to go with their data. This document should include, but not be limited to, the vision, the goals they seek to meet, steps for planning and implementation, and outcome measures. Teams of staff (with appropriate skill sets) should be included to engage in discussion and be credible stakeholders in the process of producing this vision document.
I would also recommend that schools do the necessary legwork that places them in an optimal position during pre-data collection, while they are gathering data, and later in the environment informed by that data:
- Before data collection: The school should identify data stewards and strategic high-level staff who could perhaps attend a conference on data analytics, or research topics empirically through published materials readily available on the web. These individuals could meet with colleagues at other institutions to gather examples and information about how to implement analytics.
- At the data-gathering stage: Schools should be constantly assessing the data and linking it directly back to the goals and objectives outlined in the vision document. Often, data begets more data, and there is the risk of running in many different directions, chasing hypotheses related to the data. For that reason, schools should start by trying only to manage that information that they can readily assess. Then, they can work their way up to more complex data-gathering approaches and hypothesis generation.
- Finally, after data has been gathered: How the school uses the data will be key. If the school is following their strategic planning process effectively, they should be able to “make meaning” of the data and apply it (implement it) to their specific areas of concern.
Sarah Seigle. Thank you all! I’m excited to discuss this further with you at the Effectively Leveraging Data in Enrollment Management conference.