For years, college and university enrollment was driven by gut instinct. Staff felt that two additional open house dates in the spring would secure the class number they needed, so they budgeted for it. Gut feel still plays a role, but as funnel leaks grow larger and proactive search spend becomes more of a make-or-break decision, the stakes have become too high for “what we’ve always done.” Data isn’t perfect, but it has the benefit of being real.
Why Siloed Data is Costing You Applicants
Many schools collect the necessary data for effective and quick admission processing. The issue is not with the data that is being collected but where it is stored. Data is often stored in separate siloed systems and does not communicate with one another.
For instance, when a prospective student fills out a form during their visit to campus that information may reside in a marketing database. The admissions office staff answering a phone inquiry has no way of knowing that the student visited the campus. When an applicant fills out and submits a part of their application form, the financial aid team is unaware until weeks later. Each time the applicant interacts with a different office, important information gets lost as there is no continuous thread connecting all the interactions.
These siloed systems with disconnected data, while frustrating, produce tangible consequences that cost time and money. Unnecessarily delayed responses to questions, repetitive contacts to students, lost tuition due to slow or low aid offer and confused or frustrated applicants who opt for a competitor are a few of the possible consequences of siloed data. The solution to these issues is not to hire more staff but to integrate the data silos and create a unified system that shares important data across all departments.
From Reaction to Prediction
The transformation that delivers better enrollment results isn’t simply the technical act of connecting your various software platforms. It’s about leveraging the resulting network of up-to-date and cohesive information to diagnose trouble before it metastasizes into lost tuition.
Predictive analytics is a term thrown around so much it can feel like buzzword soup. But its value to the education sector, by now, is just fundamental. By analyzing your historical data, it can reveal trends you’d never notice by staring at a grid of numbers, like which zip codes actually furnish you with students who fully complete an application, versus those that just ‘like’ the idea; or at what point and for what reason in the application sequence the students from that perfect zip code tend to stall out. That kind of information can’t be easily inferred, yet it’s essential.
Armed with that knowledge, you no longer have to make the assumption (often unproven, always expensive) that casting the widest net financially feasible is the best approach. Rather than treating all possible applicants as equally likely and equally valuable, you prioritize the exact channels and strategies that provide the best ROI.
The Infrastructure Underneath it All
Getting this right means you need to know how two arbitrary tools in a university tech stack actually function together. A CRM does early-stage recruitment: it tracks inquiries, logs communications, personalizes outreach at scale. But the CRM isn’t the system of record for your enrolled students. When a student has been admitted, that information needs to be handed off to the system of record that manages your students’ records, registration, financial aid, and transcripts, cleanly.
This is where many institutions bleed accuracy. Having student information systems explained in plain language helps your staff understand what the SIS actually does, and why you can’t count on someone to just key in all that critical information by hand at the front desk. If your systems of recruitment and enrollment aren’t sharing that data with true interoperability, you get manual re-entry. Which means errors, delays, and students wondering why the guy at the desk with a rubber stamp is typing in their address.
A cloud-based architecture makes that level of integration substantially easier to maintain, and it provides staff with real-time data on whatever device they happen to have handy rather than requiring them to go digging for the office laptop to check information that’s two days out of date.
Personalization Isn’t Optional Anymore
74% of students who are applying feeling like a college knew what they were looking for and being able to meet their needs were influential or very influential in their decision to apply to a college (Weiss-McGrath).
Enrollment Planning as an Ongoing Discipline
Improving how you manage your data makes a huge difference at the front end of the funnel where prospects become applicants and the most promising applicants become students. A clean, well-organized data infrastructure will help institutional researchers develop more predictive models to decide what markets to recruit in, what messages to use, and what students are most likely to apply, accept, and enroll.
Those kinds of insights don’t just give you a competitive advantage. They’re table stakes for many high schools and most of your peer and competitor institutions. Outreach and engagement budgets are tightening across higher education; there’s no slack to waste on students who were never going to come.
For successful recruitment organizations, data unification demands significantly less time, effort, and expertise than it used to. New cloud data warehouses and modern data management services are simpler, faster, and cheaper by at least two orders of magnitude than tail-end-of-the-monolithic-era legacy systems.
