As institutions confront increasingly complex enrollment and financial environments, a deeper, more precise understanding of student enrollment and how these patterns align with institutional resources is essential. AACRAO’s Annual Meeting sessions often help catalyze that shift, and a recent forensic enrollment management (FEM) presentation by Steve McKellips of the University of Akron serves as an important example. The session articulated concepts that many practitioners encounter daily but have not always been framed explicitly. Rather than critiquing traditional SEM approaches, it prompted a reexamination of their underlying assumptions.
McKellips began by challenging one of the most durable approaches to SEM: that increased enrollment reliably produces increased revenue. For decades, this notion has guided decision-making because the environment allowed it to work. Today’s landscape, marked by shifting demographics, aggressive discounting, rising aid expectations, and more price-sensitive students, has changed the relationship between headcount and financial stability. Institutions often continue pursuing growth targets even when the marginal revenue from additional students is flat or negative. This is a classic problem of diminishing returns: additional inputs — more students, more aid, more recruiting — no longer yield proportionate outputs, yet the system continues to operate as though they do.
FEM encourages institutions to revisit these assumptions not as failures of judgment, but as mismatches between inherited frameworks and current conditions. FEM gives us a way to see the deeper architecture of enrollment behavior. Under-enrollment or revenue shortfalls are the visible events; discounting practices, aid policies, and governance structures represent the submerged layers. These underlying structures shape outcomes long before they appear in headcount or revenue reports.
For me, the power of FEM lies in its alignment with this system’s perspective. It allows institutions to shift from viewing enrollment as a static target (how many students we enroll) to a dynamic system of stocks and flows: how students move in, through, and out of the institution, and how that movement maps to revenue. Traditional SEM already deals with flow, but FEM adds financial clarity to each part of the system. When viewed together, they provide a clearer picture of how institutional decisions shape both student experience and financial sustainability.
Understanding NetFTE, Cost of Delivery, and Revenue Clarity
Two core components of forensic enrollment management, NetFTE and cost of delivery (COD), help institutions see their financial foundations more accurately. As we know, traditional FTE counts generally treat all credits as equal. But institutions know that many credits, such as those from dual enrollment and consortium agreements, yield little or no revenue. Yet these credits consume instructional and operational resources. NetFTE corrects this by counting only revenue-producing credits. It is a small definitional shift with large implications. If the stock of FTE appears stable but the stock of revenue-generating FTE is declining, then the flow of resources in the system is misaligned with the workload it sustains.
COD, the revenue required per NetFTE simply to break even, adds even another layer of precision. COD is rarely expressed openly because it is complex, dynamic, and easy to misinterpret. Yet it reveals a central truth: institutions often operate with a breakeven point far higher than they assume. When tuition discounting increases or revenue-generating credits decline, COD rises. If institutions continue making decisions based on outdated COD assumptions, they inadvertently widen financial gaps. This is where opportunity cost becomes visible. Every dollar spent on discounting is a dollar that cannot be deployed elsewhere, whether that be raising yield where elasticity is high, stabilizing enrollment in programs with strong retention potential, or increasing student support that improves achievement.
Together, NetFTE and COD sharpen what institutions often sense but cannot quantify. They reveal that enrollment is not just a volume problem but a portfolio problem. The question is not simply, “How many students do we enroll?” but “What is the composition of the portfolio, and how does each segment contribute to institutional stability?” This aligns naturally with portfolio optimization: balancing high-need students, low-need students, high-retention groups, and high-elasticity segments to reduce volatility and improve year-over-year predictability. To this end, however, FEM does not ask institutions to value students differently. It asks institutions to understand the financial environment needed to serve all students well.
The Behavioral Economics of Aid and Institutional Assumptions
One of the deepest insights McKellips offered was his explanation of elasticity: how different students respond differently to price and aid. Institutions often assume that a certain level of discounting will move all students in similar ways. But elasticity is not uniform: some students require significant financial support to enroll; others enroll with little or no aid; still others may value program reputation, modality, or proximity more than price.
Elasticity reframes aid strategy as an exercise in behavioral economics rather than intuition. Instead of assuming what students value, institutions should measure actual behavior. Elasticity modeling identifies which students yield with minimal aid, which require targeted investment, and which are unlikely to enroll regardless of the discount level. When institutions apply a uniform approach to a non-uniform population, they inadvertently create inefficiency: over-awarding low-elasticity students (where marginal gains are minimal) and under-awarding high-elasticity students (where marginal gains could be substantial).
This is another application of marginal gains and diminishing returns. Adding more aid does not necessarily guarantee a proportional increase in yield. Conversely, small, well-placed awards can meaningfully shift the behavior of high-elasticity students. By modeling these dynamics, FEM enables institutions to use aid for maximum impact rather than relying on broad discounting strategies that may have worked in earlier eras but no longer align with student behavior.
Community colleges experience elasticity differently. Even small financial barriers, such as $150 for textbooks or transportation or lab fees, can meaningfully affect enrollment or persistence. Here, elasticity may be less about tuition and more about the total cost of attendance relative to economic fragility. By treating these patterns as measurable rather than anecdotal, community colleges can deploy scarce resources with greater precision.
FEM also highlights the difference between list price and net revenue through McKellips’ house-sale analogy. The list price of a house matters far less than the cash received at closing. Similarly, tuition only matters to the extent that it reflects actual dollars collected. When institutions rely on list price or average discount rates to understand their financial position, they confuse the map for the territory. Net revenue, not list price or even discount rate, is the real signal.
What Cohorts Reveal About Long-Term Financial Health
SEM traditionally treats retention as an unqualified good. And academically, retaining students remains a central institutional responsibility. But FEM reveals a more nuanced picture of how cohorts contribute financially over time. The immediate effect of retaining a student is keeping them in the institution. The second-order effect is understanding how that student’s net tuition revenue changes progress over time.
An application of FEM would show that cohort-level net tuition revenue often declines or decays over the first several years. High-contributing students may graduate early. Students may take lighter loads as they advance. Aid may increase as need fluctuates. Some students may persist at lower credit levels for extended periods. A retention strategy that looks strong academically may not stabilize revenue if the cohort’s financial profile shifts dramatically. In this context, FEM encourages institutions to consider not just whether students persist, but how cohort composition shapes long-term financial health. A class with strong retention but a steep downward revenue slope may require different financial or academic interventions than a class with more balanced contribution patterns.
Portfolio theory is again useful here. A sustainable enrollment portfolio balances students across multiple dimensions: need, likelihood of persistence, progression velocity, and revenue-contribution patterns. Institutions often assume high-achieving students yield the strongest financial outcomes. FEM reveals that it is often mid-range achievers — students with strong but not elite academic profiles — who may generate more stable net revenue because they persist longer with less aid escalation.
For community colleges, cohort behavior must also account for enrollment intensity. Students swirl, drop out, re-enter, switch programs, and take variable credit loads. Cohort analysis in this context is even more valuable because it provides insights into patterns that would otherwise be obscured by headcount reporting.
Institutional Blind Spots and the Work Ahead
The final contribution of McKellips’ model lies in its ability to expose institutional blind spots with kindness rather than critique. Institutions do not over-award aid because they are careless. They do so because they hope to increase yield, remain competitive, and serve students well. Hope is not a flaw. But without clear financial insight, hope can unintentionally lead institutions into patterns that are difficult to sustain. The work ahead is constructive. FEM and SEM together offer a fuller picture of institutional health. SEM clarifies student movement, process design, and operational cohesion. FEM clarifies revenue contribution, cohort evolution, and the financial implications of aid. Together, they allow institutions to make decisions with clearer alignment between mission, student success, and sustainability.
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