A Monte Carlo Simulation Framework for University Enrollment Strategy Under Marketing Uncertainty

Universities operate in increasingly uncertain financial and enrollment environments, yet evidence-based recruitment investment planning remains difficult because campaign-level data are often proprietary or unavailable. This study develops a decision-analytic framework for university enrollment strategy under uncertainty, integrating Monte Carlo simulation, econometric analysis, nonlinear optimization, and policy stress testing. The Enrollment Strategy Simulation (ESS) framework evaluates how alternative recruitment budget allocation ratios affect financial performance—including return on investment (ROI), net present value (NPV), enrollment yield, and downside risk—across a four-year discounted tuition revenue horizon. Using 10,000 replications per scenario, the analysis compares 5%, 10%, and 15% allocation ratios under stochastic variation in cost per lead (CPL), conversion rate (CR), and institutional costs. Expected ROI rises from −0.476 at 5% to 0.325 at 15%, with mean NPV turning positive at the 15% threshold. Regression results confirm that conversion rate is strongly positively and cost per lead strongly negatively associated with ROI. A risk-adjusted optimization procedure identifies approximately 19.3% as the optimal mean-variance allocation. Policy stress tests show that advertising cost inflation produces the largest deterioration in expected outcomes. The ESS framework provides a transparent and reproducible decision-support tool for enrollment investment planning when empirical institutional data are unavailable. All simulations were implemented in R, with replication code archived via GitHub and Zenodo. Keywords: Monte Carlo simulation, enrollment strategy, decision analytics, higher education finance, university budgeting, risk-adjusted optimization