Funnel-Based Predictive Modeling for Forecasting Student Admissions in Higher Education - Lumbanraja, Obaja Marum (Fakultas Ilmu Komputer, Universitas Klabat, 2025)
Forecasting student admissions remains a challenge due to fluctuating online engagement and complex administrative processes. Existing predictive models rarely integrate website behavioral data with institutional admission funnels, resulting in lower accuracy. This study bridges that gap by combining web analytics from Google Analytics 4 (GA4) with administrative enrollment funnel data from the admission of new students (Penerimaan Mahasiswa Baru/PMB) system to develop a unified predictive framework. The approach strengthens forecasting by aligning digital behavior with verified enrollment milestones. A quantitative explanatory design was employed, applying Pearson correlation to identify linear relationships and Seasonal ARIMA (SARIMA) to model cyclical admission trends. The dataset includes GA4 metrics sessions, engagement rate, bounce rate, and events per session and PMB funnel stages from account creation to confirmed enrollment. Results reveal strong correlations (r > 0.9, p < 0.001) between digital engagement and mid-funnel conversions, while SARIMA achieved its highest accuracy for early-stage predictions (MAPE ≈ 19%). Forecasts for final outcomes were less accurate, reflecting administrative variability. These findings confirm that web engagement metrics are reliable leading indicators of student interest and mid-stage commitment. This research establishes a reproducible pipeline unifying web analytics (GA4) with institutional funnel data (PMB), providing empirical evidence that digital engagement is a reliable leading indicator of early and mid-stage commitment, thereby forming a novel and adaptable foundation for data-driven enrollment planning.