Funnel-Based Predictive Modeling for Forecasting Student Admissions in Higher Education

Obaja Marum Lumbanraja

doi:10.31154/cogito.v11i2.1002.337-348

Authors

Obaja Marum Lumbanraja Universitas Advent Indonesia

DOI:

https://doi.org/10.31154/cogito.v11i2.1002.337-348

Keywords:

Predictive Modeling, Funnel Analytics, Student Admissions, SARIMA, Higher Education

Abstract

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.

References

Vaishnavi Punde, Shekhar Pawar, “Admission Prediction Using Time Series Analysis,” International Journal of Innovative Science and Research Technology, 2024. doi: 10.38124/ijisrt/ijisrt24apr2377.

Yu (April) Chen, Ran Li, Linda Serra Hagedorn, “Undergraduate International Student Enrollment Forecasting Model: An Application of Time Series Analysis,” Journal of International Students, 2019. doi: 10.32674/JIS.V9I1.266.

Na’im Akbar, “Modelling and forecasting the number of students enrolled in the College of Administrative Sciences at Kuwait University using Multiplicative SARIMA Model,” Al-Mağallah al-ʿilmiyyaẗ li Qitāʿ Kulliyyāẗ al-tiǧāraẗ bi Ǧāmiʿaẗ al-Azhar, 2023. doi: 10.21608/jsfc.2023.367828.

Fachri Ayudi Fitrony, Laksmita Dewi Supraba, Tessa Rantung, I Made Artha Agastya, Kusrini Kusrini, “Analysis to Predict the Number of New Students At UNU Pasuruan using Arima Method,” Jurnal Sistem Informasi dan Komputer, 2025. doi: 10.32736/sisfokom.v14i1.2251.

Olayinka Abiola-Adams, Bisayo Oluwatosin Otokiti, Florence Ifeanyichukwu Olinmah, Dennis Edache Abutu, Isaac Okoli, Cyril Imohiosen, “Building Performance Forecasting Models for University Enrollment Using Historical and Transfer Data Analytics,” Journal of Frontiers in Multidisciplinary Research, 2021. doi: 10.54660/.jfmr.2021.2.1.162-168.

Francisco de Borja Valdés Bertrand, “A machine learning modeling prediction of enrollment among admitted college applicants at University of Santo Tomas,” Nucleation and Atmospheric Aerosols, 2022. doi: 10.1063/5.0100174.

Jamal Hussain, David Rosangliana, Vanlalruata, “Student Gross Enrolment Ratio Forecasting: A Comparative Study Using Statistical Method and Machine Learning,” International Journal of Information and Education Technology, 2023. doi: 10.18178/ijiet.2023.13.3.1824.

Treena Basu, Ron Buckmire, Osei K. Tweneboah, “An Application of Machine Learning to College Admissions: The Summer Melt Problem,” Journal of Machine Learning for Modeling and Computing, 2022. doi: 10.1615/jmachlearnmodelcomput.2022046289.

Julius Cesar Mamaril, Melvin A. Ballera, “Multiple educational data mining approaches to discover patterns in university admissions for program prediction,” International Journal of Informatics and Communication Technology, 2022. doi: 10.11591/ijict.v11i1.pp45-56.

Jung-Pin Wu, Ming-Shr Lin, Chi-Lun Tsai, “A predictive model that aligns admission offers with student enrollment probability,” Education Sciences, 2023. doi: 10.3390/educsci13050440.

Eric Huguenin, “Google Analytics 4 Properties: A Complete Guide,” MeasureSchool, accessed Oct. 2025. [Online]. Available: https://measureschool.com/google-analytics-4-properties/

Google, “Visualize Google Analytics data in Looker Studio,” Looker Studio Help, accessed Oct. 2025. [Online]. Available: https://support.google.com/looker-studio

R. J. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice, 3rd ed. Melbourne, Australia: OTexts, 2021. [Online]. Available: https://otexts.com/fpp3/seasonal-arima.html

R. J. Hyndman and A. B. Koehler, “Another look at measures of forecast accuracy,” International Journal of Forecasting, vol. 22, no. 4, pp. 679–688, 2006. doi: 10.1016/j.ijforecast.2006.03.001.

D. C. Montgomery and G. C. Runger, Applied Statistics and Probability for Engineers, 7th ed. Hoboken, NJ, USA: Wiley, 2018.

H. A. Mengash, “Using data mining techniques to predict student performance to support decision making in university admission systems,” IEEE Access, 2020. doi: 10.1109/ACCESS.2020.2981905.

K. C. Li, B. Wong, and H. T. Chan, “Predictive analytics for university student admission: a literature review,” in Lecture Notes in Computer Science, 2023. doi: 10.1007/978-3-031-35731-2_22.

S. Shilbayeh and A. A. Abonamah, “Predicting student enrollments and attrition patterns in higher educational institutions using machine learning,” The International Arab Journal of Information Technology, 2021. doi: 10.34028/18/4/8.

K. Li, B. T. Wong, and M. Liu, “Development of a multi-model analytics system to enhance decision-making in student admission,” Interactive Technology and Smart Education, 2025. doi: 10.1108/itse-12-2024-0328.

V. Punde and S. Pawar, “Admission Prediction Using Time Series Analysis,” International Journal of Innovative Science and Research Technology, 2024. doi: 10.38124/ijisrt/ijisrt24apr2377.

F. Mohamed and B. Bukhatwa, “Predicting Student Enrollment at the Education College of Benghazi via Mathematical Modeling Approaches,” Journal of Qadisiyah Computer Science and Mathematics, 2024. doi: 10.29304/jqcsm.2024.16.41803.

Y. Zhao and A. Otteson, “A Practice in Enrollment Prediction with Markov Chain Models,” 2024. doi: 10.48550/arXiv.2405.14007.

G. A. N. Pongdatu and Y. H. Putra, “Seasonal Time Series Forecasting Using SARIMA and Holt–Winter’s Exponential Smoothing,” Microelectronics Systems Education, 2018. doi: 10.1088/1757-899X/407/1/012153.

A. Vieira, I. Pereira de Sousa, and S. Dória-Nóbrega, “Forecasting daily admissions to an emergency department considering single and multiple seasonal patterns,” Healthcare Analytics, 2023. doi: 10.1016/j.health.2023.100146.

M. Bose and K. Mali, “Handling Seasonal Pattern and Prediction Using Fuzzy Time Series Model,” 2020. doi: 10.1007/978-981-15-1041-0_4.

A. A. Rizal, “Comparative Evaluation of Forecasting Methods for Tourist Arrival Prediction: A Sliding Window-Based Analysis,” CogITo Smart Journal, vol. 11, no. 1, pp. 152–166, 2025. doi: 10.31154/cogito.v11i1.947.152-166.