User Persona Clustering to Enhance Digital Marketing Strategy in Bakrie University
DOI:
https://doi.org/10.54099/aijb.v4i2.1235Keywords:
marketing, digital marketing, promotion, K-Means, ClusteringAbstract
This research describes the Clustering of User Personas on the Digital Marketing Strategy of Bakrie University. In carrying out the testing of this algorithm, the data used is sample data from Bakrie University students. In this application, the application of clustering using the K-means algorithm is used. In the results of this study, Bakrie University can do effective digital marketing using social media Instagram.The Industrial Revolution 4.0 has transformed technology and education, pushing institutions to adopt innovative marketing strategies. This study focuses on Bakrie University's digital marketing efforts using the K-Means clustering method to analyze student personas. Data from 100 students revealed three distinct clusters based on demographics, social media usage, and preferences. Cluster 1 (younger group) favored Instagram and YouTube, Cluster 2 relied heavily on Instagram, and Cluster 3 (older group) used a mix of Instagram, YouTube, and Facebook. On average, users across all clusters spent 4-8 hours daily on social media, emphasizing its significance in marketing. The study concludes that Instagram is the most effective platform for engaging all clusters, with YouTube serving as a supplementary tool. These insights demonstrate how clustering analysis can help educational institutions develop targeted, data-driven strategies to strengthen their brand, attract students, and enhance their competitiveness in the digital era.
References
Chaffey, Dave and Fiona Ellis-Chadwick. (2016). Digital Marketing: Strategy, Implementation and Practice. Sixth Edition. United Kingdom. Pearson
Coviello, N., Miley, R., Marcolin, B. 2001. Understanding IT-enabled Interactivity In Contemporary Marketing. Journal of interactive Marketing, (Vol.15 No.4, pp. 18-33).
Di Tella, R., & MacCulloch, R. (2006). Some Uses of Happiness Data in Economics. Journal of Economic Perspectives, 20(1), 25–46. https://doi.org/10.1257/089533006776526111
Hair, J., Black, W., Babin, B., & Anderson, R. (2018). Multivariate data analysis (8th ed.). Pearson Prentice Hall.
Han, J., Kamber, M., Pei, J. 2012. Data Mining Concepts and Techniques.Waltham: Elsevier
Hung, C.M., Wu, J., Chang, J.H. & Yang, D.L., 2005. An Efficient k-Means Clustering Algorithm Using Simple Partitioning. JOURNAL OF INFORMATION SCIENCE AND ENGINEERING, XXI (1), pp.1157-77.
Iskamto, D. (2023). Data Science: Trends and Its Role in Various Fields. Adpebi International Journal of Multidisciplinary Sciences, 2(2), Article 2. https://doi.org/10.54099/aijms.v2i2.606
Iskamto, D., & Juariyah, L. (2023). Blockchain Technology Challenge in the Future: : Data Security and Efficiency. International Journal of Law Policy and Governance, 2(2), Article 2. https://doi.org/10.54099/ijlpg.v2i2.708
Madhulatha, T.S., 2012. An Overview On Clustering Methods. IOSR Journal of Engineering, II (4), pp.719-25.
Ong, J.O. “Implementasi Algoritma K-means clustering untuk menentukan strategi marketing president university,” J. Ilm. Tek. Ind., vol. vol.12, no, no. juni, pp. 10–20, 2013.
Sadewo, M.G., Windarto, A. P., dan Andani, S.R. “Pemanfaatan Algoritma Clustering Dalam Mengelompokkan Jumlah Desa / Kelurahan Yang Memiliki Sarana Kesehatan,” vol. I, pp. 124–131, 2017.
Saranya & Punithavalli, 2011. An Efficient Centroid Selection Algorithm for K-means Clustering. International Journal of Management, IT and Engineering, I (3), pp.130-40.
Suyanto, 2017, Data Mining Untuk Klasifikasi dan Klasterisasi Data. Bandung: Informatika.
Vulandari, R. T. 2017. Data Mining Teori dan Aplikasi Rapidminer. Yogyakarta: Gava Media
Wakhidah, N. (2010). Clustering Menggunakan K-Means Algorithm. Jurnal Transformatika, 8(1), 33. https://doi.org/10.26623/transformatika.v8i1.45.
Widodo. 2004. Psikologi Belajar. Jakarta: Rineka Cipta.
Windarto, A. P. “Implementation of Data Mining on Rice Imports by Major Country of Origin Using Algorithm Using K-Means Clustering Method,” Int. J. Artif. Intell. Res., vol. 1, no. 2, pp. 26–33, 2017.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Widya Handayani, Jaka Reynaldi, Metha Erzha Chairani, Jerry Heikal
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
