Identifikasi Segmen Pasar Mahasiswa Perguruan Tinggi Menggunakan Analisis Klaster Berdasarkan Variabel Psikografis

Authors

  • Hardika Khusnuliawati Universitas Sahid Surakarta
  • Dhian Riskiana Putri Universitas Sahid Surakarta

DOI:

https://doi.org/10.47028/j.risenologi.2021.61b.243

Keywords:

segmentasi pasar, analisis kluster, variabel psikografis

Abstract

The importance of the presence of higher education enables the private sector to participate in organizing academic activities in the form of higher education institutions. This causes the private higher education market to become more competitive, which implies a low number of students. Therefore, market segmentation needs to be applied to college students so that they can help to determine the model of marketing and promotional activities. The stages carried out in this study consisted of data collection, data exploration, and extracting segment. Cluster analysis was applied as a method for extracting segments of students with psychographics variables as partitioning factors. The K-Means algorithm was chosen as the method applied for cluster analysis because it produces better performance when compared to the use of K-Modes. Cluster analysis based on psychographics variables applied to this case succeeded in extracting the segment of the university students into 6 segments.

References

Abadi, S., Mat The, K. S., Nasir, B. M., Huda, M., Ivanova, N. L., Sari, T. I., Maseleno, A., Satria, F., & Muslihudin, M. (2018). Application model of k-means clustering: Insights into promotion strategy of vocational high school. International Journal of Engineering and Technology(UAE), 7(2.27 Special Issue 27), 182–187. https://doi.org/10.14419/ijet.v7i2.11491

Angulo, F., Pergelova, A., & Rialp, J. (2010). A market segmentation approach for higher education based on rational and emotional factors. Journal of Marketing for Higher Education, 20(1), 1–17. https://doi.org/10.1080/08841241003788029

Armstrong, G., Adam, S., Denize, S., & Kotler, P. (2014). Principles of marketing. Pearson Australia.

Arsova, M., & Temjanovski, R. (2019). Strategy for market segmentation and differentiation: contemporary marketing practice. Journal of Economics, 4(1), 27–35.

Casidy, R., & Wymer, W. (2018). A taxonomy of prestige-seeking university students: strategic insights for higher education. Journal of Strategic Marketing, 26(2), 140–155. https://doi.org/10.1080/0965254X.2016.1182573

Goodrich, K., Swani, K., & Munch, J. (2020). How to connect with your best student prospects: Saying the right things, to the right students, in the right media. Journal of Marketing Communications, 26(4), 434–453. https://doi.org/10.1080/13527266.2018.1514319

Govindasamy, R. (2018). Cluster Analysis of Wine Market Segmentation – A Consumer Based Study in the Mid-Atlantic USA. Economic Affairs, 63(1), 151–157. https://doi.org/10.30954/0424-2513.2018.00150.19

Hung, C., & Tsai, C. F. (2008). Market segmentation based on hierarchical self-organizing map for markets of multimedia on demand. Expert Systems with Applications, 34(1), 780–787. https://doi.org/10.1016/j.eswa.2006.10.012

Hung, P. D., Ngoc, N. D., & Hanh, T. D. (2019). K-means Clustering Using R A Case Study of Market Segmentation. Proceedings of the 2019 5th International Conference on E-Business and Applications - ICEBA 2019, 100–104. https://doi.org/10.1145/3317614.3317626

Kamthania, D., Pahwa, A., & Madhavan, S. S. (2018). Market segmentation analysis and visualization using K-mode clustering algorithm for E-commerce business. Journal of Computing and Information Technology, 26(1), 57–68. https://doi.org/10.20532/cit.2018.1003863

Leonnard, L., Daryanto, H. K. ., Sukandar, D., & Yusuf, E. Z. (2014). The Loyalty Model of Private University Student. International Research Journal of Business Studies, 7(1), 55–68. https://doi.org/10.21632/irjbs.7.1.55-68

Lin, C. F. (2002). Segmenting customer brand preference: Demographic or psychographic. Journal of Product & Brand Management, 11(4), 249–268. https://doi.org/10.1108/10610420210435443

Liu, H., Huang, Y., Wang, Z., Liu, K., Hu, X., & Wang, W. (2019). Personality or value: A comparative study of psychographic segmentation based on an online review enhanced recommender system. Applied Sciences (Switzerland), 9(10). https://doi.org/10.3390/app9101992

Maciejewski, G., Mokrysz, S., & Wróblewski, ?. (2019). Segmentation of coffee consumers using sustainable values: Cluster analysis on the Polish coffee market. Sustainability (Switzerland), 11(3), 1–20. https://doi.org/10.3390/su11030613

Makgosa, R., Matenge, T., & Mburu, P. (2016). Hybrid Segmentation in the Financial Services Market: Targeting Saving Consumers. Family and Consumer Sciences Research Journal, 44(4), 447–468. https://doi.org/10.1111/fcsr.12168

Müller, H., & Hamm, U. (2014). Stability of market segmentation with cluster analysis - A methodological approach. Food Quality and Preference, 34, 70–78. https://doi.org/10.1016/j.foodqual.2013.12.004

Nadanyiova, M., & Gajanova, L. (2019). The impact of psychographic segmentation on marketing communication in transport company. Transport Means - Proceedings of the International Conference, 2019-Octob(April), 216–220.

Schlegelmilch, B. B. (2016). Segmenting Targeting and Positioning in Global Markets. 1985, 63–82. https://doi.org/10.1007/978-3-319-26279-6_4

Tempola, F., & Assagaf, A. F. (2018). Clustering of Potency of Shrimp In Indonesia With K-Means Algorithm And Validation of Davies-Bouldin Index. 1(Icst), 730–733. https://doi.org/10.2991/icst-18.2018.148

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Published

2021-10-25

How to Cite

Hardika Khusnuliawati, & Riskiana Putri, D. (2021). Identifikasi Segmen Pasar Mahasiswa Perguruan Tinggi Menggunakan Analisis Klaster Berdasarkan Variabel Psikografis. Risenologi, 6(1b), 44–49. https://doi.org/10.47028/j.risenologi.2021.61b.243