- Log In/Sign Up
- P-ISSN1738-3110
- E-ISSN2093-7717
- SCOPUS, ESCI
ISSN : 1738-3110
Target Market Determination for Information Distribution and Student Recruitment Using an Extended RFM Model with Spatial Analysis
BAHARIN, Safiza Suhana Kamal
KASMIN, Fauziah
Abstract
Purpose: This research proposes a new modified Recency-Frequency-Monetary (RFM) model by extending the model with spatial analysis for supporting decision-makers in discovering the promotional target market. Research design, data and methodology: This quantitative research utilizes data-mining techniques and the RFM model to cluster a university's provider schools. The RFM model was modified by adapting its variables to the university's marketing context and adding a district's potential (D) variable based on heatmap analysis using Geographic Information System (GIS) and K-means clustering. The K-prototype algorithm and the Elbow method were applied to find provider school clusters using the proposed RFM-D model. After profiling the clusters, the target segment was assigned. The model was validated using empirical data from an Indonesian university, and its performance was compared to the Customer Lifetime Value (CLV)-based RFM utilizing accuracy, precision, recall, and F1-score metrics. Results: This research identified five clusters. The target segment was chosen from the highest-value and high-value clusters that comprised 17.80% of provider schools but can contribute 75.77% of students. Conclusions: The proposed model recommended more targeted schools in higher-potential districts and predicted the target segment with 0.99 accuracies, outperforming the CLV-based model. The empirical findings help university management determine the promotion location and allocate resources for promotional information distribution and student recruitment.
- keywords
- Information Distribution, RFM, Spatial Analysis, Student Recruitment, Target Market
Reference
Abbasimehr, H., & Shabani, M. (2019). A new methodology for customer behavior analysis using time series clustering: A case study on a bank’s customers. Kybernetes, 50(2), 221–242. doi: 10.1108/K-09-2018-0506
Babaiyan, V., & Sarfarazi, S. A. (2019). Analyzing customers of South Khorasan telecommunication company with expansion of RFM to LRFM model. Journal of AI and Data Mining, 7(2), 331–340. doi:.org/10.22044/JADM.2018.6035.1715
Beheshtian-Ardakani, A., Fathian, M., & Gholamian, M. (2018). A novel model for product bundling and direct marketing in e-commerce based on market segmentation. Decision Science Letters, 7(1), 39–54. doi: org/10.5267/j.dsl.2017.4.005
Bunnak, P., Thammaboosadee, S., & Kiattisin, S. (2015). Applying data mining techniques and extended RFM model in customer loyalty measurement. Journal of Advances in Information Technology, 6(4), 238–242. doi: 10.12720/jait.6.4.238-242
Christy, A. J., Umamakeswari, A., Priyatharsini, L., & Neyaa, A. (2021). RFM ranking – an effective approach to customer segmentation. Journal of King Saud University - Computer and Information Sciences, 33(10), 1251-1257. doi: 10.1016/j.jksuci.2018.09.004
Davari, M., Noursalehi, P., & Keramati, A. (2019). Data mining approach to professional education market segmentation: a case study. Journal of Marketing for Higher Education, 29(1), 45-66. doi: 10.1080/08841241.2018.1545724
Dolnicar, S., Grün, B., & Leisch, F. (2018). Market segmentation analysis: understanding it, doing it, and making it useful. Singapore: Springer Nature Singapore Pte Ltd. doi: 10.1007/978-981-10-8818-6_1
Firdaus, U., & Utama, D. N. (2021). Development of bank’s customer segmentation model based on RFM+B approach. ICIC Express Letters, Part B: Applications, 12(1), 17–26. doi: 10.24507/icicelb.12.01.17
Hemsley-brown, J. (2017). Higher Education Market Segmentation. In Jung Cheol, Shin, Pedro, Teixeira, (Eds) Encyclopaedia of International Higher Education Systems and Institutions. Dordrecht: Springer Netherlands. doi: 10.1007/978-94-017-9553-1
Hidayat, S., Rismayati, R., Tajuddin, M., & Merawati, N. L. P. (2020). Segmentation of university customers loyalty based on RFM analysis using Fuzzy C-means clustering. Jurnal Teknologi Dan Sistem Komputer, 8(2), 133–139. doi: 10.14710/jtsiskom.8.2.2020.133-139
Hosseini, Z. Z., & Mohammadzadeh, M. (2016). Knowledge discovery from patients’ behavior via clustering-classification algorithms based on weighted eRFM and CLV model: An empirical study in public health care services. Iranian Journal of Pharmaceutical Research, 15(1), 355–367.
Huang, Z. (1998). Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values. Data Mining and Knowledge Discovery, 2(3), 283–304.
Hwang, S., & Lee, Y. (2021). Identifying customer priority for new products in target marketing: using RFM model and TextRank. Innovative Marketing, 17(2), 125-136. doi: 10.21511/im.17(2).2021.12
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. doi: g/10.20532/cit.2018.1003863
Kit, T. C., Firdaus, N., & Azmi, M. (2021). Customer profiling for Malaysia online retail industry using K-Means clustering and RM model. International Journal of Advanced Computer Science and Applications, 12(1), 106–113. doi: 10.14569/IJACSA.2021.0120114
Lawson, R. G., & Jurs, P. C. (1990). New index for clustering tendency and its application to chemical problems. Journal of Chemical Information and Computer Sciences, 30(1), 36–41. doi: 10.1021/ci00065a010
Libório, M. P., Bernardes, P., Ekel, P. I., Ramalho, F. D., & Santos, A. C. G. dos. (2020). Geomarketing and the locational problem question in the marketing studies. Brazilian Journal of Marketing, 19(2), 448–469. doi: 10.5585/remark.v19i2.17777
Moghaddam, S. Q., Abdolvand, N., & Harandi, S. R. (2017). A RFMV model and customer segmentation based on variety of products. Journal of Information Systems & Telecommunication, 5(3), 155–161.
Peker, S., Kocyigit, A., & Eren, P. E. (2017). LRFMP model for customer segmentation in the grocery retail industry: a case study. Marketing Intelligence and Planning, 35(4), 544-559. doi: 10.1108/MIP-11-2016-0210
Rahadian, Y. R., & Syairudin, B. (2020). Segmentation analysis of students in X course with RFM model and clustering. Jurnal Sosial Humaniora (JSH), special ed, 59–79. doi: 10.12962/j24433527.v0i1.6776
Rezaeinia, S. M., & Rahmani, R. (2016). Recommender system based on customer segmentation (RSCS). Kybernetes, 45(6), 946-961. doi: 10.1108/K-07-2014-0130
Roshan, H., & Afsharinezhad, M. (2017). The new approach in market segmentation by using RFM model. Journal of Applied Research on Industrial Engineering, 4(4), 259–267. doi: 10.22105/jarie.2017.91297.1011
Simkin, L., & Dibb, S. (1998). Prioritising target markets. Marketing Intelligence & Planning, 16(7), 407–417.
Sukoroto, Haryono, S., & Kharisma, B. (2020). Target market selection using MCDM Approach: A study of rolling stock manufacturer. Journal of Distribution Science, 18(7), 63–72. doi: 10.15722/jds.18.7.202007.63
Sulastri, S., Usman, L., & Syafitri, U. D. (2021). K-prototypes algorithm for clustering schools based on the student admission data in IPB University. Indonesian Journal of Statistics and Its Applications, 5(2), 228–242. doi: 10.29244/ijsa.v5i2p228-242
Tapp, A., Hicks, K., & Stone, M. (2004). Direct and database marketing and customer relationship management in recruiting students for higher education. International Journal of Nonprofit and Voluntary Sector Marketing, 9(4), 335–345. doi: 10.1002/nvsm.258
Wei, J., Lin, S., & Wu, H. (2010). A review of the application of RFM model. African Journal of Business Management, 4(19), 4199–4206.
Wu, J., Shi, L., Lin, W., Tsai, S., Li, Y., Yang, L., & Xu, G. (2020). An empirical study on customer segmentation by purchase behaviors using a RFM model and K-means algorithm. Mathematical Problems in Engineering, 2020, 1-7. doi: 10.1155/2020/8884227
- Downloaded
- Viewed
- 0KCI Citations
- 0WOS Citations