Segmentation, targeting and positioning analysis using k-means clustering model: A case study of the laptop market in Indonesia
DOI:
https://doi.org/10.35335/ijafibs.v12i2.313Keywords:
Clustering, K-Means Algorithm, Laptop, Marketing, SegmentationAbstract
In Indonesia's rapidly evolving laptop market, understanding consumer preferences is crucial for maintaining competitiveness. This study employs the K-Means Clustering algorithm to segment the laptop market based on variables such as age, income, expenditure, laptop price, main usage, and selection criteria. Data were collected from 271 respondents in the Jabodetabek area through an online survey. The analysis identified six distinct customer clusters: Edu-Tech Enthusiasts, Executive Civil Servants, Gov-Corp Society, Steady State Officials, Corporate Climbers, and Emerging Entrepreneurs. Each cluster exhibits unique characteristics and preferences, including preferred brands and price ranges. The findings emphasize the importance of targeted marketing strategies tailored to the specific needs of each segment. By leveraging these insights, laptop producers can optimize product offerings, pricing strategies, and promotional campaigns to enhance market share, customer loyalty, and profitability in Indonesia's competitive laptop industry.
References
Aghdaie, M. H. (2014). Using Two-Stage Clustering and Conjoint Analysis for Benefit Segmentation of Iranian Laptop Buyers Customer-oriented benefit segmentation : an integrated approach Mohammad Hasan Aghdaie *, Parham Fami Tafreshi and Majid Behzadian. February 2012.
Apriani, A., & Heikal, J. (2024). Segmentation Analysis Using K-Means Clustering Model with SPSS Case Study of Backpacker Jakarta Community Members. Jurnal Indonesia Sosial Sains, 5(03), 441–460. https://doi.org/10.59141/jiss.v5i03.1031
Ardiansyah, G. T., Santosa, S., Hasibuan, M. S., & Heikal, J. (2024). Mapping the Wuling vehicle market with K-Means Clustering : An effective digital marketing strategy. 14(2), 136–150.
Ariati, I., Norsa, R. N., Akhsan, L., & Heikal, J. (2023). Segmentasi Pelanggan Menggunakan K-Means Clustering Studi Kasus Pelanggan Uht Milk Greenfield. Cerdika: Jurnal Ilmiah Indonesia, 3(7), 729–743.
Armstrong, G., Adam, S., Denize, S., & Kotler, P. (2014). Principles of marketing. Pearson Australia.
Bi, Q. (2019). Cultivating loyal customers through online customer communities: A psychological contract perspective. Journal of Business Research, 103, 34–44.
Dey, D. (2024). ML | Algoritma pengelompokan K-means Mini Batch. https://www.geeksforgeeks.org/ml-mini-batch-k-means-clustering-algorithm/
Farhan, M., & Heikal, J. (2024). Used Car Customer Segmentation Using K-Means Clustering Model With SPSS Program: Case Study Caroline.Id. Jurnal Indonesia Sosial Sains, 5(03), 543–559. https://doi.org/10.59141/jiss.v5i03.1042
Harahap, M., Lubis, Y., & Situmorang, Z. (2022). Analisis Pemasaran Bisnis dengan Data Science : Segmentasi Kepribadian Pelanggan berdasarkan Algoritma K-Means Clustering. Data Sciences Indonesia (DSI), 1(2), 76–88. https://doi.org/10.47709/dsi.v1i2.1348
Huang, Y., Zhang, M., & He, Y. (2020). Research on improved RFM customer segmentation model based on K-Means algorithm. 2020 5th International Conference on Computational Intelligence and Applications (ICCIA), 24–27.
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
Kotler, P., & Keller, K. L. (2016). Manajemen Pemasaran, Jilid Kedua. Erlangga.
Miraftabzadeh, S. M., Colombo, C. G., Longo, M., & Foiadelli, F. (2023). K-Means and Alternative Clustering Methods in Modern Power Systems. IEEE Access, 11(September), 119596–119633. https://doi.org/10.1109/ACCESS.2023.3327640
Mukka, H., & Dutt, I. (2024). Global PC market returns to growth in Q4 2023. https://www.canalys.com/newsroom/global-pc-market-Q4-2023
Mulyo, I. A., & Heikal, J. (2022). Customer Clustering Using The K-Means Clustering Algorithm in Shopping Mall in Indonesia. Management Analysis Journal, 11(4), 365–371.
Nica, E., Gajanova, L., & Kicova, E. (2019). Customer segmentation based on psychographic and demographic aspects as a determinant of customer targeting in the online environment. Littera Scripta, 12(2), 108–126.
Perdhana, R. B., & Heikal, J. (2024). Enhancing Customer Segmentation in Online Transportation Services: A Comprehensive Approach Using K-Means Clustering and RFM Model. Indonesian Interdisciplinary Journal of Sharia Economics (IIJSE), 7(2), 2849–2865.
Resti, A. Della, & Heikal, J. (2023). … on Top 3 Online Food Ordering Applications To Members of Roller Skates Community in Jakarta Using K-Means Clustering Method. Jurnal Scientia, 12(03), 4178–4183. http://infor.seaninstitute.org/index.php/pendidikan/article/view/1880%0Ahttp://infor.seaninstitute.org/index.php/pendidikan/article/download/1880/1580
Rose, I., & Mawarni, P. D. (2023). ANALISIS SEGMENTASI PASAR LAPTOP DENGAN METODE CLUSTERING DAN ALGORITMA K-MEANS ANALISIS SEGMENTASI PASAR LAPTOP DENGAN METODE CLUSTERING DAN ALGORITMA K-MEANS. June. https://doi.org/10.13140/RG.2.2.10545.76648
Sarkar, M., Puja, A. R., & Chowdhury, F. R. (2024). Optimizing Marketing Strategies with RFM Method and K-Means Clustering-Based AI Customer Segmentation Analysis. Journal of Business and Management Studies, 6(2), 54–60. https://doi.org/10.32996/jbms.2024.6.2.5
Shirole, R., Salokhe, L., & Jadhav, S. (2021). Customer Segmentation using RFM Model and K-Means Clustering. International Journal of Scientific Research in Science and Technology, 591–597. https://doi.org/10.32628/ijsrst2183118
Sinaga, K. P., & Yang, M. S. (2020). Unsupervised K-means clustering algorithm. IEEE Access, 8, 80716–80727. https://doi.org/10.1109/ACCESS.2020.2988796
Statistik, B. P. (2023). Persentase Penduduk yang Menggunakan Komputer menurut Provinsi dan Klasifikasi Daerah (Persen), 2021-2022. https://www.bps.go.id/id/statistics-table/2/MTY3MSMy/persentase-penduduk-yang-menggunakan-komputer-menurut-provinsi-dan-klasifikasi-daerah.html
Sugiyono. (2016). Metode Penelitian Kunatitatif Kualitatif dan R&D. Alfabeta, Bandung.
Yusnidar, Y., Dudi Yudhakusuma, & Sari, F. (2023). Personalized Marketing Strategy in Digital Business Using Data Mining Approach. International Journal Software Engineering and Computer Science (IJSECS), 3(2), 137–143. https://doi.org/10.35870/ijsecs.v3i2.1515
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