AI-Driven Customers Segmentation Using K-Means Clustering
DOI:
https://doi.org/10.70609/gtech.v9i1.6202Keywords:
AI-driven segmentation, Behavioural analysis, Customer segmentation, K-Means clustering, Market segmentationAbstract
Effective customer segmentation is essential for businesses to enhance marketing strategies and improve customer engagement. This study applies K-Means clustering to segment customers based on demographic and behavioral data collected through structured surveys and questionnaires. The dataset includes attributes such as age, income, purchase frequency, product preferences, and brand loyalty. Data preprocessing involved normalization, imputation, and encoding to ensure quality and suitability for clustering. The optimal number of clusters was determined using the Elbow Method and Davies-Bouldin Index, resulting in three distinct segments: High-Spending, Frequent Shoppers, Price-Sensitive, Infrequent Shoppers, and Brand-Loyal, Moderate Shoppers. These clusters provide actionable insights for businesses, enabling tailored strategies such as loyalty programs, discounts, and targeted promotions. The Davies-Bouldin Index score of 1.27 and visualization using Principal Component Analysis (PCA) validated the effectiveness and distinctiveness of the segmentation. This research highlights the potential of AI-driven methods like K-Means clustering in uncovering hidden patterns in customer behavior, offering a robust alternative to traditional segmentation approaches. Future studies could explore larger datasets, integrate additional behavioral attributes, and compare the performance of other clustering algorithms to further enhance segmentation outcomes.
References
Abdulhafedh, A. (2021). Incorporating K-means, Hierarchical Clustering and PCA in Customer Segmentation. Journal of City and Development, 3, 12–30.
Adeniran, I. A., Efunniyi, C. P., Osundare, O. S., & Abhulimen, A. O. (2024). Transforming Marketing Strategies with Data Analytics: A Study on Customer Behavior and Personalization. International Journal of Scholarly Research in Engineering and Technology, 4(1), 041–051.
Adesoga, T. O., Olaiya, O. P., Obani, O. Q., Orji, M.-C. U., Orji, C. A., & Olagunju, O. D. (2024). Leveraging AI for transformative business development: Strategies for market analysis, customer insights, and competitive intelligence. International Journal of Science and Research Archive, 12(2), 799–805. https://doi.org/10.30574/ijsra.2024.12.2.1291
Adewusi, A. O., Okoli, U. I., Adaga, E., Olorunsogo, T., Asuzu, O. F., & Daraojimba, D. O. (2024). Business Intelligence in The Era of Big Data: A Review of Analytical Tools and Competitive Advantage. Computer Science & IT Research Journal, 5(2), 415–431. https://doi.org/10.51594/csitrj.v5i2.791
Ahmed, M., Seraj, R., & Islam, S. M. S. (2020). The K-Means Algorithm: A Comprehensive Survey and Performance Evaluation. In Electronics (Switzerland) (Vol. 9, Issue 8, pp. 1–12). MDPI AG. https://doi.org/10.3390/electronics9081295
Andreswari, R., Fauzi, R., Izzati, M., Pradwiyasma Widartha, V., & Pramesti, D. (2023). Student Demography Clustering Based on The ICFL Program Using K-Means Algorithm. International Journal on Informatics Visualization. www.joiv.org/index.php/joiv
Ayodele, E., & Sodeinde, V. (2024). Customer Segmentation Using the K-Means Clustering Algorithm. Ilaro Journal of Science and Technology (IJST), 4. https://sciencetechjournal.federalpolyilaro.edu.ng
Bandyopadhyay, S., Thakur, S. S., & Mandal, J. K. (2021). Product Recommendation for E-Commerce Business by Applying Principal Component Analysis (PCA) and K-Means Clustering: Benefit for The Society. Innovations in Systems and Software Engineering, 17(1), 45–52. https://doi.org/10.1007/s11334-020-00372-5
Bharadiya, J. P. (2023). The Role of Machine Learning in Transforming Business Intelligence. International Journal of Computing and Artificial Intelligence, 4(1), 16–24. https://doi.org/10.33545/27076571.2023.v4.i1a.60
Chaudhary, P. S., Khurana, M. R., & Ayalasomayajula, M. (2024). Real-World Applications of Data Analytics, Big Data, and Machine Learning. In P. Singh, A. R. Mishra, & P. Garg (Eds.), Data Analytics and Machine Learning: Navigating the Big Data Landscape (pp. 237–263). Springer Nature Singapore. https://doi.org/10.1007/978-981-97-0448-4_12
Darmawan, C., Setiyawan, Y., Prasetyo, R. A., & Qurrota’Ayyun, S. K. (2024). Penerapan Algoritma K-means dan Metode Elbow Untuk Clustering Tingkat Pencemaran Sampah Plastik pada Kabupaten/Kota di Seluruh Indonesia. G-Tech: Jurnal Teknologi Terapan, 8(1), 349–358. https://doi.org/10.33379/gtech.v8i1.3637
Fischer, T. (2024). Driving Business Growth through AI-Driven Customer Insights: Leveraging Big Data Analytics for Competitive Advantage. Journal of Artificial Intelligence Reseach and Applications, 4.
Hafez, M. (2024). Pioneering Perspectives: Strategies and Considerations in Market Segmentation and Targeting. SSRN. https://ssrn.com/abstract=4802226
Hamka, M., & Ramdhoni, N. (2022). K-Means Cluster Optimization for Potentiality Student Grouping using Elbow Method. AIP Conference Proceedings, 2578(1), 060011. https://doi.org/10.1063/5.0108926
Hariguna, T. (2024). Customer Segmentation and Targeted Retail Pricing in Digital Advertising using Gaussian Mixture Models for Maximizing Gross Income. Journal of Digital Market and Digital Currency, 1(2), 183–203. https://doi.org/10.47738/jdmdc.v1i2.11
Huang, S., Kang, Z., Xu, Z., & Liu, Q. (2021). Robust Deep K-Means: An Effective and Simple Method for Data Clustering. Pattern Recognition, 117, 107996. https://doi.org/https://doi.org/10.1016/j.patcog.2021.107996
Kalusivalingam, K., Sharma, A., Patel, N., & Singh, V. (2020). Enhancing Customer Segmentation through AI: Leveraging K-Means Clustering and Neural Network Classifiers. Cognitive Computing Journal, 1.
Lone, H., & Warale, P. (2022). Cluster Analysis: Application of K-Means and Agglomerative Clustering for Customer Segmentation. In Journal of Positive School Psychology (Vol. 2022, Issue 5). http://journalppw.com
Mandapuram, M., Srujan Gutlapalli, S., Reddy, M., & Bodepudi, A. (2020). Application of Artificial Intelligence (AI) Technologies to Accelerate Market Segmentation. Global Disclosure of Economics and Business, 9, 141–150.
Miller, C. J., Brannon, D. C., Salas, J., & Troncoza, M. (2021). Advertising, Incentives, and The Upsell: How Advertising Differentially Moderates Customer vs Retailer-Directed Price Incentives’ Impact on Consumers’ Preferences for Premium Products. Journal of the Academy of Marketing Science, 1043–1064. https://doi.org/10.1007/s11747-021-00791-1/Published
Nasution, S. R., Sari, R. F., & Widyasari, R. (2023). Analisis Klaster dengan Metode K-Means Pada Penyebaran Kasus Covid-19 Berdasarkan Kabupaten/Kota di Sumatera Utara. G-Tech: Jurnal Teknologi Terapan, 7(3), 1308–1314. https://doi.org/10.33379/gtech.v7i3.2904
Niloy, S. R., Hasan, T. M., Apu, Md. S., Hasan, R., Shahin, K. I., Nguyen, H.-H., & Farid, D. Md. (2024). Customer Segmentation and Classification Using K-Modes Clustering with Ensemble Learning. In N. Thai-Nghe, T.-N. Do, & S. Benferhat (Eds.), Intelligent Systems and Data Science (pp. 3–18). Springer Nature Singapore.
Raja, P. S., & Thangavel, K. (2020). Missing Value Imputation using Unsupervised Machine Learning Techniques. Soft Computing, 24(6), 4361–4392. https://doi.org/10.1007/s00500-019-04199-6
Rane, N. L., Achari, A., & Choudhary, S. P. (2023). Enhancing Customer Loyalty Through Quality of Service: Effective Strategies to Improve Customer Satisfaction, Experience, Relationship, and Engagement. International Research Journal of Modernization in Engineering Technology and Science. https://doi.org/10.56726/irjmets38104
Rane, N. L., Paramesha, M., Choudhary, S. P., & Rane, J. (2024). Artificial Intelligence, Machine Learning, and Deep Learning for Advanced Business Strategies: A Review. Partners Universal International Innovation Journal (PUIIJ), 2. https://doi.org/10.5281/zenodo.12208298
Ros, F., Riad, R., & Guillaume, S. (2023). PDBI: A partitioning Davies-Bouldin index for clustering evaluation. Neurocomputing, 528, 178–199. https://doi.org/https://doi.org/10.1016/j.neucom.2023.01.043
Sampathrajan, S., Priya MCA, R., & MPhil, Be. (2023). Enhance Enterprise Marketing Strategy by Target Customer Segmentation Based on Customer’s Variances. International Journal of Research In Computer Applications and Information Technology (IJRCAIT), 6(1), 9–27. https://doi.org/10.17605/OSF.IO/HR2BW
Smith, J. D. (2024). The Impact of Technology on Sales Performance in B2B Companies. Journal of Artificial Intelligence General Science (JAIGS), 3. http://creativecommons.org/licenses/by/4.0
Sudirjo, F. (2023). Marketing Strategy in Improving Product Competitiveness in the Global Market. Journal of Contemporary Administration and Management (ADMAN), 1(2), 63–69. https://doi.org/10.61100/adman.v1i2.24
Tabianan, K., Velu, S., & Ravi, V. (2022). K-Means Clustering Approach for Intelligent Customer Segmentation Using Customer Purchase Behavior Data. Sustainability (Switzerland), 14(12). https://doi.org/10.3390/su14127243
Tarek, A., & Hossam, A. (2022). Evaluating the Effectiveness of AI-Driven Customer Segmentation in Enhancing Targeted Marketing Strategies. Journal of Computational Social Dynamics Research Article: Journal of Computational Social Dynamics, 7.
Vatavwala, S., Kumar, B., Sharma, A., Billore, A., & Sadh, A. (2022). Customer disengagement in business-to-business markets: A framework for analysis. Industrial Marketing Management, 105, 114–130. https://doi.org/https://doi.org/10.1016/j.indmarman.2022.05.018
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