Machine Learning in Business: Product Bundling Strategy and Customer Segmentation via Market Basket Analysis Algorithm
Kata Kunci:
Manajemen Pemasaran, Manajemen Strategis, Pembelajaran Mesin, Segmentasi PelangganAbstrak
Penelitian ini menggunakan algoritma analisis keranjang belanja untuk mengetahui asosiasi antara kategori produk dan nama barang. Algortima ini bertujuan untuk mengidentifikasi pola pembelian pelanggan dan segmentasi pelanggan di berbagai wilayah. Sebuah perusahaan yang menjual bahan bangunan di Pontianak, Kalimantan Barat, Indonesia, belum pernah menganalisis data riwayat penjualan untuk meningkatkan strategi promosi atau memperoleh wawasan terkait segmentasi pelanggan berdasarkan wilayah. Penelitian ini menggunakan pendekatan kuantitatif eksperimental dengan perusahaan yang berlokasi di Pontianak sebagai objek studi, menggunakan data primer berupa riwayat penjualan, basis data pelanggan, dan basis data produk. Populasi penelitian terdiri atas 12.600 transaksi penjualan, dengan sampel sebanyak 3.462 transaksi yang berfokus pada merek Onda dari 2 Januari hingga 30 Desember 2023. Hasil algoritma analisis keranjang belanja akan dievaluasi berdasarkan nilai support, confidence, dan lift. Dari hasil analisis, ditemukan asosiasi antara subkategori produk dan nama barang, yang memberikan wawasan untuk menentukan strategi bundling atau cross-selling berdasarkan pola pembelian konsumen, seperti kombinasi stop kran JF 11 dengan kran wastafel Y 321 C. Segmentasi pelanggan berdasarkan minat konsumen di setiap wilayah juga diperoleh, yang dapat digunakan untuk mengembangkan strategi periklanan pada platform media sosial guna meningkatkan penjualan produk dan kesadaran merek.
Referensi
Alawadh, K. S., & Barnawi, A. (2022). A Survey on Methods and Applications of Intelligent Market Basket Analysis Based on Association Rule. Journal on Big Data, 4(1). doi:10.32604/jbd.2022.021744
Efrat, A. R., Gernowo, R., & Farikhin. (2020). Consumer Purchase Patterns Based on Market Basket Analysis Using Apriori Algorithms. The 9th International Seminar on New Paradigm and Innovation of Natural Sciences. Central Java: IOP Publishing Ltd. doi:10.1088/1742-6596/1524/1/012109
Gehlot, A., & Singh, R. (2022). Execution of Market Basket Analysis and Recommendation Systems in Physical Retail Stores to Advance Sales Revenues. 2022 International Interdisciplinary Humanitarian Conference for Sustainability (IIHC), 517-522. doi:10.1109/IIHC55949.2022.10060559
Griva, A., Zampou, E., Stavrou, V., Papakiriakopoulos, D., & Doudikis, G. (2024). A Two-Stage Business Analytics Approach to Perform Behavioural and Geographic Customer Segmentation Using E-Commerce Delivery Data. Journal of Decision Systems, 1-29. doi:10.1080/12460125.2022.2151071
Hananto, A., & Arizona, P. (2020). Customer profiling and market basket analysis using k-means algorithm and association rule mining: Evidence from Indonesia e-commerce company. In Advances in Business, Management, and Entrepreneurship (p. 6). CRC Press.
Iswahyudi, M. S., Budaya, I., Purwoko, P., Riswanto, A., & Anggia Ayu Lestari, E. W. (2023). Manajemen Pemasaran : Strategi dan Praktek yang efektif. Jambi: PT. Sonpedia Publishing Indonesia.
Kabasakal, İ. (2020). Understanding Shopping Behaviors With Category- and Brand-Level Market Basket Analysis. Journal of IGI Global, 26. doi:10.4018/978-1-7998-0035-4.ch012
Li, Y., Chu, X., Tian, D., Feng, J., & Mu, W. (2021). Customer Segmentation Using K-Means Clustering and The Adaptive Particle Swarm Optimization Algorithm. Applied Soft Computing, 113.
Maheswari, B. U., & Sujatha, R. (2023). Marketing Analytics. In B. U. Maheswari, & R. Sujatha, Understanding Consumer Behavior Using Market Basket Analysis (p. 29). Apple Academic Press. doi:10.1201/9781003300632-7
Musalem, A., Aburto, L., & Bosch, M. (2018). Market Basket Analysis Insights to Support Category Management. European Journal of Marketing, 52, 1550-1573. doi:10.1108/EJM-06-2017-0367
Nadiah, Soim, S., & Sholihin. (2022). Implementation of Decision Tree Algorithm Machine Learning in Detecting Covid-19 Virus Patients Using Public Datasets. Indonesian Journal of Artificial Intelligence and Data Mining (IJAIDM), 33-43.
Nagaraj, S. (2021). Market Basket Analysis: An Effective Data-Mining Technique for Anticipating Consumer Purchase Behavior. In Big Data Analytics (p. 13). New York: Auerbach Publications.
Neupane, A., Dhakal, B., Aryal, B., & Kandel, N. (2023). Identifying Product Bundle from Market Basket Analysis. International Journal on Engineering Technology, 1(1), 129–138. doi:https://doi.org/10.3126/injet.v1i1.60935
Olson, D. L., & Lauhoff, G. (2019). Descriptive Data Mining. Singapore: Springer.
Pani, S. K., Tripathy, S., Jandieri, G., Kundu, S., & Butt, T. A. (2021). Applications of Machine Learning in Big-Data Analytics and Cloud Computing. Gisturp: River Publishers.
Patwary, A. H., Eshan, M. T., Debnath, P., & Sattar, A. (2021). Market Basket Analysis Approach to Machine Learning. 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), 1-9. doi:10.1109/ICCCNT51525.2021.9580175
Pillai, R. A., & Jolhe, D. A. (2021). Market Basket Analysis: Case Study of a Supermarket. Lecture Notes in Mechanical Engineering ((LNME)) (pp. 727–734). Singapore: Springer. doi:10.1007/978-981-15-3639-7_87
Sumathi, S., Rajappa, S., Kumar, L. A., & Paneerselvam, S. (2022). Machine Learning for Decision Sciences with Case Studies in Python. Boca Raton: CRC Press.
Suryadi, A., & Islami, M. C. (2022). Analysis of Data Mining at Supermarket X in Surabaya Using Market Basket Analysis to Determine Consumer Buying Patterns. 3rd International Conference Eco-Innovation in Science, Engineering, and Tech-nology (pp. 28-32). NST Proceedings. doi:10.11594/nstp.2022.2705
Vijayalakshmi, V., & Selvan, S. (2023). Advanced Data Mining. India: SK Research Group of Companies.
