Exploring Hypermarket Product Segmentation using Machine Learning Approach in Muscat City, Oman
DOI:
https://doi.org/10.53840/myjict9-1-187Abstract
The Omani retail industry faces various issues, including identifying patterns in customer behavior and categorizing sold products, analyzing the value of products, and segmenting these products for marketing purposes. In addition, the wide range of products and the differences in consumer purchasing patterns across various product categories, as well as the challenges in precisely monitoring and assigning sales to customers. This research aims to explore the product segmentation at one hypermarket located in Muscat, Sultanate of Oman. Based on consumers' purchase histories, recency frequency monetary value (RFM) Analysis was used to project a product sales trend and to provide recommendations to stakeholders for how to enhance digital platforms. Using methods from unsupervised machine learning, this study constructed models employing K-means and Fuzzy C-means algorithms for hard clustering, and the density-based clustering algorithm (DBSCAN) for soft clustering. Product segmentation procedures were performed to gauge the three algorithms' relative performance. The findings revealed that the DBSCAN method had the best performance, scoring 0.89 across all three clusters, while the K-means algorithm scored 0.724 and the Fuzzy C-means strategy scored 0.702. The results may be used to shed light on client buying habits and provide data-driven suggestions for enhancing stakeholders' digital platforms. In preventing issues like inventory loss, stockouts, and overstock, businesses might benefit from delivering customized suggestions based on consumers' purchasing behavior history. Improved marketing, stock control, and customer satisfaction are all possible benefits from this study.
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Alghanam, O. A., Al-Khatib, S. N., & Hiari, M. O. (2022). Data mining model for predicting customer purchase behavior in e-commerce context. International Journal of Advanced Computer Science and Applications, 13(2)
Alsabti, K., Ranka, S., & Singh, V. (1997). An efficient k-means clustering algorithm.
Alsahafi, M., Nour, M., Alhindi, A., & Farrash, M. (2022, December). Application of Unsupervised Learning and Data Clustering to Saudi Online Stores. In 2022 Fifth National Conference of Saudi Computers Colleges (NCCC) (pp. 71-78). IEEE.
Amos, C., Holmes, G.R. and Keneson, W.C. (2014), “A meta-analysis of consumer impulsive buying”, Journal of Retailing and Consumer Services, Vol. 21 No. 2, pp. 86-97
Bang, J., Cho, Y., & Kim, M. S. (2015). Getting business insights through clustering online behaviors. Modelling and Simulation in Engineering, 2015, 4-4
Brahmana, R. S., Mohammed, F. A., & Chairuang, K. (2020). Customer segmentation based on RFM model using K-means, K-medoids, and DBSCAN methods. Lontar Komput. J. Ilm. Teknol. Inf, 11(1), 32.
Brownlee, J. (2020). 10 Clustering Algorithms with Python. 10 Clustering Algorithms with Python.
Bruestle, S., Pappalardo, L., & Guidotti, R. (2019). Defining Geographic Markets from Probabilistic Clusters: A Machine Learning Algorithm Applied to Supermarket Scanner Data. Available at SSRN 3452058.
Chauhan, S., Banerjee, R., & Dagar, V. (2023). Analysis of impulse buying behaviour of consumer during COVID-19: An empirical study. Millennial Asia, 14(2), 278-299
Dobre, C., & Milovan-Ciuta, A. M. (2015). Personality influences on online stores customers behavior. Ecoforum Journal, 4(1), 9.
Drenik, G. 2021. 2020 Accelerated the Future of ECommerce. What Does That Means For 2021 (Online), Available: https://www.forbes.com/sites/garydrenik/2021/01/21/2020-accelerated-the-future-of-ecommerce-what-does-that-means-for-2021/?sh=372650387167 (Accessed Jan 21, 2021).
Erjavec, J., & Manfreda, A. (2022). Online shopping adoption during COVID-19 and social isolation: Extending the UTAUT model with herd behavior. Journal of Retailing and Consumer Services, 65, 102867
Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996, August). A density-based algorithm for discovering clusters in large spatial databases with noise. In kdd (Vol. 96, No. 34, pp. 226-231).
Fahim, A. M., Salem, A. M., Torkey, F. A., & Ramadan, M. (2006). An efficient enhanced k-means clustering algorithm. Journal of Zhejiang University-Science A, 7, 1626-1633.
Florez-Lopez, R., & Ramon-Jeronimo, J. M. (2009). Marketing segmentation through machine learning models: An approach based on customer relationship management and customer profitability accounting. Social Science Computer Review, 27(1), 96-117
Gustriansyah, R., Suhandi, N., & Antony, F. (2020). Clustering optimization in RFM analysis based on k-means. Indonesian Journal of Electrical Engineering and Computer Science, 18(1), 470-477.
Grimmeau, J. P. (2013). A forgotten anniversary: the first European hypermarkets open in Brussels in 1961. Brussels Studies. La revue scientifique pour les recherches sur Bruxelles/Het wetenschappelijk tijdschrift voor onderzoek over Brussel/The Journal of Research on Brussels.
Hengsberger, A. (2018). 4 reasons why innovations fail. LEAD Innovations Blog. Retrieved from https://www.lead-innovation.com/english-blog/why-innovations-fail
Hu, G., Zhang, Y., & Yang, Q. (2019, May). Transfer meets hybrid: A synthetic approach for cross-domain collaborative filtering with text. In The World Wide Web Conference (pp. 2822-2829)
Idowu, S., Annam, A., Rangaraja, E., & Kattukottai, S. (2019). Customer Segmentation Based on RFM Model Using K-Means, Hierarchical and Fuzzy C-Means Clustering Algorithms
Koofan, A. A. A., & Kaleem, M. (2019). Analyze and Enhance Sales in Lulu Supermarket using Data Mining Technology. Journal of Student Research.
Kseniia, K. (2018). Market Basket Visualization for Hypermarkets with the Use of Big Data Analytics.
Lee, D., & Hosanagar, K. (2019). How do recommender systems affect sales diversity? A cross-category investigation via randomized field experiment. Information Systems Research, 30(1), 239-259.
Ma, L., & Sun, B. (2020). Machine learning and AI in marketing–Connecting computing power to human insights. International Journal of Research in Marketing, 37(3), 481-504
Mahfuza, R., Islam, N., Toyeb, M., Emon, M. A. F., Chowdhury, S. A., & Alam, M. G. R. (2022). LRFMV: An efficient customer segmentation model for superstores. Plos one, 17(12), e0279262.
Medrano, N., Olarte-Pascual, C., Pelegrín-Borondo, J., & Sierra-Murillo, Y. (2016). Consumer behavior in shopping streets: the importance of the salesperson's professional personal attention. Frontiers in psychology, 7, 125
Monalisa, S., & Kurnia, F. (2019). Analysis of DBSCAN and K-means algorithm for evaluating outlier on RFM model of customer behaviour. Telkomnika (Telecommunication Computing Electronics and Control), 17(1), 110-117.
Moon, S. K., Kumara, S. R., & Simpson, T. W. (2006, January). Data mining and fuzzy clustering to support product family design. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 4255, pp. 317-325)
Nguyen, P. (2021). Influence of Recommender System Use on Consumer Decision Making (Master's thesis, Hanken School of Economics)
Shahapure, K. R., & Nicholas, C. (2020, October). Cluster quality analysis using silhouette score. In 2020 IEEE 7th international conference on data science and advanced analytics (DSAA) (pp. 747-748). IEEE
Tahiri, N., Mazoure, B., & Makarenkov, V. (2019). An intelligent shopping list based on the application of partitioning and machine learning algorithms. In proceedings of the 18th Python in Science Conference (SCIPY 2019)
Vehmas, K., Ervasti, M., Tihinen, M., & Mensonen, A. (2015). Digitalization boosting novel digital services for consumers. ACSIJ Advances in Computer Science: An International Journal, 4(4), 80-92.
Zhang, E., Li, H., Huang, Y., Hong, S., Zhao, L., & Ji, C. (2022). Practical multi-party private collaborative k-means clustering. Neurocomputing, 467, 256-265.
Zuo, Y., Yada, K., & Ali, A. S. (2016, December). Prediction of consumer purchasing in a grocery store using machine learning techniques. In 2016 3rd Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE) (pp. 18-25). IEEE.
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