Visit Recommendation Model: Recursive K-Means Clustering Analysis of Retail Sales Data
Abstract
In the context of retail distribution, this study employs recursive K-means clustering on retail sales data to optimize clusters of nearest-distance stores for salesperson route recommendations. This approach addresses the stochastic salesperson problem by generating effective routes, enhancing cost reduction, and improving service efficiency. The recursive K-means algorithm dynamically adjusts to continuous changes in store numbers, locations, and transaction data. Consequently, this research successfully developed a model that automatically re-clusters the data with each change, providing continuously updated and effective store recommendations.
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References
Levy, M., & Sharma, A. Relationship among Measures of Retail Salesperson Performance. Journal of the Academy of Marketing Science, 21(3), 231-238, 1993..
James S. Boles, Barry J. Babin, Thomas G. Brashear & Charles Brooks, An Examination of the Relationships between Retail Work Environments, Salesperson Selling Orientation-Customer Orientation and Job Performance, Journal of Marketing Theory and Practice, 9:3, 1-13, 2001.
Abhijit Guha, Dhruv Grewal, Praveen K. Kopalle, Michael Haenlein, Matthew J. Schneider, Hyunseok Jung, Rida Moustafa, Dinesh R. Hegde, Gary Hawkins, How artificial intelligence will affect the future of retailing, Journal of Retailing, 97(1), 28-41, 2021.
Auke Hunneman, Tammo H.A. Bijmolt, J. Paul Elhorst, Evaluating store location and department composition based on spatial heterogeneity in sales potential, Journal of Retailing and Consumer Services, 2023.
Nagel, D.M., Cronin, J.J., Bourdeau, B.L., Hopkins, C.D., Brocato, D., Retailing in the Digital Age: Surviving Mobile App Failure: An Abstract. In: Rossi, P., Krey, N. (eds) Finding New Ways to Engage and Satisfy Global Customers. AMS WMC 2018. Developments in Marketing Science: Proceedings of the Academy of Marketing Science. Springer, Cham, 2019.
S. Anily, A. Federgruen, One Warehouse Multiple Retailer Systems with Vehicle Routing Costs. Management Science, 36(1):92-114, 1990.
Moussa, Hassan. Using Recursive KMean and Dijkstra Algorithms to Solve {CVRP}. arXiv:2102.00567
Alfiyatin, A. N., Mahmudy, W. F., & Anggodo, Y. P. (2018). K-Means Clustering and Genetic Algorithm to Solve Vehicle Routing Problem with Time Windows Problem. Indonesian Journal of Electrical Engineering and Computer Science, 11(2), 462.
N. P. Barbosa, E. S. Christo, and K. A. Costa, “Demand forecasting for production planning in a food company,” ARPN J. Eng. Appl. Sci., vol. 10, no. 16, pp. 7137–7141, 2015.
V. K. Anand, S. K. A. Rahiman, E. Ben George and A. S. Huda, "Recursive clustering technique for students' performance evaluation in programming courses," 2018 Majan International Conference (MIC), Muscat, Oman, 2018, pp. 1-5
L. A. Maglaras and J. Jiang, "OCSVM model combined with K-means recursive clustering for intrusion detection in SCADA systems," 10th International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, Rhodes, Greece, 2014, pp. 133-13
Laporte, G., Louveaux, F.V., Mercure, H., 1992. The vehicle routing problem with stochastic travel time. Transp. Sci. 26 (3), 161-170
Kenyon, Astrid S. and David P. Morton. “Stochastic Vehicle Routing with Random Travel Times.” Transp. Sci. 37 (2003): 69-82.
Chen, Angela H. L., Liang, Yun-Chia, Chang, Wan-Ju, Siauw, Hsuan-Yuan, Minanda, Vanny, RFM Model and K-Means Clustering Analysis of Transit Traveller Profiles: A Case Study, Journal of Advanced Transportation, 2022, 1108105, 14 pages, 2022. https://doi.org/10.1155/2022/1108105
MacQueen J., Some methods for classification and analysis of multivariate observations, Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1, June 1967, Berkeley, CA, USA, no. 14, 281–297.
Chowlur Revanna, J. K., & Al-Nakash, N. Y. B. (2022). Vehicle routing problem with time window constraint using KMeans clustering to obtain the closest customer. Global Journal of Computer Science and Technology: Neural & Artificial Intelligence, 22(1), Version 1.0.
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