Customer Segmentation and Business Sales Forecasting using Machine Learning for Business Development
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Abstract
This study explores the application of machine learning techniques for business development, focusing on sales prediction and customer segmentation, using a Walmart dataset. Performance metrics include Mean Absolute Error (MAE) and R2 scores. Our hybrid approach combines the BIRCH algorithm with time-lagged machine learning (TL-ML). The results reveal that customer segmentation significantly improves model performance across all metrics. Among the techniques tested, models incorporating customer segmentation (CS-RFR and CS-TL-ML) outperform standard Random Forest Regressor models. Specifically, CS-TL-ML shows a slight advantage in terms of both lower MAE and higher R2 scores, confirming its efficacy for sales prediction and customer segmentation tasks.
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C. Shao, Y. Yang, S. Juneja, and T. GSeetharam, “IoT data visualization for business intelligence in corporate finance,” Inf. Process. Manag., vol. 59, no. 1, p. 102736, Jan. 2022, doi: 10.1016/J.IPM.2021.102736.
A. Gehlot, B. K. Ansari, D. Arora, H. Anandaram, B. Singh, and J. L. Arias-Gonzáles, “Application of Neural Network in the Prediction Models of Machine Learning Based Design,” Proc. 2022 Int. Conf. Innov. Comput. Intell. Commun. Smart Electr. Syst. ICSES 2022, 2022, doi: 10.1109/ICSES55317.2022.9914184.
Ali, Naeem, Taher M. Ghazal, Alia Ahmed, Sagheer Abbas, M. A. Khan, Haitham M. Alzoubi, Umar Farooq, Munir Ahmad, and Muhammad Adnan Khan. "Fusion-based supply chain collaboration using machine learning techniques." Intelligent Automation and Soft Computing 31, no. 3 (2022): 1671-1687.
M. Huber, J. Meier, and H. Wallimann, “Business analytics meets artificial intelligence: Assessing the demand effects of discounts on Swiss train tickets,” Transp. Res. Part B Methodol., vol. 163, pp. 22–39, Sep. 2022, doi: 10.1016/J.TRB.2022.06.006.
D. Irfan, X. Tang, V. Narayan, P. K. Mall, S. Srivastava, and V. Saravanan, “Prediction of Quality Food Sale in Mart Using the AI-Based TOR Method,” J. Food Qual., vol. 2022, 2022, doi: 10.1155/2022/6877520.
M. Hawkins, “Metaverse Live Shopping Analytics: Retail Data Measurement Tools, Computer Vision and Deep Learning Algorithms, and Decision Intelligence and Modeling,” J. Self-Governance Manag. Econ., vol. 10, no. 2, pp. 2377–0996, 2022, doi: 10.22381/jsme10220222.
R. Patriarca, G. Di Gravio, R. Cioponea, and A. Licu, “Democratizing business intelligence and machine learning for air traffic management safety,” Saf. Sci., vol. 146, p. 105530, Feb. 2022, doi: 10.1016/J.SSCI.2021.105530.
M. N. Omri and W. Mribah, “Intelligent Systems and Applications, 2022, 1, 1-23J. Intelligent Systems and Applications,” vol. 1, pp. 1–23, 2022, doi: 10.5815/ijisa.2022.01.01.
Watson, Robert. "The virtual economy of the metaverse: Computer vision and deep learning algorithms, customer engagement tools, and behavioral predictive analytics." Linguistic and Philosophical Investigations 21 (2022): 41-56.
A. Shewale, A. Mokhade, N. Funde, and N. D. Bokde, “A Survey of Efficient Demand-Side Management Techniques for the Residential Appliance Scheduling Problem in Smart Homes,” Energies 2022, Vol. 15, Page 2863, vol. 15, no. 8, p. 2863, Apr. 2022, doi: 10.3390/EN15082863.
Y. Niu, L. Ying, J. Yang, M. Bao, and C. B. Sivaparthipan, “Organizational business intelligence and decision making using big data analytics,” Inf. Process. Manag., vol. 58, no. 6, p. 102725, Nov. 2021, doi: 10.1016/J.IPM.2021.102725.
M. D. Tamang, V. Kumar Shukla, S. Anwar, and R. Punhani, “Improving Business Intelligence through Machine Learning Algorithms,” Proc. 2021 2nd Int. Conf. Intell. Eng. Manag. ICIEM 2021, pp. 63–68, Apr. 2021, doi: 10.1109/ICIEM51511.2021.9445344.
C. A. Tavera Romero, J. H. Ortiz, O. I. Khalaf, and A. R. Prado, “Business Intelligence: Business Evolution after Industry 4.0,” Sustain. 2021, Vol. 13, Page 10026, vol. 13, no. 18, p. 10026, Sep. 2021, doi: 10.3390/SU131810026.
J. Ranjan and C. Foropon, “Big Data Analytics in Building the Competitive Intelligence of Organizations,” Int. J. Inf. Manage., vol. 56, p. 102231, Feb. 2021, doi: 10.1016/J.IJINFOMGT.2020.102231.
Sakura Nakamura, Machine Learning in Environmental Monitoring and Pollution Control , Machine Learning Applications Conference Proceedings, Vol 3 2023.
A. J. Nakhal A, R. Patriarca, G. Di Gravio, G. Antonioni, and N. Paltrinieri, “Investigating occupational and operational industrial safety data through Business Intelligence and Machine Learning,” J. Loss Prev. Process Ind., vol. 73, p. 104608, Nov. 2021, doi: 10.1016/J.JLP.2021.104608.
J. Z. Zhang, P. R. Srivastava, D. Sharma, and P. Eachempati, “Big data analytics and machine learning: A retrospective overview and bibliometric analysis,” Expert Syst. Appl., vol. 184, p. 115561, Dec. 2021, doi: 10.1016/J.ESWA.2021.115561.
K. Annapurani, E. Poovammal, C. Ruvinga, and I. Venkat, “Healthcare Data Analytics Using Business Intelligence Tool,” Mach. Learn. Anal. Healthc. Syst., pp. 191–212, Jun. 2021, doi: 10.1201/9781003185246-10.
Prof. Arun Pawar, Mr. Dharmesh Dhabliya. (2018). Intelligent Modulation Recognition System and its Implementation using MATLAB. International Journal of New Practices in Management and Engineering, 7(01), 08 - 14. https://doi.org/10.17762/ijnpme.v7i01.63.
S. Fraihat, W. A. Salameh, A. Elhassan, B. A. Tahoun, and M. Asasfeh, “Business Intelligence Framework Design and Implementation: A Real-estate Market Case Study,” ACM J. Data Inf. Qual., vol. 13, no. 2, Jun. 2021, doi: 10.1145/3422669.
H. W. Khan et al., “Intelligent Optimization Framework for Efficient Demand-Side Management in Renewable Energy Integrated Smart Grid,” IEEE Access, vol. 9, pp. 124235–124252, 2021, doi: 10.1109/ACCESS.2021.3109136.
A. J. Silva, P. Cortez, C. Pereira, and A. Pilastri, “Business analytics in Industry 4.0: A systematic review,” Expert Syst., vol. 38, no. 7, p. e12741, Nov. 2021, doi: 10.1111/EXSY.12741.
https://www.kaggle.com/competitions/walmart-recruiting-store-sales-forecasting/data