Articles
| Open Access |
DOI: MACHINE LEARNING APPROACHES FOR DEMAND FORECASTING: THE IMPACT OF CUSTOMER SATISFACTION ON PREDICTION ACCURACY
Ashim Chandra Das , Master of Science in Information Technology, Washington University of Science and Technology, USA Md Shahin Alam Mozumder , Master of Science in Information Technology, Washington University of Science and Technology, USA Md Amit Hasan , Master of Science in Information Technology, Washington University of Science and Technology, USA Maniruzzaman Bhuiyan , Satish & Yasmin Gupta College of Business, University of Dallas, Texas Md Rasibul Islam , Department of Management Science and Quantitative Methods, Gannon University, USA Md Nur Hossain , Master’s in information technology management, Webster University, USA Salma Akter , Department of Public Administration, Gannon University, Erie, PA, USA Md Imdadul Alam , Master of Science in Financial Analysis, Fox School of Business, Temple University, USAAbstract
This study investigates the effectiveness of various machine learning models in predicting product demand based on customer satisfaction data. Four models—Linear Regression, Random Forest, Gradient Boosting, and Support Vector Machine (SVM)—were evaluated using performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R² score. The results indicate that Gradient Boosting achieved the highest accuracy, with an MAE of 2.56, MSE of 12.75, RMSE of 3.57, and R² score of 0.82, effectively capturing the complex, non-linear relationships inherent in customer satisfaction factors. Random Forest also demonstrated strong performance, while Linear Regression and SVM showed limitations in handling intricate datasets. These findings underscore the importance of utilizing advanced machine learning techniques for accurate demand forecasting, highlighting the critical role of customer satisfaction data in enhancing predictive capabilities. The insights gained from this research can guide organizations in optimizing inventory management and improving customer satisfaction in a rapidly evolving market.
zenodo DOI:- https://doi.org/10.5281/zenodo.13908001
Keywords
Product Demand Forecasting, Customer Satisfaction, Machine Learning
References
Anderson, E. W., & Mittal, V. (2000). Strengthening the satisfaction-profit chain. Journal of Service Research, 3(2), 107-120. https://doi.org/10.1177/109467050032002
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts. https://otexts.com/fpp3/
Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems (pp. 4765-4774).
Papachristos, G., Kourentzes, N., & Petropoulos, F. (2021). Ensemble methods for forecasting: A review and a case study. International Journal of Forecasting, 37(4), 1395-1412. https://doi.org/10.1016/j.ijforecast.2021.01.003
Zhang, J., Zhang, Z., & Chen, Y. (2017). The impact of online customer reviews on product sales: Evidence from the online retail market. Journal of Retailing and Consumer Services, 39, 334-339. https://doi.org/10.1016/j.jretconser.2017.07.007
Mozumder, M. A. S., Nguyen, T. N., Devi, S., Arif, M., Ahmed, M. P., Ahmed, E., ... & Uddin, A. (2024). Enhancing Customer Satisfaction Analysis Using Advanced Machine Learning Techniques in Fintech Industry. Journal of Computer Science and Technology Studies, 6(3), 35-41.
Modak, C., Ghosh, S. K., Sarkar, M. A. I., Sharif, M. K., Arif, M., Bhuiyan, M., ... & Devi, S. (2024). Machine Learning Model in Digital Marketing Strategies for Customer Behavior: Harnessing CNNs for Enhanced Customer Satisfaction and Strategic Decision-Making. Journal of Economics, Finance and Accounting Studies, 6(3), 178-186.
Mozumder, M. A. S., Nguyen, T. N., Devi, S., Arif, M., Ahmed, M. P., Ahmed, E., ... & Uddin, A. (2024). Enhancing Customer Satisfaction Analysis Using Advanced Machine Learning Techniques in Fintech Industry. Journal of Computer Science and Technology Studies, 6(3), 35-41.
Modak, C., Ghosh, S. K., Sarkar, M. A. I., Sharif, M. K., Arif, M., Bhuiyan, M., ... & Devi, S. (2024). Machine Learning Model in Digital Marketing Strategies for Customer Behavior: Harnessing CNNs for Enhanced Customer Satisfaction and Strategic Decision-Making. Journal of Economics, Finance and Accounting Studies, 6(3), 178-186.
Sarkar, M. A. I., Reja, M. M. S., Arif, M., Uddin, A., Sharif, K. S., Tusher, M. I., Devi, S., Ahmed, M. P., Bhuiyan, M., Rahman, M. H., Mamun, A. A., Rahman, T., Asaduzzaman, M., & Ahmmed, M. J. (2024). Credit risk assessment using statistical and machine learning: Basic methodology and risk modeling applications. International Journal on Computational Engineering, 1(3), 62-67. https://www.comien.org/index.php/comien
Arif, M., Hasan, M., Al Shiam, S. A., Ahmed, M. P., Tusher, M. I., Hossan, M. Z., ... & Imam, T. (2024). Predicting Customer Sentiment in Social Media Interactions: Analyzing Amazon Help Twitter Conversations Using Machine Learning. International Journal of Advanced Science Computing and Engineering, 6(2), 52-56.
Mozumder, M. A. S., Nguyen, T. N., Devi, S., Arif, M., Ahmed, M. P., Ahmed, E., ... & Uddin, A. (2024). Enhancing Customer Satisfaction Analysis Using Advanced Machine Learning Techniques in Fintech Industry. Journal of Computer Science and Technology Studies, 6(3), 35-41.
Shahid, R., Mozumder, M. A. S., Sweet, M. M. R., Hasan, M., Alam, M., Rahman, M. A., ... & Islam, M. R. (2024). Predicting Customer Loyalty in the Airline Industry: A Machine Learning Approach Integrating Sentiment Analysis and User Experience. International Journal on Computational Engineering, 1(2), 50-54.
Modak, C., Ghosh, S. K., Sarkar, M. A. I., Sharif, M. K., Arif, M., Bhuiyan, M., ... & Devi, S. (2024). Machine Learning Model in Digital Marketing Strategies for Customer Behavior: Harnessing CNNs for Enhanced Customer Satisfaction and Strategic Decision-Making. Journal of Economics, Finance and Accounting Studies, 6(3), 178-186.
Mozumder, M. A. S., Mahmud, F., Shak, M. S., Sultana, N., Rodrigues, G. N., Al Rafi, M., ... & Bhuiyan, M. S. M. (2024). Optimizing Customer Segmentation in the Banking Sector: A Comparative Analysis of Machine Learning Algorithms. Journal of Computer Science and Technology Studies, 6(4), 01-07.
Chowdhury, M. S., Shak, M. S., Devi, S., Miah, M. R., Al Mamun, A., Ahmed, E., ... & Mozumder, M. S. A. (2024). Optimizing E-Commerce Pricing Strategies: A Comparative Analysis of Machine Learning Models for Predicting Customer Satisfaction. The American Journal of Engineering and Technology, 6(09), 6-17.
Miah, J., Ca, D. M., Sayed, M. A., Lipu, E. R., Mahmud, F., & Arafat, S. Y. (2023, November). Improving Cardiovascular Disease Prediction Through Comparative Analysis of Machine Learning Models: A Case Study on Myocardial Infarction. In 2023 15th International Conference on Innovations in Information Technology (IIT) (pp. 49-54). IEEE.
Miah, J., Cao, D. M., Sayed, M. A., Taluckder, M. S., Haque, M. S., & Mahmud, F. (2023). Advancing Brain Tumor Detection: A Thorough Investigation of CNNs, Clustering, and SoftMax Classification in the Analysis of MRI Images. arXiv preprint arXiv:2310.17720.
Article Statistics
Copyright License
Copyright (c) 2024 Miah , Jonayet, Ashim Chandra Das, Md Shahin Alam Mozumder, Md Amit Hasan, Maniruzzaman Bhuiyan, Md Rasibul Islam, Md Nur Hossain, Salma Akter, Md Imdadul Alam
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain the copyright of their manuscripts, and all Open Access articles are disseminated under the terms of the Creative Commons Attribution License 4.0 (CC-BY), which licenses unrestricted use, distribution, and reproduction in any medium, provided that the original work is appropriately cited. The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations.