Presented at the International Conference on Advances in Electrical-Electronics Engineering and Computer Science (ICEEECS2024), Ankara, Türkiye, Nov 09, 2024

SETSCI Conference Proceedings, 2024, 19, Page (s): 17-21 , https://doi.org/10.36287/setsci.19.5.022

Published Date: 21 November 2024    | 221     0

E-commerce is evolving rapidly, creating a more competitive market environment. With this development, gaining a competitive advantage has become even more crucial. Companies implement various strategic moves to maintain their position in this competitive market. Strategies and forecasts have a high ranking among these strategies. Predicting sales cancellations and returns is crucial for companies to anticipate future challenges and stay ahead. The aim of this study is to develop sales cancellation/return prediction models for the e-commerce sector. To achieve this, sales cancellation/return prediction models have been generated using Multi-Layer Perceptron (MLP), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Logistic Regression (LR). A weekly dataset has been created using 4909 rows of sales cancellation/return data. The performance of the models has been evaluated using precision, recall, F1 score, and accuracy. Among all the methods, it has been observed that RF and XGBoost delivered the best performance.

Keywords - Machine Learning, Customer Behavior Analysis, Sales Cancellation/Return Prediction, E-commerce, E-commerce Analytics

[1] Ministry of Trade of the Republic of Turkey. (May 2024). E-commerce outlook report in Turkey.

[2] Chaturvedi, H. (2022, May 30). Product returns are wasteful for companies and the planet. Here’s how to change that. Fast Company

[3] Adebanjo, S., & Banchani, E. (2023). DEVELOPMENT OF MACHINE LEARNING TECHNIQUES FOR DETERMINING OPTIMAL PRICING AND INVENTORY POLICY WITH ORDER CANCELLATION UNDER THE CASH-ON-DELIVERY PAYMENT SCHEME. Advance Journal of Current Research, 8(12), 18-41.

[4] Chen, S., Ngai, E. W., Ku, Y., Xu, Z., Gou, X., & Zhang, C. (2023). Prediction of hotel booking cancellations: Integration of machine learning and probability model based on interpretable feature interaction. Decision Support Systems, 170, 113959.

[5] Dzyabura, D., El Kihal, S., Hauser, J. R., & Ibragimov, M. (2023). Leveraging the power of images in managing product return rates. Marketing Science, 42(6), 1125-1142

[6] Jauhar, S. K., Chakma, B. R., Kamble, S. S., & Belhadi, A. (2024). Digital transformation technologies to analyze product returns in the e-commerce industry. Journal of Enterprise Information Management, 37(2), 456-487.

[7] Liu, Z., Jiang, P., Wang, J., Du, Z., Niu, X., & Zhang, L. (2023). Hospitality order cancellation prediction from a profit-driven perspective. International Journal of Contemporary Hospitality Management, 35(6), 2084-2112.

[8] Qiu, R., Ma, L., & Sun, M. (2023). A robust omnichannel pricing and ordering optimization approach with return policies based on data-driven support vector clustering. European Journal of Operational Research, 305(3), 1337-1354.

[9] Sun, H., Lv, Z., Li, J., Xu, Z., & Sheng, Z. (2023). Will the Order Be Canceled? Order Cancellation Probability Prediction Based on Deep Residual Model. Transportation Research Record, 2677(6), 142-160.

[10] Sweidan, D., Johansson, U., Alenljung, B., & Gidenstam, A. (2023, July). Improved Decision Support for Product Returns using Probabilistic Prediction. In 2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE) (pp. 1567-1573). IEEE.

[11] Wang, Z., Luo, C., Luo, X. R., & Xu, X. (2024). Understanding the effect of group emotions on consumer instant order cancellation behavior in livestreaming E-commerce: Empirical evidence from TikTok. Decision Support Systems, 179, 114147.

[12] Xu, W., Zhang, X., Chen, R., & Yang, Z. (2023). How do you say it matters? A multimodal analytics framework for product return prediction in live streaming e-commerce. Decision Support Systems, 172, 113984.

[13] Al Bataineh, A., & Manacek, S. (2022). MLP-PSO hybrid algorithm for heart disease prediction. Journal of Personalized Medicine, 12(8), 1208.

[14] Park, S., & Kim, J., Landslide susceptibility mapping based on random forest and boosted regression tree models, and a comparison of their performance. Applied Sciences, 9(5), 942, 2019.

[15] Park, S., & Kim, J., Landslide susceptibility mapping based on random forest and boosted regression tree models, and a comparison of their performance. Applied Sciences, 9(5), 942, 2019.

[16] Yuan, H., Yang, G., Li, C., Wang, Y., Liu, J., Yu, H., ... & Yang, X. (2017). Retrieving soybean leaf area index from unmanned aerial vehicle hyperspectral remote sensing: Analysis of RF, ANN, and SVM regression models. Remote Sensing, 9(4), 309.

[17] Bellacicco, M., Vellucci, V., Scardi, M., Barbieux, M., Marullo, S., & D’Ortenzio, F. (2019). Quantifying the impact of linear regression model in deriving bio-optical relationships: the implications on ocean carbon estimations. Sensors, 19(13), 3032.

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