Analysing the Refurbished Smart Phone Market with Machine Learning
Öz
The refurbished smartphone market has recently attracted attention because of its economic and environmental benefits. In particular, rising environmental awareness and the search for cost-effective alternatives have increased demand for refurbished products. However, the dynamics of this market and its pricing practices differ from those of the new-device market.
Price formation depends on several product-specific factors, including device condition and model. Yet, analysing this multi-factor structure and producing accurate price estimates remains challenging for consumers, sellers, and remanufacturers. In this context, machine learning can support high-accuracy price prediction.
Developing feature-based price prediction models for refurbished smartphones helps to explain price fluctuations and to estimate a device’s value by considering usage and post-refurbishment condition. In this study, both traditional machine learning and deep learning methods are used to improve prediction accuracy. Model performance is evaluated using MSE, MAE, RMSE, and the R² score. The XGB Regressor achieved the best result among the traditional machine learning algorithms, with an R² of 0.9902. Among the deep learning models, LSTM also performed strongly, reaching an R² of 0.9870.
Anahtar Kelimeler
Refurbished Market, Smartphone Price, Regression, Price Prediction, Sustainability
Teşekkür
SENATECH BİLGİ TEKNOLOJİLERİ SANAYİ TİCARET A.Ş.'ye gerçek piyasa verilerine ulaşma, sektörel deneyimlerle çalışmanın bulgularını doğrulama ve alan uzmanlarıyla saha araştırmaları gerçekleştirme konusundaki değerli katkıları için içten teşekkürlerimizi sunarız.
Kaynakça
- Agostini, L., Bigliardi, B., Filippelli, S., & Galati, F. (2021). Seller reputation, distribution and intention to purchase refurbished products. Journal of Cleaner Production, 316, 128296. https://doi.org/10.1016/j.jclepro.2021.128296.
- Barros, M., & Dimla, E. (2021). From planned obsolescence to the circular economy in the smartphone industry: An evolution of strategies embodied in product features. Proceedings of the Design Society, 1, 1607-1616. https://doi.org/10.1017/pds.2021.422.
- F. M. Basysyar and G. Dwilestari. (2022). House price prediction using exploratory data analysis and machine learning with feature selection, Acadlore Trans. Mach. Learn., vol. 1, no. 1, pp. 11-21. https://doi.org/10.56578/ataiml010103.
- Bigliardi, B., Filippelli, S., & Quinto, I. (2022). Environmentally-conscious behaviours in the circular economy. An analysis of consumers' green purchase intentions for refurbished smartphones. Journal of Cleaner Production, 378, 134379. https://doi.org/10.1016/j.jclepro.2022.134379.
- Charbuty, B., & Abdulazeez, A. (2021). Classification Based on Decision Tree Algorithm for Machine Learning. (2021). Journal of Applied Science and Technology Trends, 2(01), 20-28. https://doi.org/10.38094/jastt20165.
- Chen, J., & Lin, S. (2004). A neural network approach-decision neural network (DNN) for preference assessment. in IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 34, no. 2, pp. 219-225, May 2004. https://doi.org/10.1109/TSMCC.2003.819703.
- Chicco D, Warrens MJ, Jurman G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science 7:e623. https://doi.org/10.7717/peerj-cs.623.
- Dhapte, A. (2024, October). Global refurbished smartphone market overview. Market Research Future. Retrieved from https://www.marketresearchfuture.com/reports/refurbished-smartphone-market-11690.
- Gülmez, B., & Kulluk, S. (2023). Türkiye’de ikinci el araçların büyük veri ve makine öğrenme teknikleriyle analizi ve fiyat tahmini. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 38(4), 2279-2290. https://doi.org/10.17341/gazimmfd.980840.
- Halim, S., San, G. S., & Oentoro, J. (2022). Identifying factors that influence customers’ interest in buying refurbished smartphones: An Indonesian context. Petra Christian University, https://so01.tci- thaijo.org/index.php/APST/article/view/258256.