A Novel Approach for Prediction of Consumer Buying Behaviour of Luxury Fashion Goods Using Machine Learning Algorithms
Keywords:
Luxury fashion, Consumer buying behaviour, Predictive analyticsAbstract
Consumer behaviour in the luxury fashion sector is a dynamic interplay of intricate factors, requiring businesses to adopt innovative methodologies for accurate prediction. This study introduces a novel approach that integrates advanced machine learning algorithms to forecast consumer buying behaviour in the realm of luxury fashion goods. Leveraging a diverse set of models, including decision trees, ensemble methods, and neural networks, our methodology scrutinizes vast datasets encompassing demographic information, online interactions, and historical purchase patterns. The core of our approach lies in predictive analytics, aiming to enhance the precision of forecasting models. By doing so, businesses can anticipate and respond proactively to shifts in consumer preferences. This research not only sheds light on the intricacies of consumer decision-making but also holds implications for refining marketing strategies, optimizing inventory management, and guiding product development within the luxury fashion sector. As the luxury fashion industry grapples with the challenges of an ever-changing consumer landscape, our innovative approach provides a promising avenue for businesses. Through the power of data-driven insights, it fosters a more adaptive and consumer-centric approach to marketing luxury fashion goods, ensuring a strategic edge in an increasingly competitive market.
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References
Yang, J., & Kwon, Y. (2023). Novel CNN-Based Approach for Reading Urban Form Data in 2D Images: An Application for Predicting Restaurant Location in Seoul, Korea. ISPRS International Journal of Geo-Information, 12(9), 373.
Shahidzadeh, M. H., Shokouhyar, S., Javadi, F., & Shokoohyar, S. (2022). Unscramble social media power for waste management: A multilayer deep learning approach. Journal of Cleaner Production, 377, 134350.
Guo, W., Tian, J., & Li, M. (2023). Price-aware enhanced dynamic recommendation based on deep learning. Journal of Retailing and Consumer Services, 75, 103500.
Ebrahimi, P., Salamzadeh, A., Soleimani, M., Khansari, S. M., Zarea, H., & Fekete-Farkas, M. (2022). Startups and consumer purchase behavior: Application of support vector machine algorithm. Big Data and Cognitive Computing, 6(2), 34.
Ruano, M., & Huang, C. Y. (2023). A Novel Approach to Service Design within the Tourism Industry: Creating a Travel Package with AHP-TRIZ Integration. Systems, 11(4), 178.
ul Hasan, H. R., Lang, C., & Xia, S. (2022). Investigating consumer values of secondhand fashion consumption in the mass market vs. luxury market: a text-mining approach. Sustainability, 15(1), 254.
Wang, J., Chong, W. K., Lin, J., & Hedenstierna, C. P. T. (2023). Retail Demand Forecasting Using Spatial-Temporal Gradient Boosting Methods. Journal of Computer Information Systems, 1-13.
Christen, T., Hess, M., Grichnik, D., & Wincent, J. (2022). Value-based pricing in digital platforms: A machine learning approach to signaling beyond core product attributes in cross-platform settings. Journal of Business Research, 152, 82-92.
Seyedan, M., Mafakheri, F., & Wang, C. (2022). Cluster-based demand forecasting using Bayesian model averaging: An ensemble learning approach. Decision Analytics Journal, 3, 100033.
Kuang, M., Safa, R., Edalatpanah, S. A., & Keyser, R. S. (2023). A HYBRID DEEP LEARNING APPROACH FOR SENTIMENT ANALYSIS IN PRODUCT REVIEWS. Facta Universitatis, Series: Mechanical Engineering, 21(3), 479-500.
Rane, N. (2023). Enhancing Customer Loyalty through Artificial Intelligence (AI), Internet of Things (IoT), and Big Data Technologies: Improving Customer Satisfaction, Engagement, Relationship, and Experience. Internet of Things (IoT), and Big Data Technologies: Improving Customer Satisfaction, Engagement, Relationship, and Experience (October 13, 2023).
Alaql, A. A., Alqurashi, F., & Mehmood, R. (2023). Multi-generational labour markets: data-driven discovery of multi-perspective system parameters using machine learning. arXiv preprint arXiv:2302.10146.
Wen, H. (2023). Webcast marketing platform optimization via 6G R&D and the impact on brand content creation. Plos one, 18(10), e0292394.
Sharma, M., Shail, H., Painuly, P. K., & Kumar, A. S. (2023). AI-Powered Technologies Used in Online Fashion Retail for Sustainable Business: AI-Powered Technologies Impacting Consumer Buying Behavior. In Sustainable Marketing, Branding, and Reputation Management: Strategies for a Greener Future (pp. 538-561). IGI Global.
Rana, J., Gaur, L., Singh, G., Awan, U., & Rasheed, M. I. (2022). Reinforcing customer journey through artificial intelligence: a review and research agenda. International Journal of Emerging Markets, 17(7), 1738-1758.
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