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Subject: search.filters.subject.Fashion Retail

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    PublicationOpen Access
    The Impact of Social Media Marketing on Generation Z’s Purchasing Behavior in the Fashion Retail Industry
    (SLIIT City UNI, 2025-07-08) Christy, R
    This research paper explores how Social media advertising is a strong force of Generation Z consumers in the fashion retail industry. The study explores the influence of advertising, user-generated content, and AI-based recommendations on consumer purchase decisions. Based on secondary research studies, it identifies the key drivers of brand consideration and conversion. The study understands that top fashion brands like Zara and Shein effectively support micro influencers, interactive content, and one-toone marketing, while traditional brands like Gap miss the consumer target. This study highlights the importance of originality, personal experiences, and sustainability messaging to engage Gen Z consumers. The results to brands are to extend micro-influencer partnerships, adopt AI driven content personalization, and step up interactive marketing. This study also provides practical outcomes to policymakers and industry stakeholders who wish to maximize digital marketing effectiveness and consumer interaction.
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    PublicationEmbargo
    Sales Optimization Solution for Fashion Retail
    (IEEE, 2021-12-09) Ganhewa, N. B; Abeyratne, S. M. L. B; Chathurika, G. D. S; Lunugalage, D; De Silva, D. I
    The Fashion industry is one of the extensive, changeable, and growing businesses to exist. It encompasses fashion retailing which functions as a mediator between the manufacturers and clients. On account of the inconsistency of this industry, maximizing sales has been a crucial task. The objective of this research study is to analyze and explore product and consumer behavior and thereby maximize sales in the fashion retail industry for women’s clothing to overcome the struggles regarding gaining sales confronted by the industry. The emergence of big data and machine learning has a positive influence on fashion retailing. ML has been utilized in this research to implement a web application that aids in optimizing sales. It comprehends sales forecasting, customer segmentation, and customer demand analytics. Each research component obtains diverse inputs to initialize the prediction and visualization procedure. The models are built employing the Extra Trees Regressor algorithm, K-means algorithm, and Naïve Bayes algorithm. Finally, for specified inputs, results will be predicted that comprise sales forecasts for products, segmentation of consumers, and forecasts about most demanded fashion item’s characteristics. This paper portrays the proceedings of data preparation, model development, and results of each research component.