Leveraging Digital Transformation and Social Media Analytics for Optimizing Us Fashion Brands' Performance: a Machine Learning Approach
Year: 2024
Type: article
Abstract: This study explores how machine learning algorithms can optimize the performance of US fashion brands by analyzing the relationship between digital transformation, social media analytics, and customer engagement. Using a Kaggle dataset, models including linear regression, random forest, gradient boosting, and neural networks were evaluated to predict brand performance. Neural networks achieved the highest accuracy (R-squared: 0.92), while gradient boosting balanced performance and interpretability (R-squared: 0.88). Results highlight the critical role of customer engagement in driving brand success and demonstrate the potential of machine learning for actionable insights. This research provides a robust framework for data-driven strategies in the fashion industry.
Authors: Md Risalat Hossain Ontor, Asif Iqbal, Emon Ahmed, Tanvirahmedshuvo ,, Ashequr Rahman
Institutions: International American University, Westcliff University
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4404832881