AI-Driven Consumer Behaviour Prediction: A Machine Learning Based Application

Résumé fourni par la source

Today, fashion retailers face immense pressure to adapt their marketing strategies to keep pace with changing shopper demands, rapid technological advances, and intense competition in the highly connected digital world. Social media has become a critical channel for brand engagement, allowing merchants unprecedented access to insights into consumer preferences and behaviors. However, contrast this with traditional digital marketing methods, which often do not offer the level of personalization, immediacy, and relevance in context needed by the modern-day consumer. AI in digital marketing has now bridged this gap by permitting fashion companies to automate decision-making using large user databases and generate personalized content strategies. This process focuses on the integration of AI in the field of digital marketing, considering in particular the ability of machine learning models to predict consumer decisions and engagement in the fashion retail sector. Given the real user and product-level data from the fashion marketplaces, this work investigates customer purchase intention, user social interaction, and purchase decision metrics using Random Forest Regressor (RF), Support Vector Regression (SVR), and XGBoost, and the features have been pre-processed through engineering, including integration, missing value handling, categorical encoding, and feature scaling. The performance of the proposed method is evaluated using the MAE, MSE, RMSE, and R2metrics. The proposed ML models were more effective than the baseline models with R2of 99.67%, 30.09%, and 99.94%; MAE of (0.0310, 0.0792, 0.0297); MSE of (0.1673, 0.7708, 0.1389); and RMSE of (0.4090, 0.8780, 0.3727) for RF, SVR, and XGBoost, respectively. ANOVA and the Friedman test provisionally confirmed that the differences in model performance were significant. The results show that XGBoost has the best predictive capacity (R2 = 99.94%), outperforming Random Forest and SVR. The study contributes to the understanding of AI as it relates to CME profiling and efficient use of social media targeting.

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