Predictive Analytics in E-Commerce for CustomerBehavior Forecasting using hybrid Ret-DNN withXGBoost Model
arXiv:2606.17931v1 Announce Type: new Abstract: In recent years, electronic (E) commerce services have rapidly increased in the daily lives of people, which helpsthem to purchase products online. However, retail platforms have struggled to understand customer behavior and make it difficult to predict their future purchases. To overcome these challenges, this study proposes a hybrid Retail Deep NeuralNetwork (Ret-DNN) with an Extreme Gradient Boosting(XGBoost) model for capturing temporal features and tabular dynamics of retail data. First, data were sourced from a UnitedKingdom (UK)-based onli
The increased volume and complexity of e-commerce data, coupled with advancements in AI models, necessitate more sophisticated predictive analytics for competitive advantage.
Improved customer behavior forecasting allows e-commerce platforms to optimize inventory, personalize marketing, and enhance user experience, directly impacting revenue and market share.
Retailers can move beyond basic analytics to proactively understand and predict individual customer purchasing patterns with higher accuracy through advanced hybrid AI models.
- · E-commerce platforms
- · Retail analytics firms
- · AI model developers
- · Consumers (through personalized experiences)
- · Traditional retail (without advanced analytics)
- · Companies with poor data infrastructure
Retailers will gain a significant competitive edge through superior demand forecasting and customer engagement.
This could lead to a consolidation in the e-commerce market as smaller players struggle to compete with AI-powered giants.
The development of 'predictive customer agents' could emerge, fully automating personalized retail experiences and potentially further reducing human interaction.
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Read at arXiv cs.LG