Year: 2024
Type: article
Abstract: Retailers frequently need help in delivering personalized marketing experiences due to fragmented customer data and the lack of real-time insights. Personalization significantly enhances customer engagement and drives conversions, thereby maintaining a competitive edge. This paper discusses the application of deep learning algorithms to analyze customer behavior and preferences, facilitating the creation of tailored marketing campaigns. By integrating these insights into the eCommerce platform, personalized promotions and product recommendations can be delivered in real-time. The methodology includes data collection and preprocessing, deep learning model development using convolutional neural networks (CNNs) and recurrent neural networks (RNNs), and integration with eCommerce platforms. The results demonstrate a significant improvement in customer engagement, click-through rates, and conversion rates due to real-time personalization. However, challenges such as the need for large data sets, computational resources, and privacy concerns must be addressed. Future research should focus on developing more efficient algorithms and ethical data practices. This study underscores the potential of deep learning to revolutionize personalized marketing in retail and eCommerce.
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4401539989