Data-driven personalized marketing: deep learning in retail and E-commerce
1 Darden School of Business, University of Virginia, Charlottesville, VA, USA.
2 The Ohio State University, Fisher College of Business, Columbus, OH, USA.
3 Jack Welch College of Business, Sacred Heart University, Fairfield, CT, USA.
4 Sahara Group, Lagos, Nigeria.
5 Fisher College of Business, The Ohio State University, Ohio, USA.
6 Booth School of Business, University of Chicago, IL, USA.
7 Quinnipiac University, Hamden, CT, USA.
Review Article
World Journal of Advanced Research and Reviews, 2024, 23(02), 788–796
Publication history:
Received on 25 June 2024; revised on 06 August 2024; accepted on 08 August 2024
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.
Keywords:
Personalized Marketing; Deep Learning; Retail; E-commerce; Customer Engagement; Real-time Insights
Full text article in PDF:
Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0