Enhancing e-Commerce Strategies: A Deep Learning Framework for Customer Behavior Prediction
Received: 27 May 2024 | Revised: 8 June 2024 | Accepted: 12 June 2024 | Online: 2 August 2024
Corresponding author: Yasser D. Al-Otaibi
Abstract
Today, the use of artificial intelligence (AI) to enhance the processes of online shopping is crucial for e-commerce as it uses the past purchasing behavior of customer-automated processes. Nevertheless, predicting or understanding customers’ buying behavior remains a major challenge. This research work attempts to put forward a new approach by utilizing Deep Learning (DL) models to identify whether a customer will buy or not depending on his age and salary. By employing lightweight dense layers in the DL architecture, the model is trained with the use of publicly available datasets and has great accuracy and performance metrics. This predictive model offers valuable lessons for e-commerce because the recommendation and marketing personalization methods it deploys can be integrated into the business to yield improved experience and performance for customers and users.
Keywords:
artificial intelligence, deep learning, e-commerce applications, customer buying behaviorDownloads
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