Authors :

Omar Al-Hassan and Yasmin Al-Nuaimi

Address :

Department of Computer Networks, American University in Dubai, UAE

Department of Artificial Intelligence, Zayed University,UAE

Abstract :

E-commerce platforms collect massive amounts of data, including product images, user interactions and purchase activity. These are usually underutilized in marketing decision-making processes. The fast rise in e-commerce has brought further challenges in optimising marketing strategies toward customer attraction, interaction, and retention. Inefficient data-driven strategies have caused suboptimal efforts in marketing, thus resulting in lower satisfaction among customers and reduced conversion rates. Conventional marketing techniques can rarely take advantage of massive data generated by e-commerce, resulting in low efficiency in customer targeting and campaign effectiveness. This paper presents a novel method called ECMS-CNN that applies a Convolutional Neural Network for optimizing E-Commerce Marketing Strategies focused on three key areas: product image analysis, personalized recommendations, and dynamic ad targeting. The CNN model would be trained on large datasets such as Amazon Product Reviews to identify patterns in product images, customer preference, and purchase behavior. Further, the model will be integrated into a marketing pipeline to automate product tagging, personalized recommendations, and real-time ad targeting, all supported by visual and behavioral data. The application of CNN significantly enhances marketing strategies for effectiveness and efficiency. These experiments demonstrate the accuracy of personalized product recommendations increased by up to 20%, and ad targeting became more efficient by 25%. Additionally, product image analysis with the application of CNNs enabled simplification in the tagging process by taking 30% less manual effort and providing more relevant suggestions to the customers. These results demonstrate the role of CNNs in increasing conversion rates, Mean Absolute Error, and Click-Through Rate on e-commerce platforms.

Keywords :

Convolutional Neural Networks, E-commerce, Marketing Strategy, Personalization, Ad Targeting, Image Analysis, Product Recommendations.