Transforming E-Commerce with Pragmatic Advertising Using Machine Learning Techniques

Sankara Reddy Thamma

doi:10.32628/CSEIT25111248

Authors

Sankara Reddy Thamma Deloitte Consulting LLP, USA Author

DOI:

https://doi.org/10.32628/CSEIT25111248

Keywords:

E-commerce, Machine Learning, Pragmatic Advertising, Personalized Advertising, Consumer Behavior, Recommendation Systems, Natural Language Processing, Supervised Learning, Unsupervised Learning, Ad Targeting

Abstract

Today e-commerce has had tremendous growth in the past years primarily due to changes in technology and customer’s buying behavior. One of the big shifts in the process has been the use of ML in advertising which has the capability to transform the marketing domain together with consumer interactions. This paper discusses the viability of using machine learning for designing realistic models of advertising to increase effectiveness of target and personalized advertising, as well as conversion rates in e-commerce. Several techniques are explored in the study, such as supervised and unsupervised learning, recommender systems, and content optimization with natural language processing. By using case studies and experimental models, we discuss how and to what extent ML is beneficial in e-commerce advertising transformation. The results presented in this paper indicate that using machine learning in advertising has a potential of dramatically improving the customer experience while simultaneously increasing brand recognition and sales.

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