The impact of Artificial Intelligence on E-commerce Customer Experience and Personalization

Abstract

Businesses aim to provide clients with personalized recommendations in today's cutthroat market environment in order to boost engagement, retention, and income. Businesses are looking for creative ways to customize their marketing campaigns and content to each customer's tastes as a result of the development of artificial intelligence (AI). Machine learning (ML) models can forecast personalized recommendations for goods, services, and content by examining consumer behavior and interests. The objective of present investigation is to investigate how ML methods can be used to forecast e-commerce product on-time delivery, annual spending, and customer attrition. The research has demonstrated that a range of ML techniques, including as KNN, Navie Bayes, and gradient boosting, have been successfully used to forecast customer churn across many industries. The study used ML techniques to empirically analyses customer turnover in e-commerce in order to fill this vacuum in the literature. The results of the study show that ML methods are useful for forecasting customer attrition and product delivery in e-commerce. The top-performing algorithm, gradient boosting, predicted customer attrition with an accuracy of 90.67% and product on-time delivery with an accuracy of 73.75%.

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