AI Algorithms for Personalization: Recommender Systems, Predictive Analytics, and Beyond
Keywords:
AI personalization, e-commerce, customer experience, recommendation systems, data analytics, machine learning, customer behaviour metrics, qualitative researchAbstract
Aim: This study is intended to engage in the in-depth study of AI-enabled personalization tactics on the quality of customer experience as competition informs the e-commerce environment. The research employs a case study assessment of a prominent world-wide retailer with the primary aim of revealing the dominant influence of cutting-edge AI personalisation technology in actual applications.
Methods: The study applied the mixed-methods research, which was made up of quantitative as well as qualitative research techniques such as sound data analysis and field research approaches to arrive at a comprehensive apprehension of the phenomenon. Data science system features such as studying key customer behaviour metrics, conversions, average order, customer value, and satisfaction, appreciate the company's case from superior data systems. The qualitative side of this study was indicated through the revelation of in-depth interviews that were done with a group of educated customers and an extensive online survey that was designed to capture their preferences, opinions, and perceptions in relation to the personalized shopping experience(Gao & Liu, 2022b).
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