Enhancing Personalization of Customer Services in E-Commerce System using Predictive Analytics

The extensive study was conducted to enhance the prediction of customer turnover in an online retail and distribution organization. The study combines data from surveys, consumer comments, and financial records to uncover themes from textual assessments using state-of-the-art methodologies. Methods such as Dirichlet Multilayer Perceptron Mixing, Latent Dirichlet Allocation and Random Sampling fall within this category. In addition to its usage for assessing geographic data for location-based consumer segmentation, DBSCAN is a crucial tool for this investigation. Model development for churn prediction and root cause analysis makes use of logistic regression and extreme gradient boosting. The statistical and practical benefits of the proposed paradigm are shown via comparison to existing options. A model’s predictive efficacy may be evaluated using the area under the curve or the lift metric. The research also introduces the concept of “Consumer-driven energy-efficient WSNs architecture for Personalization and contextualization in E-commerce Systems,” which suggests using wireless sensor networks (WSNs) to collect data efficiently, provide customized service and provide context for online purchases. Overall, the research demonstrates the effectiveness of machine learning in harnessing consumer input for strategic decision-making, illuminating the potential of creative sensor network integration in enhancing e-commerce personalization and contextualization.