CUSTOMER BEHAVIOR ANALYSIS USING BIG DATA ANALYTICS AND MACHINE LEARNING

L. Muradkhanli, Zaman Karimov
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Abstract

This paper delves into the utilization of big data analytics and Machine Learning (ML) in the realm of customer behavior analysis for digital marketing. It explores the practical application of ML algorithms and the ML pipeline in the development of predictive models. The primary objectives revolve around forecasting customer churn, identifying prospects with a high propensity to convert, determining optimal communication channels, and leveraging sentiment analysis to enhance the overall customer experience. Concrete real-world examples and compelling case studies are employed to illustrate the efficacy of ML in analyzing customer behavior. Moreover, the paper acknowledges the existing limitations and challenges in this domain, while also outlining potential directions for future research. By offering a comprehensive guide, the aim is to empower businesses with the knowledge and tools needed to effectively leverage big data analytics and ML for customer behavior analysis in the digital marketing landscape. The paper concludes by addressing limitations, challenges, and future research directions in this field, aiming to provide a comprehensive guide to leveraging big data analytics and ML for customer behavior analysis.
使用大数据分析和机器学习进行客户行为分析
本文深入探讨了大数据分析和机器学习(ML)在数字营销客户行为分析领域的应用。它探讨了机器学习算法和机器学习管道在预测模型开发中的实际应用。主要目标围绕着预测客户流失,识别具有高转换倾向的潜在客户,确定最佳沟通渠道,并利用情感分析来增强整体客户体验。具体的现实世界的例子和引人注目的案例研究被用来说明机器学习在分析客户行为方面的功效。此外,本文承认该领域存在的局限性和挑战,同时也概述了未来研究的潜在方向。通过提供全面的指南,目的是为企业提供有效利用大数据分析和机器学习进行数字营销领域客户行为分析所需的知识和工具。本文总结了该领域的局限性、挑战和未来的研究方向,旨在为利用大数据分析和机器学习进行客户行为分析提供全面的指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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