如何利用人工智能(AI)管理客户终身价值(CLV)--系统性文献综述

Edo Belva Firmansyah , Marcos R. Machado , João Luiz Rebelo Moreira
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引用次数: 0

摘要

客户终身价值(CLV)代表了客户在一段时间内对公司的总价值,有助于企业进行资源分配和量身定制的盈利营销。本文献综述通过研究如何将客户风险因素纳入 CLV 计算,填补了研究空白。我们在数据库中进行了系统的文献综述,严格遵守相关性和质量标准。综述分析了客户价值计算方法和结果,强调了使用均值-方差分析来优化客户组合,并将客户收入波动确定为主要风险因素。研究还探讨了 CLV 研究的演变,特别是机器学习 (ML) 在风险调整 CLV 中的应用。我们的研究结果提供了一个全面的概述,为未来的研究奠定了基础,有助于企业完善风险管理策略,识别高风险客户,并通过更动态的数据驱动模型提升客户价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
How can Artificial Intelligence (AI) be used to manage Customer Lifetime Value (CLV)—A systematic literature review

Customer Lifetime Value (CLV) represents the total worth of a customer to a company over time, aiding businesses in resource allocation and tailored marketing for profitability. This literature review fills a research gap by examining how customer risk factors are integrated into CLV calculations. We conducted a systematic literature review across databases, adhering to strict criteria for relevance and quality. The review analyzed CLV methodologies and outcomes, highlighting the use of mean–variance analysis to optimize customer portfolios, with customer income fluctuations identified as a major risk factor. The study also explores the evolution of CLV research, particularly in the application of Machine Learning (ML) for risk-adjusted CLV. Our findings offer a comprehensive overview, laying the groundwork for future research and helping businesses refine risk management strategies, identify high-risk customers, and enhance customer value through more dynamic, data-driven models.

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