This paper explores the application of machine learning techniques in modeling customer lifetime value (CLV) within the context of modern business practices. The study aims to provide a comprehensive overview of the methodologies and algorithms that can be utilized to predict the long-term value of customers to a company. By integrating historical data, customer behaviors, and market trends, the proposed model leverages advanced machine learning algorithms to forecast CLV more accurately. The paper discusses the challenges and opportunities in implementing such models, including the need for quality data, algorithm selection, and ethical considerations. The results indicate that the integration of machine learning methods in CLV modeling can lead to improved customer relationship management, strategic decision-making, and increased profitability for businesses.
Jackson, E. (2020). Customer Lifetime Value Modeling Using Machine Learning. Management Analytics and Decision, 2(1), 6. doi:10.69610/j.mad.20200212
ACS Style
Jackson, E. Customer Lifetime Value Modeling Using Machine Learning. Management Analytics and Decision, 2020, 2, 6. doi:10.69610/j.mad.20200212
AMA Style
Jackson E. Customer Lifetime Value Modeling Using Machine Learning. Management Analytics and Decision; 2020, 2(1):6. doi:10.69610/j.mad.20200212
Chicago/Turabian Style
Jackson, Emma 2020. "Customer Lifetime Value Modeling Using Machine Learning" Management Analytics and Decision 2, no.1:6. doi:10.69610/j.mad.20200212
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ACS Style
Jackson, E. Customer Lifetime Value Modeling Using Machine Learning. Management Analytics and Decision, 2020, 2, 6. doi:10.69610/j.mad.20200212
AMA Style
Jackson E. Customer Lifetime Value Modeling Using Machine Learning. Management Analytics and Decision; 2020, 2(1):6. doi:10.69610/j.mad.20200212
Chicago/Turabian Style
Jackson, Emma 2020. "Customer Lifetime Value Modeling Using Machine Learning" Management Analytics and Decision 2, no.1:6. doi:10.69610/j.mad.20200212
APA style
Jackson, E. (2020). Customer Lifetime Value Modeling Using Machine Learning. Management Analytics and Decision, 2(1), 6. doi:10.69610/j.mad.20200212
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References
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