Enhancing Customer Lifetime Value Using Data Science and Predictive Modeling
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Abstract
Customer lifetime value is considered one of the major performance measurements for companies targeting the highest achievable retention and profit levels. It involves the usage of data science and predictive models in maximizing the customer lifetime value through more significant insights into a customer's behavior, needs, and future values. Utilizing more sophisticated statistical techniques, such as machine learning, regression analysis, and segmentation, organizations can enhance their CLV prediction and identify valuable customers sooner. To this extent, organizations can subsequently implement the models in CRM applications that facilitate targeted marketing campaigns, resource allocation, and personalized offerings to realize greater customer satisfaction and loyalty. These methods, in the article, are reported to be exposed to organizational pitfalls, for instance, data quality, model complexity, and ongoing model refinement. Finally, it offers a strategic data science and predictive analytics platform for CLV maximization, sustainable growth, and firm performance.
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