Strategies for Enhancing Customer Lifetime Value through Data Modeling

Zhuoer Ma

Authors

Zhuoer Ma Acorns, Analytics, Irvine, California, 92617, United States Author

Keywords:

customer lifetime value (CLV), data modeling, personalized marketing

Abstract

With the increasingly fierce social competition, customer lifetime value (CLV) is recognized as an important indicator to measure customer relationship and long-term value of enterprises. Through the method of improving CLV to maximize customer value and promote the sustainable development of enterprises. With the rapid development of big data technology, data modeling has become one of the best means to improve CLV. With data modeling as the core, this paper analyzes the means to improve the customer lifetime value by using accurate customer prediction, personalized marketing, loss prediction and other methods. This paper reviews the theoretical basis of customer lifetime value (CLV) and how to use data modeling to improve customer prediction accuracy and behavior analysis. This paper discusses the practical application of data modeling in customer segmentation, dynamic pricing, personalized recommendation and so on. This paper provides some guidance and reference methods for enterprises to use data modeling to enhance customer lifetime value.

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