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Research on customer lifetime value based on machine learning algorithms and customer relationship management analysis model.
Sun, Yuechi; Liu, Haiyan; Gao, Yu.
Afiliación
  • Sun Y; School of Economics and Management, China University of Geosciences (Beijing), No. 29 Xueyuan Road, Haidian District, Beijing 100083, China.
  • Liu H; School of Economics and Management, China University of Geosciences (Beijing), No. 29 Xueyuan Road, Haidian District, Beijing 100083, China.
  • Gao Y; School of Economics and Management, China University of Geosciences (Beijing), No. 29 Xueyuan Road, Haidian District, Beijing 100083, China.
Heliyon ; 9(2): e13384, 2023 Feb.
Article en En | MEDLINE | ID: mdl-36852044
Customer lifetime value is one of the most important tasks for enterprises to maintain customer relationships. However, due to the limitations of using a single data mining method, the measurement of customer lifetime value under the condition of noncontractual relationship has always been a research difficulty. This paper focuses on customer value measurement and customer segmentation based on customer lifecycle value theory, and carries out customer value measurement and customer segmentation research from the perspective of customer value, and constructs customer segmentation model. This paper first conducts feature engineering, such as data selection, data preprocessing, data transformation, and knowledge discovery, and then conducts customer value segmentation based on machine learning algorithms and customer relationship management analysis models and builds a customer value segmentation identification model under the condition of noncontractual relationship. Finally, empirical analysis is carried out with the real customer transaction data of the actual online shopping platform, which verifies the validity and applicability of the customer segmentation method and value calculation method proposed in this paper.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Heliyon Año: 2023 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Heliyon Año: 2023 Tipo del documento: Article País de afiliación: China