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Research on customer lifetime value based on machine learning algorithms and customer relationship management

Yuechi Sun1, Haiyan Liu1, Yu Gao1

  • 1School of Economics and Management, China University of Geosciences (Beijing), No. 29 Xueyuan Road, Haidian District, Beijing 100083, China.

Heliyon
|February 28, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for calculating customer lifetime value (CLV) and segmenting customers, even without contracts. It enhances customer relationship management by accurately identifying valuable customers using machine learning.

Keywords:
Customer lifetime valueCustomer segmentationData miningMachine learning

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Area of Science:

  • Business Analytics
  • Machine Learning
  • Customer Relationship Management

Background:

  • Accurately measuring Customer Lifetime Value (CLV) is crucial for businesses, especially in non-contractual relationships.
  • Traditional data mining methods face limitations in effectively calculating CLV and segmenting customers in these scenarios.
  • Customer lifecycle value theory provides a framework for understanding and managing customer value over time.

Purpose of the Study:

  • To develop a robust model for customer value measurement and segmentation in non-contractual settings.
  • To address the research difficulty in CLV calculation due to the limitations of single data mining techniques.
  • To construct a customer segmentation model based on customer value and lifecycle theory.

Main Methods:

  • Feature engineering, including data selection, preprocessing, transformation, and knowledge discovery.
  • Application of machine learning algorithms and customer relationship management (CRM) analysis models for segmentation.
  • Development of a customer value segmentation identification model tailored for non-contractual relationships.

Main Results:

  • Empirical validation using real customer transaction data from an online shopping platform.
  • Demonstration of the validity and applicability of the proposed customer segmentation and value calculation methods.
  • Successful construction of a customer segmentation model that overcomes limitations of previous approaches.

Conclusions:

  • The proposed method effectively measures customer value and segments customers in non-contractual environments.
  • The integrated approach of machine learning and CRM analysis enhances customer relationship management.
  • This research provides a practical solution for businesses to better understand and leverage customer lifetime value.