Customer Value Analysis Based on Rough Set Data Mining Technology

Author: Wang Hong
Publisher:
Publish Date: 2006-09-01
Features: This book studies data mining methods based on classification patterns, particularly the extraction of classification rules based on rough set theory and the construction of decision trees with their classification rule extraction strategies. It proposes an improved algorithm for a rough set-based decision tree model. In customer value management, the rough set-based decision tree method is applied by establishing a model based on a series of feasible indicator systems to measure customer value and segment customers. Based on data mining, it effectively identifies customers, evaluates customer value, segments customers, improves sales performance, retains customers, and enhances customer satisfaction and loyalty. It provides intelligent decision support for enterprise customer management. Starting from the fundamental theory of customer value, this book employs data mining techniques based on rough sets and constructs models using the rough set-based decision tree method to conduct research on customer value. The introduction primarily introduces the research objectives and significance, the current status and development trends of related theories at home and abroad, research methods, and innovations. Chapter 1 serves as the foundational part of the book, introducing relevant theories. Chapter 2 conducts theoretical research on customer value, elaborating on the customer life cycle. Chapters 3, 4, and 5 respectively study classification data mining techniques, data mining techniques based on rough sets, and the rough set-based decision tree method. Chapter 6 presents an empirical analysis of the application of the rough set-based decision tree technique in customer value research. Chapter 7 introduces methods to enhance customer value and strengthen the competitive advantage of enterprises.