基于数据挖掘技术的客户关系管理应用研究

本文先分析了客户关系管理定义和功能、接着分析了数据挖掘的流程,最后浅析了数据挖掘技术在企业客户关系管理中的具体应用。     

客户关系管理中基于数据挖掘的客户细分研究

客户关系管理(CRM)是适应企业“以产品为中心”到“以客户为中心”的经营模式的战略转移和关系营销的需要而发展起来的新的管理理念,它把在保证企业利益的前提下追求客户满意和客户忠诚作为最终目标。客户细分是客户关系管理系统的核心功能之一,可以对客户获取、客户保持及客户增值等客户关系管理过程提供全面支持,提升客户满意和客户忠诚。     

基于数据挖掘技术的企业客户关系管理(CRM)

由于竞争的全球化、需求的拉动和管理理念的更新,使客户关系管理(CRM)得到产生和发展。面对企业海量的数据,如何从其中发现有价值的知识和规律是企业急需解决的难题。数据挖掘技术为此提供了工具和途径。在了解CRM的概念和框架、数据挖掘的各种技术后,还必须了解数据挖掘在CRM中的应用流程和应用的业务领域。     

关系营销是客户关系管理的核心理念和指导思想

关系营销是客户关系管理的核心理念和指导思想,客户关系管理的核心是客户关系。通过客户关系管理这一过程,企业最大程度地掌握和利用顾客信息,以培养和增强顾客的忠诚度,实现顾客的终身挽留。关系营销与客户关系管理进行有效整合,可以使关系营销理论得到全新发展,也让客户关系管理理论在中国的环境中得到更好的发挥和实现。     

数据挖掘技术在物流企业管理的应用研究

本文针对物流领域的实际业务流程、业务需要以及实际拥有的数据进行了研究,论述了物流企业管理中数据挖掘的目标、算法和操作。阐述了数据挖掘技术及其在物流领域管理中的应用。     

Partitioning for Profit: An Empirical Study of Methods for Handling Unequal Costs of Error in Predictive Data Mining

This paper expands prior work on the Sequential Binary Programming (SBP) algorithm as a framework for cost-sensitive classification. The field of cost-sensitive learning has provided a number of methods to adapt predictive data mining from engineering and hard science applications to those in commerce. This discussion will test theoretical limitations of classical cost-sensitive algorithms empirically and outline the appropriate conditions under which various methods (specifically SBP) should be implemented in favor of others.     

Click Here for a Data Scientist: Big Data,Predictive Analytics,and Theory Development in the Era of a Maker Movement Supply Chain

Predictive analytics is impacting many diverse areas, ranging from baseball and epidemiology to forecasting and customer relationship management. Manufacturers, retailers, software companies, and consultants are creatively discovering new applications of big data using predictive analytics in supply chain management and logistics. In practice, predictive analytics is generally atheoretical; however, we develop a 2 × 2 model to explain the role of predictive analytics in the theory development process. This 2 × 2 model shows that in our discipline we have traditionally taken one path to theory development, but that predictive analytics can be a salient component of a comprehensive theory development process. The model points to a number of research questions that need to be addressed by our research community. These questions are not just highly relevant to the academic community but also in urgent need of answers to help practitioners execute the right strategies with greater precision and efficiency. We also discuss how one disruptive trend, the maker movement, changes the nature of who the producers are in the supply chain, making big data even more valuable. As we engage in higher levels of dialogue we will be able to make meaningful progress addressing these vital research topics.