从客户生命周期角度分析数据挖掘技术的应用

3.2、从客户生命周期角度分析数据挖掘技术的应用

3.2 Analysis of the application of data mining technology from the perspective of the customer lifecycle

在对CRM的广泛理解中，最简单的含义就是：管理所有与客户的交互行为。在实践中，这需要在客户关系的各个阶段使用与客户相关的信息来预测与客户的交互行为。客户关系的各个阶段定义为客户生命周期。客户生命周期包括三个阶段：获得客户；提高客户的价值；保持效益客户。如果将数据挖掘结合在CRM中或者作为一个独立的应用程序来实施，数据挖掘可以在每一个阶段都提高企业的收益（8。

The simplest meaning of the extensive understanding of CRM: manage all interactions with the customer. In practical use, it needs to be used in different stages of the customer relationship and cust omers’ related information to predict interaction with customers. The different stages of the customer relationship are defined as the customer lifecycle. There are three stages of customer life cycle: customer acquisition; customer value improvement; maintaining of beneficial customers. If data mining could be combined in in CRM or applied as a standalone application, it can improve enterprise profits in every phase（8.

（1）通过数据挖掘获取新客户：在CRM中的第一步是识别潜在客户然后将他们转变成真正的客户，数据挖掘可以辅助进行客户细分，识别潜在客户（33。（2）提高客户价值：1、数据挖掘支持客户盈利能力分析，预测客户盈利能力变动以增强客户盈利能力；2、支持客户购买行为模式分析，进行客户细分，从而提供更具针对性的个性化服务（1；3、有效进行交叉营销，向现有的客户提供新的产品和服务，实现购买推荐和升级销售（18。

（3）客户保持：包括客户忠诚度分析和客户流失警示分析。通过数据挖掘，对客户历史交易行为的分析，警示客户异常行为，并提出相应的对策建议。

(1) Acquire new customers through data mining: the first step in CRM is to identify potential customers and then develop them into true customers, and data mining is able to assist customer segmentation, identifying potential customers (33.

(2) Improve customer value: 1. data mining support customer profitability analysis, predicting customer’s profitability changes to enhance his profitability; 2. support customer purchasing behavior pattern analysis to make customer segmentation, thus, higher personalized service could be provided (1; 3. making effective cross-selling, providing new products and services to existing customers, realizing purchasing recommend and upgraded sales (18.

(3) Customer retention: including customer loyalty analysis and customer churn warning analysis. Data mining could analyze the customer historical trade, warning the analysis of historical customer transactions, and putting forward corresponding countermeasures and suggestions.

3.3、从行业角度分析数据挖掘技术的应用

3.3 Analyzing the application of data mining technology from professinal perspective CRM中数据挖掘应用的深度和广度针对行业的不同而有所不同，特别是针对与客户交流频繁、客户支持要求高的行业，如银行、证券、保险、电信、税务、零

售、旅游、航空、医疗保健等（10。下面例举零售业CRM数据挖掘的应用。The application of data mining in CRM varies according to the depth and breadth in view of the industry, especially for frequent communication with customers and customer support demanding industry, such as banking, securities, insurance, telecommunications, tax, retail, tourism, aviation, health care, etc. (10 The examples of CRM data mining application in retail industry are as follows.

零售业CRM中的数据挖掘：零售业CRM是数据挖掘的主要应用领域，特别是由于日益增长的Web或电子商务方式的兴起(9。零售数据挖掘可有助于识别客户购买行为，发现客户购买模式和趋势，改进服务质量，取得更好的客户保持力和满意度，提高货品销量比率，设计更好的货品运输与分销策略，减少商业成本(30。例如：1、使用多特征数据立方体进行销售、客户、产品、时间和地区的多维分析；2、使用多维分析和关联分析进行促销活动的有效性分析；3、序列模式挖掘可用于客户忠诚分析；4、利用关联分析挖掘关联信息进行购买推荐和商品参照（20。5、分类和聚类的方法可用于客户群体的识别和目标市场的分析（6；Retail data mining in CRM: retailing CRM is the main application field of data mining, especially because of the growing Web or the rise of e-commerce mode (9. Retail data mining can help to identify the customer purchasing behavior, finding the customer purchase patterns and trends, improving the service quality and achieving better customer retention and satisfaction, improving product sales rate, designing better goods transportation and distribution strategy, reducing business costs (30. For example: 1. employing multiple feature data cube of multidimensional analysis of sales, customers, products, time and region; 2. the use of multidimensional analysis and correlation analysis of the effectiveness of sales promotion; 3. the sequence pattern mining can be used in the analysis of customer loyalty; 4. using correlation analysis to mine association information to buy recommends and product reference (20. 5. classification and clustering methods can be used for customer group identification and the analysis of the target market (6;

4、中数据挖掘应用研究的发展方向

数据挖掘技术在CRM中的应用研究是当前的研究热点。目前CRM中进行有效的数据挖掘面临的主要技术问题包括：（1）挖掘方法和用户交互问题：1、挖掘的知识类型（21；2、多个抽象层的交互知识挖掘（27；3、领域知识的使用（23；

4、数据挖掘查询语言（12；4、数据挖掘结果的表示和可视化（19；

5、处理噪声和不完全数据；（2）关于数据库类型的多样性问题：1、关系的和复杂的数据类型的处理（9；2、由异种数据库（19和全球信息系统挖掘信息如Web挖掘（3。其他问题包括数据挖掘的应用开发和它们的社会影响。这些问题是数据挖掘技术未来发展的主要需求，同时给数据挖掘也提出了许多挑战性的课题。

数据挖掘技术在CRM中的应用研究方向建议应主要面向如下方面：

（1）应用目标的转变：随着企业的战略目标的转变，CRM中的数据挖掘的应用目标重点应从增加企业收入转移到节约企业成本（16；

（2）应用的对象：从企业规模来看，中小型企业是企业类型中的主体，针对中小型企业的CRM应用相应的数据挖掘技术提升决策支持的智能化水平对中国企