摘要
针对CRM客户分类,提出模拟退火算法与K均值算法相结合的聚类算法。利用模拟退火算法全局寻优能力改变k均值算法易陷入局部极值的缺点。经标准数据集检验,证明算法有效。根据烟草商业企业业务数据和卷烟营销特点分析,设计客户分类评价指标模型。将算法应用于烟草商业企业CRM客户分类,分类结果符合卷烟营销特点,从实用角度验证算法有效。根据客户分类设计了差异化CRM营销策略。
Propose a simulated annealing K-means (SAKM) algorithm for CRM customer clustering, which use the global optimize a- bility of SA to remedy the local extremum shortcoming of KM. The standard data computational results indicate that it is better than the K-means algorithm. A clustering attributes model had been designed which come from the analyzing of tobacco commercial enter- prise' sale data and characteristic. Testify the SAKM in tobacco commercial enterprise CRM customer clustering, the result validate SAKM is in effect, and then propose a personal CRM strategy.
出处
《微计算机信息》
北大核心
2008年第21期182-184,共3页
Control & Automation