现在，您已经定义了功能并创建了最终表，唯一剩下的步骤是建立实际客户细分模型。
Now that you have defined the features and created the final table, the only step remaining is to build the actual customer segmentation model.
现代客户细分模型与传统客户分类的区别主要集中在不同的细分变量以及采用的方法。
The main difference between modern customer segmentation model and traditional customer classified method has concentrated on different segmentation variables and on ways.
客户细分模型是指选择一定的细分变量，按照一定的划分标准对客户进行分类的方法。
Customer segmentation model refers to a model that classifying customers by selected segmentation variables, on certain standard.
本文把数据挖掘技术应用于基于客户价值矩阵的客户价值细分中，建立各类价值客户的分类模型。
This thesis applies data mining techniques to customer segmentation based on customer value matrix and builds the classification model of customer with different value.
国外已有一些关于消费者行为应用于客户细分方面的研究，如RFM细分模型、客户价值矩阵模型等。
There has been some research abroad on consumer behavior applied in the field of consumer segmentation, like RFM model, Customer Value Matrix (CVM) model, etc.
CIAS的商业逻辑层包括交叉销售、客户响应、客户细分、客户流失、客户利润，五个商业模型。
The business rules level of CIAS consists of five kinds of business models: cross selling, customer responsibility, customer segmentation, customer churn, customer profitability.
然后详细分析了信息技术的影响机理，并提出基于信息技术与客户驱动的供应链重构模型。
Then it describes the application of IT in SCM in detail. Finally, a model of supply chain reengineering is established driven by customers and the development of IT.
本文对电信产品生命周期管理模型进行了分析，并且运用数据挖掘的相关知识着重对模型中的客户细分和产品预演进行了研究。
The paper analyzes the model of TPLC management, and using the correlative knowledge of Data Mining researches emphatically the customer segmentation and the product forecast in the TPLC.
基于该模型进行客户细分，探讨了如何采取不同的客户战略；最后通过一个实际案例证明了客户潜在价值预测模型的有效性。
Based on the model, we propose customer segmentation and different market strategies on various customers, and finally prove the validity of multivariate profit model with practical example.
将关键属性作为SOM神经模型的输入，提高客户细分质量。
Key attributes are used as inputs for learning by SOM neural network so as to obtain better clustering quality.
将关键属性作为SOM神经模型的输入，提高客户细分质量。
Key attributes are used as inputs for learning by SOM neural network so as to obtain better clustering quality.
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