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客户忠诚度是客户关系管理研究的重要内容,对企业的经济效益有着巨大的影响。本文运用主成分分析法和支持向量机相结合的方法来预测客户忠诚度。其中通过主成分分析法对原始数据进行降维,消除冗余,从而提高客户忠诚度评价指标的分析精度,然后将得到的主成分作为支持向量机的输入进行建模和预测,企业可以根据结果制定相应的客户保持和发展策略。最后通过实例验证此方法的可行性和有效性。
Customer loyalty is an important part of customer relationship management research and has a huge impact on the economic benefits of enterprises. This paper uses principal component analysis and support vector machines to predict customer loyalty. The principal component analysis method is used to reduce the dimensionality of the original data and eliminate the redundancy so as to improve the analysis accuracy of the customer loyalty evaluation index. Then, the principal component obtained is modeled and predicted as the input of the support vector machine. Based on the result Develop appropriate customer retention and development strategies. Finally, an example is given to verify the feasibility and effectiveness of this method.