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Customer loyalty prediction in multimedia Service Provider Company with K-Means segmentation and C4.5 algorithm

机译:利用K-Means分割和C4.5算法的多媒体服务提供商公司中的客户忠诚度预测

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摘要

The development needs the Internet and cable television entertainment increase per year that affect popping up various multimedia service provider company which is offered a lot of services to win the market. This makes customer has a lot of company choices and makes customer to be more demanded and move easily from a provider to other provider, where company knows that keep customer has the cost that is lower than the cost to get new customer. So, it's important for company to know customer loyalty and company can also project the income as reference in company development planning. Company needs to has accurate model, so researcher uses k-means segmentation and C4.5 classification algorithm, which can be seen that the model has accuracy 79.33% and Area Under Curve (AUC) 0.831. This research contribution is the use of related data using customer potential segmentation based on Recency Frequency Monetary (RFM) model, so can increase accuracy percentage in customer loyalty classification research.
机译:发展需要每年互联网和有线电视娱乐增加,这影响到弹出提供各种服务来赢得市场的各种多媒体服务提供商公司。这使客户有很多公司选择,并使客户的要求更高,并且可以轻松地从提供商转移到其他提供商,在这种情况下,公司知道保留客户的成本低于获得新客户的成本。因此,了解公司的客户忠诚度对公司很重要,公司也可以将收入计划为公司发展规划的参考。公司需要准确的模型,因此研究人员使用k均值分割和C4.5分类算法,可以看出该模型具有79.33%的准确度和0.831的曲线下面积(AUC)。这项研究的贡献是利用基于最近度频率货币(RFM)模型的客户潜力细分来使用相关数据,因此可以提高客户忠诚度分类研究中的准确性百分比。

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