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THE EMPIRICAL STUDY OF APPLYING NEURO TO ESCALATE MARKETING PERFORMANCE

机译:应用NEURO评估营销绩效的实证研究

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We develop a F-measure performance evaluation system by combining with RFM criteria and Bayesian classification, Decision Tree Induction, Gini Index, Neuro and by analyzing the factors with recall related and precision.By using this system, more profitable customers can be discovered and less unprofitable customers will be missed. The research collects and analyzes references on promoting buying rate; moreover, it introduces R-F-M (recency, frequency, monetary amount) criteria, brings up the idea of identify each individual customer to promote both the marketing profit and the customer's lifetime value.The result shows that marketing performance derive from Neuro-weighted RFM model has the advantage over traditional RFM model by 27.80%.
机译:我们结合RFM标准和贝叶斯分类,决策树归纳,基尼指数,神经网络,并分析与召回相关和精确度的因素,开发了F衡量绩效评估系统,通过使用该系统,可以发现更多盈利客户,而更少无利可图的客户将被错过。该研究收集并分析了有关提高购买率的参考资料;此外,它引入了RFM(汇率,频率,金额)标准,提出了识别每个客户的想法,以提高营销利润和客户的生命周期价值。结果表明,神经加权RFM模型得出的营销绩效具有与传统RFM模型相比,优势提高了27.80%。

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