共查询到19条相似文献，搜索用时 187 毫秒
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电子商务客户流失三阶段预测模型 总被引：5，自引：0，他引：5
采用某网上商场的2525名客户样本,构建了基于SMC和最小二乘支持向量机(LSSVM)的电子商务客户流失三阶段预测模型.首先应用SMC模型计算出客户活跃度,以0.5为阚值判断出客户流失状态,识别出正判客户和错判客户;其次将训练样本送入LSSVM进行训练和学习,进而对测试样本的客户流失状态进行判别,然后将误判客户样本输入最近邻分类器进行再判断.结果表明,与SMC模型、BP神经网络模型、LSSVM模型相比,三阶段模型对测试样本预测精度更高,是一种更有效和实用的分类方法,可为电子商务企业客户关系管理提供一个新的方法. 相似文献
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本文分别建立了四川省工业利润总额方面的GMDH自回归预测模型和AC预测模型,然后针对它们的预测效果建立了GMDH-AC组合预测模型。并将组合预测结果与实际值以及单一的GMDH模型、AC模型的预测效果进行了分析和比较,表明了所述方法的有效性和可行性,从而为预测工业利润总额及其它宏观经济预测指标在选择预测方法提供了参考。 相似文献
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分析了通信企业客户流失现状,给出选择CART算法的原因,然后利用CART算法建立客户流失预测模型并以移动通信企业为例,对其客户流失情况进行预测,把预测结果反馈给相关部门,从而制定出有效的营销措施预防客户流失。 相似文献
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基于客户价值的信息用户流失预测研究 总被引：1，自引：0，他引：1
对信息用户流失分析中的相关问题展开了研究,提出基于客户价值的流失预测模型。结果表明在客户价值细分后进行流失预测,可以提高预测精度并深刻地了解用户特征,从而更有针对性地开展用户保持工作。 相似文献
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组合方法首先选取支持向量机预测算法和一阶指数平滑法对经济时间序列分别进行预测,来建立模糊自适应变权重组合预测模型。为对比模糊自适应变权重的经济时间序列组合预测模型的预测效果,选取了两种定值加权组合预测模型:平均加权模型、误差平方和最小组合预测模型。通过实验比较分析:模糊自适应变权重组合预测可以综合利用各单项预测方法的优点,比单一模型预测结果精度有了很大提高,且优于定值加权组合预测,在经济时间序列的预测方面有较高的应用价值。 相似文献
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组合预测能够充分利用已知信息,从而提高预测精度.通过建立预测模型与预测对象的关系数据模型,利用离散化属性数据值来建立知识表达系统和决策表,并依据粗糙集理论计算组合预测模型中各单一模型的权系数,将该方法运用到我国R&D经费投入预测中,从而证明了该方法的可行性和有效性. 相似文献
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通过分析选择决策树作为保险业客户流失预测模型实现的手段,给出了决策树实现流失预测的算法设计,通过流程图及相应数据结构的说明,介绍了算法实现要点,最后的实验结果表明所建立的预测模型具有良好的准确率。 相似文献
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基于粗集的组合预测方法在我国R＆D经费投入中的应用 总被引：1，自引：0，他引：1
组合预测能够充分利用已知信息,从而提高预测精度。通过建立预测模型与预测对象的关系数据模型,利用离散化属性数据值来建立知识表达系统和决策表,并依据粗糙集理论计算组合预测模型中各单一模型的权系数,将该方法运用到我国R＆D经费投入预测中,从而证明了该方法的可行性和有效性。 相似文献
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Cross-Company Churn Prediction (CCCP) is a domain of research where one company (target) is lacking enough data and can use data from another company (source) to predict customer churn successfully. To support CCCP, the cross-company data is usually transformed to a set of similar normal distribution of target company data prior to building a CCCP model. However, it is still unclear which data transformation method is most effective in CCCP. Also, the impact of data transformation methods on CCCP model performance using different classifiers have not been comprehensively explored in the telecommunication sector. In this study, we devised a model for CCCP using data transformation methods (i.e., log, z-score, rank and box-cox) and presented not only an extensive comparison to validate the impact of these transformation methods in CCCP, but also evaluated the performance of underlying baseline classifiers (i.e., Naive Bayes (NB), K-Nearest Neighbour (KNN), Gradient Boosted Tree (GBT), Single Rule Induction (SRI) and Deep learner Neural net (DP)) for customer churn prediction in telecommunication sector using the above mentioned data transformation methods. We performed experiments on publicly available datasets related to the telecommunication sector. The results demonstrated that most of the data transformation methods (e.g., log, rank, and box-cox) improve the performance of CCCP significantly. However, the Z-Score data transformation method could not achieve better results as compared to the rest of the data transformation methods in this study. Moreover, it is also investigated that the CCCP model based on NB outperform on transformed data and DP, KNN and GBT performed on the average, while SRI classifier did not show significant results in term of the commonly used evaluation measures (i.e., probability of detection, probability of false alarm, area under the curve and g-mean). 相似文献
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基于CART二叉决策树的电信业客户流失的模型构建与控制 总被引：1，自引：0，他引：1
分析了决策树在电信业客户流失分析中的重要应用,建立了基于CART算法的二叉决策树构造的电信业客户流失分析模型,研究如何使用二叉决策树的构造方法来克服客户流失分析中数据碎片的产生,使用spss clementin数据挖掘平台构造挖掘模型并且使用实时的数据出现概率来进行模型的优化控制。实验与仿真证明,该模型的准确率较高,效果显著。 相似文献
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Undoubtedly, the change in consumers’ choices and expectations, stemming from the emerging technology and also significant availability of different products and services, created a highly competitive landscape in various customer service sectors, including the financial industry. Accordingly, the Canadian banking industry has also become highly competitive due to the threats and disruptions caused by not only direct competitors, but also new entrants to the market.The primary objective of this paper is to construct a predictive churn model by utilizing big data, including the structured archival data, integrated with unstructured data from sources such as online web pages, the number of website visits and phone conversation logs, for the first time in the financial industry. It also examines the effect of different aspects of customers’ behavior on churning decisions. The Datameer big data analytics tool on the Hadoop platform and predictive techniques using the SAS business intelligence system were applied to study the client retirement journey path and to create a churn prediction model. By deploying the above systems, we were able to uncover a wealth of data and information associated with over 3 million customers’ records within the retiree segment of the target bank, from 2011 to 2015. 相似文献
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《Information processing & management》2020,57(2):102052
Finding structural and efficient ways of leveraging available data is not an easy task, especially when dealing with network data, as is the case in telco churn prediction. Several previous works have made advancements in this direction both from the perspective of churn prediction, by proposing augmented call graph architectures, and from the perspective of graph featurization, by proposing different graph representation learning methods, frequently exploiting random walks. However, both graph augmentation as well as representation learning-based featurization face drawbacks. In this work, we first shift the focus from a homogeneous to a heterogeneous perspective, by defining different probabilistic meta paths on augmented call graphs. Secondly, we focus on solutions for the usually significant number of random walks that graph representation learning methods require. To this end, we propose a sampling method for random walks based on a combination of most suitable random walk generation strategies, which we determine with the help of corresponding Markov models. In our experimental evaluation, we demonstrate the benefits of probabilistic meta path-based walk generation in terms of predictive power. In addition, this paper provides promising insights regarding the interplay of the type of meta path and the predictive outcome, as well as the potential of sampling random walks based on the meta path structure in order to alleviate the computational requirements of representation learning by reducing typically sizable required data input. 相似文献
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利用多案例分析和扎根理论探讨了平台型企业开放式服务创新风险的内在机理及作用机制。研究发现了风险因素的9个主范畴,3个风险核心范畴,以及2个风险发生后果,构成了平台型企业开放式服务创新的风险成因。实证研究表明,商业环境风险、平台战略风险、管理风险这三个风险对平台创新活动影响巨大,显著影响了平台型企业客户流失以及经营绩效。商业环境风险是平台创新面临的外部风险,与平台战略风险和管理风险呈显著正向关系,战略风险会增加管理风险发生的可能性。三大风险与客户流失呈正比,与平台经营绩效呈反比。本研究拓展了传统的管理要素模型,揭示了互联网平台企业创新过程中的风险互动机制。 相似文献
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在较全面分析网络产品特有的经济特征基础之上,采用定性与定量分析相结合的方法,利用AHP法、模糊综合评判及Markov法相结合的方法,构建出网络经济环境下客户满意度动态分析评价模型。 相似文献
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《Information processing & management》2019,56(4):1300-1316
Dynamic Ensemble Selection (DES) strategy is one of the most common and effective techniques in machine learning to deal with classification problems. DES systems aim to construct an ensemble consisting of the most appropriate classifiers selected from the candidate classifier pool according to the competence level of the individual classifier. Since several classifiers are selected, their combination becomes crucial. However, most of current DES approaches focus on the combination of the selected classifiers while ignoring the local information surrounding the query sample needed to be classified. In order to boost the performance of DES-based classification systems, we in this paper propose a dynamic weighting framework for the classifier fusion during obtaining the final output of an DES system. In particular, the proposed method first employs a DES approach to obtain a group of classifiers for a query sample. Then, the hypothesis vector of the selected ensemble is obtained based on the analysis of consensus. Finally, a distance-based weighting scheme is developed to adjust the hypothesis vector depending on the closeness of the query sample to each class. The proposed method is tested on 30 real-world datasets with six well-known DES approaches based on both homogeneous and heterogeneous ensemble. The obtained results, supported by proper statistical tests, show that our method outperforms, both in terms of accuracy and kappa measures, the original DES framework. 相似文献
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将多分类器融合技术用于CRM中的客户分类研究,以提高分类性能。以决策树作为基本分类器,引入最小二乘技术进行多分类器线性融合。实证结果显示,4种不同的融合方案的分类性能均胜过任一基本分类器,甚至优于基于遗传算法的神经网络融合分类结果,从而表明了该方法的可行性和有效性。 相似文献