电信运营企业客户流失预测与评价研究
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摘要
电信运营企业客户流失是一个受多因素影响的复杂问题，尤其是2008年以后我国电信业针对3G牌照的发放又进行了新一轮的电信重组，全业务运营下的三大运营企业从此展开了激烈的客户市场竞争。由于我国移动客户群体庞大，中低端客户在不同运营企业间流动性强，因此，针对客户流失的成因分析和建立客户流失预测模型具有重要的理论价值和现实意义。
本文详细分析了国内外学者在客户流失领域的研究成果，探讨了客户流失的影响因素和客户流失预测的方法。通过对3G时代电信运营环境的分析，总结了国内外电信运营企业客户流失的现状，并从电信运营环境角度、运营企业流失客户数据统计分析角度深入研究了电信运营企业客户流失的成因，归纳得到客户流失成因的8种类型。据此，基于数据挖掘和客户价值的理论和方法，研究了BP神经网络算法、支持向量机算法、C5.0决策树算法在客户流失预测上的应用，为了获得更好的预测效果，构建了Lagrange组合预测模型和基于客户价值的预测模型。重点就以下问题进行了研究：
在广泛研究和借鉴国内外相关数据挖掘理论及成果的基础上，探讨了电信运营企业的客户构成，深入分析了客户流失与流失客户的概念、以及客户流失的现象与特征，从而梳理给出三户关系模型。
对构建模型的客户属性进行了分类，即原始属性与衍生属性。以往对电信客户流失预测的研究都是采用客户消费行为、个人信息、缴费信息等原始属性数据，这些原始属性数据很难真实地反映客户流失的行为；加入了衍生属性，如：月租标志、呼转标志、账户余额标志、充值行为标志等，其数据集能更好的预测客户流失，使得预测的命中率更高，计算的客户价值更具研究意义。
通过分析客户协议数据、消费行为数据和账单数据得出与客户流失密切相关的属性集，根据获取运营企业数据的难易程度，建立了客户流失预测指标体系，并基于数据挖掘算法建立了Lagrange组合预测模型。针对客户流失预测问题的研究，选择了数据挖掘的三种经典算法（BP、SVM、C5.0）构建了单一客户流失预测模型，并通过对模型的评估显示，任意单一模型都没有最优。据此借助Lagrange函数求极值的思想构建了客户流失的组合预测模型，其预测效果比单一模型更理想。
提出二维度预防客户流失的方法，即基于Lagrange的客户流失组合预测与基于客户价值的流失客户评价。根据组合预测模型预测得到的客户流失名单是否有挽留的价值，或者说是否有对这样的客户有再投入成本挽留的必要，取决于该客户对运营企业是否是有价值客户，并依据这两种途径的预测结果，再分析客户流失的根本原因。
最后，通过对客户流失成因的分析，以及对客户流失预测模型的评估，提出电信运营企业降低客户流失的措施与建议。
At present, the telecom operation enterprise customer loss is a complex probleminfluenced by various factors. Especially China's telecom industry has conducted a newround of telecom restructuring again according to the issuance of3G licenses, since then theentire business operations under three operation enterprise have had a fierce customer marketcompetition. Because of the huge mobile customer group of China mobile and the strongmobility of the low-end customers between different operation enterprise, in view of thecustomer loss, analyzing and building customer loss prediction model has importanttheoretical value and practical significance.
This paper analyzed the research of scholars in the field of customer churn in detail,discussed the factors and prediction methods influencing the customer loss. Through theanalysis of the telecom operating environment of3G era, the current situation of the domesticand foreign telecom operation enterprise customer loss were summarized. In the angle oftelecom operating environment and the operation enterprise losing customer data statisticalanalysis, the causes of the telecom operation enterprise customer churn were discussed deeply.Then, eight types of the customer loss causes were summarized. Accordingly, based on datamining and customer value theory and method, the application of the BP neural networkalgorithm, the algorithm of support vector machine, C5.0decision tree algorithm in customerloss prediction were studied. In order to obtain better forecast effect, Lagrange combinationforecast model and prediction model based on customer value were constructed. We havefocused on the following questions:
First, based on studying and drawing the domestic and international relevant data miningtheory and results extensively, we explored the customer structure of the telecom operationenterprise, analyzed the concept of customer loss and losing customer, as well as thephenomenon and characteristics of customer loss, thus combing three relation models havebeen organized.
Second, carrying on the constructing model of customer attribute classification study, weput forward primary attributes and derivative attributes' effect on studying customer lossprediction comparatively. In the past, the studying on telecom customer loss prediction was onthe basis of customer consumption behavior, personal information, payment information and other original attribute data which are hard to truly reflect the behavior of the customer loss;The derivative attributes were added, such as: rent signs, call turn signs, the account balancesigns, recharge behavior signs and so on. The data set can better predict the customer loss,making prediction hit rate higher, the research significance of customer value calculationmore.
Third, by analyzing customer agreement data，consumer behavior data and billing data，attribute set relating the customer loss were concluded. According to the ease of access to theoperating enterprise data, the customer loss prediction index system was established. At thesame time, the Lagrange combination forecast model was built based on data miningalgorithms. For customer loss prediction research, three classic data mining algorithm (BP,SVM, C5.0) were selected to build a single customer churn prediction model, and the modelassessment shows that not any single model are optimal. Accordingly, with the thought ofLagrange function for extreme, the combination forecast model of customer loss isconstructed whose forecast effect is more ideal than a single model.
Fourth, that whether the customer loss list resulting from he prediction of thecombination forecasting model is valuable to retain or is it necessary to reinvest for thesecustomer is depending on if they are valuable for customers to the operating enterprise.Therefore, the methods based on two dimensions to enhance customer loss prediction resultswere proposed. The two dimensions are customer loss combination forecast based on theLagrange and the loss prediction on the basis of customer value. Then according to theprediction results of these two approaches, the root causes of customer loss have beenanalyzed.
Finally, through the analysis of the causes of the customer loss, as well as the assessmentof customer churn prediction model, measures and suggestions to reduce customer loss for thetelecom operating enterprise would be proposed.
本文详细分析了国内外学者在客户流失领域的研究成果，探讨了客户流失的影响因素和客户流失预测的方法。通过对3G时代电信运营环境的分析，总结了国内外电信运营企业客户流失的现状，并从电信运营环境角度、运营企业流失客户数据统计分析角度深入研究了电信运营企业客户流失的成因，归纳得到客户流失成因的8种类型。据此，基于数据挖掘和客户价值的理论和方法，研究了BP神经网络算法、支持向量机算法、C5.0决策树算法在客户流失预测上的应用，为了获得更好的预测效果，构建了Lagrange组合预测模型和基于客户价值的预测模型。重点就以下问题进行了研究：
在广泛研究和借鉴国内外相关数据挖掘理论及成果的基础上，探讨了电信运营企业的客户构成，深入分析了客户流失与流失客户的概念、以及客户流失的现象与特征，从而梳理给出三户关系模型。
对构建模型的客户属性进行了分类，即原始属性与衍生属性。以往对电信客户流失预测的研究都是采用客户消费行为、个人信息、缴费信息等原始属性数据，这些原始属性数据很难真实地反映客户流失的行为；加入了衍生属性，如：月租标志、呼转标志、账户余额标志、充值行为标志等，其数据集能更好的预测客户流失，使得预测的命中率更高，计算的客户价值更具研究意义。
通过分析客户协议数据、消费行为数据和账单数据得出与客户流失密切相关的属性集，根据获取运营企业数据的难易程度，建立了客户流失预测指标体系，并基于数据挖掘算法建立了Lagrange组合预测模型。针对客户流失预测问题的研究，选择了数据挖掘的三种经典算法（BP、SVM、C5.0）构建了单一客户流失预测模型，并通过对模型的评估显示，任意单一模型都没有最优。据此借助Lagrange函数求极值的思想构建了客户流失的组合预测模型，其预测效果比单一模型更理想。
提出二维度预防客户流失的方法，即基于Lagrange的客户流失组合预测与基于客户价值的流失客户评价。根据组合预测模型预测得到的客户流失名单是否有挽留的价值，或者说是否有对这样的客户有再投入成本挽留的必要，取决于该客户对运营企业是否是有价值客户，并依据这两种途径的预测结果，再分析客户流失的根本原因。
最后，通过对客户流失成因的分析，以及对客户流失预测模型的评估，提出电信运营企业降低客户流失的措施与建议。
At present, the telecom operation enterprise customer loss is a complex probleminfluenced by various factors. Especially China's telecom industry has conducted a newround of telecom restructuring again according to the issuance of3G licenses, since then theentire business operations under three operation enterprise have had a fierce customer marketcompetition. Because of the huge mobile customer group of China mobile and the strongmobility of the low-end customers between different operation enterprise, in view of thecustomer loss, analyzing and building customer loss prediction model has importanttheoretical value and practical significance.
This paper analyzed the research of scholars in the field of customer churn in detail,discussed the factors and prediction methods influencing the customer loss. Through theanalysis of the telecom operating environment of3G era, the current situation of the domesticand foreign telecom operation enterprise customer loss were summarized. In the angle oftelecom operating environment and the operation enterprise losing customer data statisticalanalysis, the causes of the telecom operation enterprise customer churn were discussed deeply.Then, eight types of the customer loss causes were summarized. Accordingly, based on datamining and customer value theory and method, the application of the BP neural networkalgorithm, the algorithm of support vector machine, C5.0decision tree algorithm in customerloss prediction were studied. In order to obtain better forecast effect, Lagrange combinationforecast model and prediction model based on customer value were constructed. We havefocused on the following questions:
First, based on studying and drawing the domestic and international relevant data miningtheory and results extensively, we explored the customer structure of the telecom operationenterprise, analyzed the concept of customer loss and losing customer, as well as thephenomenon and characteristics of customer loss, thus combing three relation models havebeen organized.
Second, carrying on the constructing model of customer attribute classification study, weput forward primary attributes and derivative attributes' effect on studying customer lossprediction comparatively. In the past, the studying on telecom customer loss prediction was onthe basis of customer consumption behavior, personal information, payment information and other original attribute data which are hard to truly reflect the behavior of the customer loss;The derivative attributes were added, such as: rent signs, call turn signs, the account balancesigns, recharge behavior signs and so on. The data set can better predict the customer loss,making prediction hit rate higher, the research significance of customer value calculationmore.
Third, by analyzing customer agreement data，consumer behavior data and billing data，attribute set relating the customer loss were concluded. According to the ease of access to theoperating enterprise data, the customer loss prediction index system was established. At thesame time, the Lagrange combination forecast model was built based on data miningalgorithms. For customer loss prediction research, three classic data mining algorithm (BP,SVM, C5.0) were selected to build a single customer churn prediction model, and the modelassessment shows that not any single model are optimal. Accordingly, with the thought ofLagrange function for extreme, the combination forecast model of customer loss isconstructed whose forecast effect is more ideal than a single model.
Fourth, that whether the customer loss list resulting from he prediction of thecombination forecasting model is valuable to retain or is it necessary to reinvest for thesecustomer is depending on if they are valuable for customers to the operating enterprise.Therefore, the methods based on two dimensions to enhance customer loss prediction resultswere proposed. The two dimensions are customer loss combination forecast based on theLagrange and the loss prediction on the basis of customer value. Then according to theprediction results of these two approaches, the root causes of customer loss have beenanalyzed.
Finally, through the analysis of the causes of the customer loss, as well as the assessmentof customer churn prediction model, measures and suggestions to reduce customer loss for thetelecom operating enterprise would be proposed.
引文
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