天津移動關(guān)鍵績效指標(biāo)MIC和LASSO分析及規(guī)則集成預(yù)測
發(fā)布時間：2018-09-05 20:28
【摘要】：績效管理已經(jīng)從以年終分配為目的的績效考核發(fā)展到以全面提升企業(yè)管理水平為目的的績效管理。2002年以來中國移動公司已將關(guān)鍵績效指標(biāo)KPI管理模式的績效管理置于核心位置：公司高層制定決策少不了KPI，月經(jīng)營分析更是圍繞KPI展開。以天津移動公司為例，其績效考核雖已有一套較為完整的方法，但是員工得分普遍較低，得分低的一個重要原因就是KPI目標(biāo)定的過高，因此不能充分發(fā)揮它的激勵作用。所以本文首次利用各種相關(guān)系數(shù)及套索法LASSO，制定出KPI的合理目標(biāo)，使績效考核真正達(dá)到對客戶經(jīng)理的激勵作用，從而提高移動公司的經(jīng)濟(jì)效益。 客戶經(jīng)理的績效受三方面因素的影響：外部行業(yè)環(huán)境，自身能力和隨機因素。外部行業(yè)環(huán)境可以通過公司的經(jīng)營收入反映，通過計算相關(guān)系數(shù)之間的相關(guān)程度得到。如果相關(guān)系數(shù)的兩個變量都是正態(tài)分布，則線性相關(guān)系數(shù)檢驗功效高；如果不是正態(tài)分布，就需考慮其它的相關(guān)系數(shù)。分別計算客戶經(jīng)理關(guān)鍵績效指標(biāo)完成值同公司運營收入之間的距離相關(guān)DCC(DistanceCorrelation Coefcient)、和HHG距離(Heller Heller Gorfine Distance)及最大信息指數(shù)MIC(Maximal Information Coefcient)等系數(shù)，綜合比較這些系數(shù)，即可得到外部行業(yè)環(huán)境對客戶經(jīng)理績效影響的結(jié)果。 員工的自身的個人能力因素可選取年齡、性別、職位、職級、所屬團(tuán)體、最高學(xué)歷和工作天數(shù)7個指標(biāo)為自變量，以2013年客戶經(jīng)理累計完成的集團(tuán)信息化收入為因變量，利用LASSO方法找出對因變量影響最大的自變量。性別、職位職級、所屬團(tuán)體、最高學(xué)歷都為分類變量，不能直接用于LASSO回歸，因此，回歸之前需要采用虛擬編碼。LASSO方法循環(huán)迭代的步數(shù)可通過交叉證實(CrossValidation)得到最優(yōu)值。從而得到對客戶經(jīng)理績效影響最大的個人能力因素。 根據(jù)以上分析結(jié)果表明，客戶經(jīng)理績效指標(biāo)的完成值和公司運營收入關(guān)聯(lián)性很大。因此，為了更好地制定績效考核目標(biāo)，就需要提前預(yù)測公司收入。目前由于聯(lián)通、電信的強力競爭，移動公司客戶流失嚴(yán)重，整個公司收入也在減少。選取天津濱海分公司簽約的200家集團(tuán)用戶為例，將簽約時間、2012年是否在網(wǎng)、2012客戶規(guī)模、2012年集團(tuán)成員統(tǒng)一付費通信收入、2012年增值業(yè)務(wù)收入指標(biāo)作為自變量，，將2013年是否在網(wǎng)作為因變量，利用機器學(xué)習(xí)法中的先進(jìn)規(guī)則集成(Rule Ensemble)法，進(jìn)行分類擬合。再計算出各變量的重要性和繪制偏相關(guān)圖形、交互作用圖形等。最后預(yù)測出容易流失和需要重點攻關(guān)的集團(tuán)用戶，從而保證移動公司業(yè)務(wù)的收入穩(wěn)定增長。
[Abstract]:Performance management has developed from performance appraisal aimed at year-end distribution to performance management aimed at improving the level of enterprise management. Since 2002, China Mobile has implemented the performance management of the key performance indicator KPI management model. At the core: the company's top decision making without KPI, monthly business analysis is around the KPI. Taking Tianjin Mobile Corporation as an example, although its performance appraisal has a set of relatively complete method, but the staff score is generally low, one of the important reasons of low score is that the goal of KPI is too high, so it can not give full play to its incentive role. So this paper makes use of all kinds of correlation coefficient and the lasso method LASSO, to formulate the reasonable goal of KPI for the first time, so that the performance appraisal can really achieve the incentive function to the customer manager, thus improving the economic benefit of the mobile company. Account manager performance is affected by three factors: external industry environment, self-competence and random factors. The external industry environment can be reflected by the company's operating income, and the correlation degree between the correlation coefficients can be calculated. If both variables of the correlation coefficient are normal distribution, the efficiency of linear correlation coefficient test is high; if the correlation coefficient is not normal distribution, other correlation coefficients should be considered. The distance correlation DCC (DistanceCorrelation Coefcient), HHG distance (Heller Heller Gorfine Distance) and the maximum information index MIC (Maximal Information Coefcient) between the completion value of the key performance index of the customer manager and the company's operating income were calculated, and these coefficients were compared synthetically. We can get the result of the effect of the external industry environment on the performance of the customer manager. The individual ability factors of employees can be selected as seven independent variables: age, gender, position, rank, affiliated group, highest educational background and working days, and the income of group informatization completed by the customer manager in 2013 is dependent variable. The LASSO method is used to find out the independent variables which have the greatest influence on dependent variables. Gender, rank and rank of position, group and highest education are all classified variables, which can not be directly used in LASSO regression. Therefore, the number of steps that need to be iterated by virtual coding. LASSO method before regression can be cross-verified by (CrossValidation) to obtain the optimal value. Thus, the personal ability factors which have the greatest influence on the customer manager's performance are obtained. According to the above analysis results, the completion value of the customer manager performance index is closely related to the operating income of the company. Therefore, in order to better establish performance appraisal goals, we need to predict the company's income ahead of time. At present, due to the strong competition of Unicom and telecom, mobile company customers are losing a lot, and the whole company's revenue is also decreasing. Taking 200 group users signed by Tianjin Binhai Branch as an example, the time of signing the contract, whether the customer size of 2012 is on the net, the unified payment communication revenue of 2012 group members, and the revenue index of value-added service in 2012 are taken as independent variables. In this paper, we use the advanced rules of machine learning method to integrate (Rule Ensemble) method and classify fit whether or not it is in the network as dependent variable in 2013. Then calculate the importance of each variable and draw partial correlation figure, interaction figure and so on. Finally, the group users who are easy to lose and need key problems are predicted to ensure the steady growth of mobile business revenue.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：F626
本文編號：2225378
[Abstract]:Performance management has developed from performance appraisal aimed at year-end distribution to performance management aimed at improving the level of enterprise management. Since 2002, China Mobile has implemented the performance management of the key performance indicator KPI management model. At the core: the company's top decision making without KPI, monthly business analysis is around the KPI. Taking Tianjin Mobile Corporation as an example, although its performance appraisal has a set of relatively complete method, but the staff score is generally low, one of the important reasons of low score is that the goal of KPI is too high, so it can not give full play to its incentive role. So this paper makes use of all kinds of correlation coefficient and the lasso method LASSO, to formulate the reasonable goal of KPI for the first time, so that the performance appraisal can really achieve the incentive function to the customer manager, thus improving the economic benefit of the mobile company. Account manager performance is affected by three factors: external industry environment, self-competence and random factors. The external industry environment can be reflected by the company's operating income, and the correlation degree between the correlation coefficients can be calculated. If both variables of the correlation coefficient are normal distribution, the efficiency of linear correlation coefficient test is high; if the correlation coefficient is not normal distribution, other correlation coefficients should be considered. The distance correlation DCC (DistanceCorrelation Coefcient), HHG distance (Heller Heller Gorfine Distance) and the maximum information index MIC (Maximal Information Coefcient) between the completion value of the key performance index of the customer manager and the company's operating income were calculated, and these coefficients were compared synthetically. We can get the result of the effect of the external industry environment on the performance of the customer manager. The individual ability factors of employees can be selected as seven independent variables: age, gender, position, rank, affiliated group, highest educational background and working days, and the income of group informatization completed by the customer manager in 2013 is dependent variable. The LASSO method is used to find out the independent variables which have the greatest influence on dependent variables. Gender, rank and rank of position, group and highest education are all classified variables, which can not be directly used in LASSO regression. Therefore, the number of steps that need to be iterated by virtual coding. LASSO method before regression can be cross-verified by (CrossValidation) to obtain the optimal value. Thus, the personal ability factors which have the greatest influence on the customer manager's performance are obtained. According to the above analysis results, the completion value of the customer manager performance index is closely related to the operating income of the company. Therefore, in order to better establish performance appraisal goals, we need to predict the company's income ahead of time. At present, due to the strong competition of Unicom and telecom, mobile company customers are losing a lot, and the whole company's revenue is also decreasing. Taking 200 group users signed by Tianjin Binhai Branch as an example, the time of signing the contract, whether the customer size of 2012 is on the net, the unified payment communication revenue of 2012 group members, and the revenue index of value-added service in 2012 are taken as independent variables. In this paper, we use the advanced rules of machine learning method to integrate (Rule Ensemble) method and classify fit whether or not it is in the network as dependent variable in 2013. Then calculate the importance of each variable and draw partial correlation figure, interaction figure and so on. Finally, the group users who are easy to lose and need key problems are predicted to ensure the steady growth of mobile business revenue.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：F626
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本文編號：2225378
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