【正文】 圖 1 顯示了升力曲線完美的把“流失”和“非流失”分離開 :所有的流失客戶都被預(yù)測模型檢測。 Logistic 回歸在提供最好可能的評(píng)估轉(zhuǎn)變一個(gè)依賴的 logistic 的因變量 [8]。在零售環(huán)境下的客戶流失是指客戶轉(zhuǎn)向另一個(gè)商店進(jìn)行購買。 、客戶流失 客戶流失的關(guān)鍵是確定那些有流失危 險(xiǎn)的客戶和分析能否留住這些有價(jià)值的客戶。 客戶流失的需求預(yù)測 本文中我們的案例在零售銀行業(yè)的一個(gè)公司經(jīng)營提供的數(shù)據(jù)。 個(gè)人零售銀行業(yè)務(wù)部門的特點(diǎn)是客 戶與公司建立很長時(shí)間的聯(lián)系。這些公司中有多少公司能真正因?yàn)槭チ丝蛻袅魇Ф幸庾R(shí) ,為保持適度的運(yùn)動(dòng)規(guī)模而努力。所有的流失預(yù)測結(jié)果是在第 6 章介紹。 LTV 分析還可以幫助公司定制滿足消費(fèi)者需求的產(chǎn)品和服務(wù)。這些方法在流失分析的表內(nèi)的數(shù)據(jù)尺度和市場部門的信息有一定的介紹。例如 Buckinx 等人 ,已經(jīng)把 logistic 回歸運(yùn)用在零售環(huán)境中預(yù)測有缺陷的客戶 [4]。我 們將利用升力曲線的回歸來分析估計(jì)結(jié)果。 圖 1 顯示了升力曲線完善了流失客戶和非流失客戶的區(qū)別和非區(qū)別。 Logistic回歸模型在生物科學(xué)界有很長的歷史。這可能是因?yàn)樗麄冊(cè)O(shè)置 了他們只專注于忠誠的客戶。舉例信用卡業(yè)務(wù)的客戶很容易使用另一種信用卡 ,而對(duì)以前的信用卡的公司的唯一的指標(biāo)是下降的?？蛻艚K身價(jià)值分析將有助于面對(duì)這一挑 戰(zhàn)。即使繼續(xù)與有潛在收入損失的客戶保持關(guān)系 ,在這種情況下客戶流失是巨大的。傳統(tǒng)的統(tǒng)計(jì)方法非常成功的預(yù)測客戶流失。還有一個(gè)關(guān)于客戶的流失文獻(xiàn)中第 2 章中。 更好地理解客戶終身價(jià)值的好處有很多。在市場營銷角度有足夠的信息的流失就可能是客戶流失。 Logistic 回歸 ,可以用來預(yù)測一個(gè)連續(xù)和 /或分類變量上離散的結(jié)果。即 連續(xù)概率的結(jié)果是通過邏輯回歸模型使用一個(gè)閾值在兩組中進(jìn)行區(qū)別而產(chǎn)生的。因此 ,一個(gè)公司可以指望有多少客戶聯(lián)系 ,如果 25%的潛在流失客戶是與外界保持聯(lián)系。它導(dǎo)致了用線性回歸的方法來解決評(píng)估。這使客戶流失在銀行業(yè)優(yōu)先于大多數(shù)行業(yè) ,Garland 做了個(gè)關(guān)于個(gè)人零售銀行的客戶盈利的研究 [11]。 Hwang 等人在競爭激烈的無線電信業(yè)定義最熱門的客戶流失問題。 在 LTV 概念的潛在意識(shí)和客戶關(guān)系來衡量終身價(jià)值是很簡單的。首先是在銀行方面進(jìn)行流失客戶的分析?？蛻袅魇Х治鐾馕姆g 畢業(yè)論文 (設(shè)計(jì) )外文翻譯 題 目 : 客戶流失問題研究 一、外文原文 標(biāo)題 :Customer churn analysis ? a case study 原文 : ABSTRACT Customer value analysis is critical for a good marketing and a customer relationship management strategy. An important ponent of this strategy is the customer retention rate. Customer retention rate has a strong impact on the customer lifetime value, and understanding the true value of a possible customer churn will help the pany in its customer relationship management. Conventional statistical methods are very successful in predicting a customer churn. The goal of this study is to apply logistic regression techniques to predict a customer churn and analyze the churning and nochurning customers by using data from a personal retail banking pany. 1. Introduction The subject of customer retention, loyalty, and churn is receiving attention in many industries. This is important in the customer lifetime value context. A pany will have a sense of how much is really being lost because of the customer churn and the scale of the efforts that would be appropriate for retention campaign. The mass marketing approach cannot succeed in the diversity of consumer business today. Customer value analysis along with customer churn predictions will help marketing programs target more specific groups of customers Personal retail banking sector is characterized by customers who stays with a pany very long time. Customers usually give their financial business to one pany and they won’t switch the provider of their financial help very often. In the pany’s perspective this produces a stabile environment for the customer relationship management. Although the continuous relationships with the customers the potential loss of revenue because of customer churn in this case can be huge. This paper will present a customer churn analysis in personal retail banking sector. The goal of this paper is twofold. First the churning customers are analyzed in banking context. The second objective is a forecast of churning customers based on a logistic regression model. After the introduction this paper has 6 sections. The background for customer lifetime value concept is presented in the chapter 2. There is also a literature review about the customer churn included in the chapter 2. The methods used in this study are present