【正文】 因此 ,一個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。這種情況發(fā)生時 ,客戶流失概率是隨機的。該曲線是有關升降機的信號檢測理論和精確召回曲線中華民國在信息檢索文獻曲線 [16]。即 連續(xù)概率的結果是通過邏輯回歸模型使用一個閾值在兩組中進行區(qū)別而產(chǎn)生的。它導致了用線性回歸的方法來解決評估。 這里提到的 logistic 回歸是 . Cramer 在一本書中提出的。多項式回歸已被用于預測客戶的未來盈 利能力的基礎上 ,他們的人口統(tǒng)計信息和購買歷史的讀書俱樂部 [1]。 Logistic 回歸 ,可以用來預測一個連續(xù)和 /或分類變量上離散的結果。這使客戶流失在銀行業(yè)優(yōu)先于大多數(shù)行業(yè) ,Garland 做了個關于個人零售銀行的客戶盈利的研究 [11]。 Butnix 等人如果他們從偏離既定的購買行為分類客戶是有缺陷的 [4]。 Buckinx 等人測量忠誠度和流失率在零售環(huán)境下是不同的。在市場營銷角度有足夠的信息的流失就可能是客戶流失。 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。業(yè)務部門和客戶的關系影響客戶流失的預測?？蛻魞r值的理解能幫助該公司把重點放在生產(chǎn)性收入和產(chǎn)量與可能帶來的負面影響的客戶群的客戶收入上。 更好地理解客戶終身價值的好處有很多。 在 LTV 概念的潛在意識和客戶關系來衡量終身價值是很簡單的。因此 ,確定最后的客戶關系甚至要幾十年 ,該公司必須解決的一個潛在客戶流失的價值。而本研究的結論是在第 7 章。還有一個關于客戶的流失文獻中第 2 章中。首先是在銀行方面進行流失客戶的分析。在公司的角度來看 ,這將產(chǎn)生一個客戶關系管理的穩(wěn)定環(huán)境。消費業(yè)務多元化的今天 ,大眾營銷方式是不能成功的。傳統(tǒng)的統(tǒng)計方法非常成功的預測客戶流失?？蛻袅魇Х治鐾馕姆g 畢業(yè)論文 (設計 )外文翻譯 題 目 : 客戶流失問題研究 一、外文原文 標題 :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 presented in the chapter 3 while a closer look at the case data is taken in the chapter 4. The focus of this case study is described in the chapter 5. All the results of the churn prediction are presented in the chapter 6. And conclusions of this study are lef