外文翻译--预测电信行业客户流失——基于一种SAS生存分析模式的应用程序

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1、 中文 3770字 标题： Predicting Customer Churn in the Telecommunications Industry An Application of Survival Analysis Modeling Using SAS 原文： ABSTRACT Conventional statistical methods (e.g. logistics regression, decision tree, and etc.) are very successful in predicting customer churn. However, these method

2、s could hardly predict when customers will churn, or how long the customers will stay with. The goal of this study is to apply survival analysis techniques to predict customer churn by using data from a telecommunications company. This study will help telecommunications companies understand customer

3、 churn risk and customer churn hazard in a timing manner by predicting which customer will churn and when they will churn. The findings from this study are helpful for telecommunications companies to optimize their customer retention and/or treatment resources in their churn reduction efforts. INTRO

4、DUCTION In the telecommunication industry, customers are able to choose among multiple service providers and actively exercise their rights of switching from one service provider to another. In this fiercely competitive market, customers demand tailored products and better services at less prices, w

5、hile service providers constantly focus on acquisitions as their business goals. Given the fact that the telecommunications industry experiences an average of 30-35 percent annual churn rate and it costs 5-10 times more to recruit a new customer than to retain an existing one, customer retention has

6、 now become even more important than customer acquisition. For many incumbent operators, retaining high profitable customers is the number one business pain. Many telecommunications companies deploy retention strategies in synchronizing programs and processes to keep customers longer by providing th

7、em with tailored products and services. With retention strategies in place, many companies start to include churn reduction as one of their business goals. In order to support telecommunications companies manage churn reduction, not only do we need to predict which customers are at high risk of chur

8、n, but also we need to know how soon these high-risk customers will churn. Therefore the telecommunications companies can optimize their marketing intervention resources to prevent as many customers as possible from churning. In other words, if the telecommunications companies know which customers a

9、re at high risk of churn and when they will churn, they are able to design customized customer communication and treatment programs in a timely efficient manner. Conventional statistical methods (e.g. logistics regression, decision tree, and etc.) are very successful in predicting customer churn. Th

10、ese methods could hardly predict when customers will churn, or how long the customers will stay with. However, survival analysis was, at the very beginning, designed to handle survival data, and therefore is an efficient and powerful tool to predict customer churn. OBJECTIVES The objectives of this

11、study are in two folds. The first objective is to estimate customer survival function and customer hazard function to gain knowledge of customer churn over the time of customer tenure. The second objective is to demonstrate how survival analysis techniques are used to identify the customers who are

12、at high risk of churn and when they will churn. DEFINITIONS AND EXCLUSIONS This section clarifies some of the important concepts and exclusions used in this study. Churn In the telecommunications industry, the broad definition of churn is the action that a customers telecommunications service is can

13、celed. This includes both service-provider initiated churn and customer initiated churn. An example of service-provider initiated churn is a customers account being closed because of payment default. Customer initiated churn is more complicated and the reasons behind vary. In this study, only custom

14、er initiated churn is considered and it is defined by a series of cancel reason codes. Examples of reason codes are: unacceptable call quality, more favorable competitors pricing plan, misinformation given by sales, customer expectation not met, billing problem, moving, change in business, and so on

15、. High-Value Customers Only customers who have received at least three monthly bills are considered in the study. High-value customers are these with monthly average revenue of $X or more for the last three months. If a customers first invoice covers less than 30 days of service, then the customer m

16、onthly revenue is prorated to a full months revenue. Granularity This study examines customer churn at the account level. Exclusions This study does not distinguish international customers from domestic customers. However it is desirable to investigate international customer churn separately from do

17、mestic customer churn in the future.Also, this study does not include employee accounts, since churn for employee accounts is not of a problem or an interest for the company. SURVIVAL ANALYSIS AND CUSTOMER CHURN Survival analysis is a clan of statistical methods for studying the occurrence and timin

18、g of events. From the beginning, survival analysis was designed for longitudinal data on the occurrence of events. Keeping track of customer churn is a good example of survival data. Survival data have two common features that are difficult to handle with conventional statistical methods: censoring

19、and time-dependent covariates. Generally, survival function and hazard function are used to describe the status of customer survival during the tenure of observation. The survival function gives the probability of surviving beyond a certain time point t. However, the hazard function describes the ri

20、sk of event (in this case, customer churn) in an interval time after time t, conditional on the customer already survived to time t. Therefore the hazard function is more intuitive to use in survival analysis because it attempts to quantify the instantaneous risk that customer churn will take place

21、at time t given that the customer already survived to time t. For survival analysis, the best observation plan is prospective. We begin observing a set of customers at some well-defined point of time (called the origin of time) and then follow them for some substantial period of time, recording the times at which customer churns occur. Its not necessary that every customer experience churn