外文翻譯--預(yù)測(cè)電信行業(yè)客戶流失——基于一種sas生存分析模式的應(yīng)用程序-文庫(kù)吧資料
2025-05-22 05:40本頁(yè)面
【正文】 lp telemunications panies understand customer 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 telemunications panies to optimize their customer retention and/or treatment resources in their churn reduction efforts. INTRODUCTION In the telemunication 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 petitive market, customers demand tailored products and better services at less prices, while service providers constantly focus on acquisitions as their business goals. Given the fact that the telemunications industry experiences an average of 3035 percent annual churn rate and it costs 510 times more to recruit a new customer than to retain an existing one, customer retention has now bee even more important than customer acquisition. For many incumbent operators, retaining high profitable customers is the number one business pain. Many telemunications panies deploy retention strategies in synchronizing programs and processes to keep customers longer by providing them with tailored products and services. With retention strategies in place, many panies start to include churn reduction as one of their business goals. In order to support telemunications panies manage churn reduction, not only do we need to predict which customers are at high risk of churn, but also we need to know how soon these highrisk customers will churn. Therefore the telemunications panies can optimize their marketing intervention resources to prevent as many customers as possible from churning. In other words, if the telemunications panies know which customers are at high risk of churn and when they will churn, they are able to design customized customer munication and treatment programs in a timely efficient manner. Conventional statistical methods (. logistics regression, decision tree, and etc.) are very successful in predicting customer churn. These 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 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 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 telemunications industry, the broad definition of churn is the action that a customer’s telemunications service is canceled. This includes both serviceprovider initiated churn and customer initiated churn. An example of serviceprovider initiated churn is a customer’s account being closed because of payment default. Customer initiated churn is more plicated and the reasons behind vary. In this study, only customer 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 petitor’s pricing plan, misinformation given by sales, customer expectation not met, billing problem, moving, change in business, and so on. HighValue Customers – Only customers who have received at least three monthly bills are considered in the study. Highvalue customers are these with monthly average revenue of $X or more for the last three months. If a customer’s first invoice covers less than 30 days of service
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