外文翻譯--預(yù)測(cè)電信行業(yè)客戶(hù)流失——基于一種sas生存分析模式的應(yīng)用程序-展示頁(yè)
2025-05-27 05:40本頁(yè)面
【正文】 , then the customer monthly revenue is prorated to a full month’s 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 domestic customer churn in the , this study does not include employee accounts, since churn for employee accounts is not of a problem or an interest for the pany. SURVIVAL ANALYSIS AND CUSTOMER CHURN Survival analysis is a clan of statistical methods for studying the occurrence and timing 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 mon features that are difficult to handle with conventional statistical methods: censoring and timedependent 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 risk 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 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 welldefined 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. It’s not necessary that every customer experience churn (customers who are yet to experience churn are called censored cases, while those customers who already churned are called observed cases). Typically, not only do we predict the timing of customer churn, we also want to analyze how timedependent covariates (. customers calls to service centers, customers change plan types, customers change billing options, and etc.) impact the occurrence and timing of customer churn. SAS/STAT has two procedures for survival analysis: PROC LIFEREG and PROC PHREG. The LIFEREG procedure produces parametric regression models with censored survival data using maximum likelihood estimation. The PHREG procedure is a semiparametric regression analysis using partial likelihood estimation. PROC PHREG has gained popularity over PROC LIFEREG in the last decade since it handles time dependent .However if the shapes of survival distribution and hazard function are known, PROC LIFEREG produces more efficient estimates (with smaller standard error) than PROC PHREG does. SAMPLING STRATEGY On August 16, 2020, a sample of 41,374 active highvalue customers was randomly selected from the entire customer base from a telemunications pany. All these customer were followed for the next 15 months. Therefore August 16, 2020 is the origin of time and November 15, 2020 is the observation termination time. During this 15month observation period, the timing of customer churn was recorded. For each customer in the sample, a variable of DUR is used to indicate the time that customer churn occurred, or for censored cases, the last time at which customers were observed, both measured from the origin of time (August 16, 2020). A second variable of STATUS is used to distinguish the censored cases from observed cases. It is mon to have STATUS = 1 for observed cases and STATUS = 0 for censored cases. In this study, the survival data are singly right censored so that all the censored cases have a value of 15 (months) for the variable DUR.
畢業(yè)設(shè)計(jì)相關(guān)推薦
鄂ICP備17016276號(hào)-1