Database marketers are often tasked with stemming the tide of customer attrition as mature markets fizzle and new markets overtake existing ones. They use models as a key component in their marketing programs to make headway against a declining customer database. For example, in the financial services and telecommunications industries, database marketer use attrition models to identify individuals who are likely to cancel their credit cards and cellular services, respectively, and then develop campaigns targeted to those individuals intended to excite rather than cancel activity. Logistic regression analysis is the standard method for building an attrition model to explain and predict a binary target variable - defined by attriters and nonattriters – based on static variables (e.g., age and gender of customer) and time-series variables (e.g., January through December balances due). Specifically, the model provides an individual’s likelihood of attrition in a prescribed time period in the future, e.g., one month prior to the cancellation of the product or service. The time-series data must be in correct relative position with respect to the prescribed time period before the data analyst begins model building. This article discusses the working concepts for building an attrition model by reviewing the basics of logistic regression analysis, presenting an explicit definition of the attrition model, and providing the SAS-code program for aligning times-series data, which should be a welcomed entry in the tool kit of data analysts who frequently work on the attrition problem