Financial services such as banking and insurance use applications of predictive analytics for churn modeling, because customer retention is an essential part of most financial services' business models. Other sectors have also discovered the power of predictive analytics, including retailing, telecommunications and pay-TV operators. One of the main objectives of modeling customer churn is to determine the causal factors, so that the company can try to prevent the attrition from happening in the future. Some companies want to prevent their "good" customers from deteriorating (e.g., by falling behind in their payments) and becoming less profitable customers, so they introduced the notion of partial customer churn.