Customer Churn is big problem for enterprises. Churn is normal due to multiple reasons however when customer churn is more than new business for a particular year. It can have negative impact on revenue. There are many ways to measure and predict customer churn using scientific methods and predictive modelling. This article explains that how organization can build customer churn model and focus to minimize the churn based on mathematical and statistical approaches by using customer pattern of purchase, usage, engagement, and customer call center data. Building a customer churn model may take some time to analyze the data, model to use and sometime model takes some time to mature based on sales department input of model outcome. Result of Customer Churn model are very encouraging if it is built correctly and being natured by the time. This article will help to many enterprises and data scientist to have a systematic way to approach a customer churn model with or without a scripting language knowledge.