The most practical way to build knowledge on customer behavior is to produce scores that explain a target variable, such as churn, appetency, or upselling. The score is computed by a model using input variables that describe customers; for example, their current subscription, purchased devices, consumed minutes, and so on. The scores are then used by the information system for things like providing relevant personalized marketing actions.
A customer is the main entity in most of the customer-based relationship databases; getting to know the customer's behavior is important. The customer's behavior produces a score in relation to the churn, appetency, or upselling. The basic idea is to produce a score using a computational model, which may use different parameters, such as the current subscription of the customer, devices purchased,...
