Customer Analytics (Churn, Life-Time Value & Retention)

Why Predicting Customer Churn is Important?

Predicting that particular customers are at a high risk of churning, while there is still time to take necessary action and do something about it; represents a huge additional potential revenue source for every business. Besides the direct loss of revenue that results from a customer abandoning the business, the costs of initially acquiring that customer may not have already been covered by the customer’s spending to date. Furthermore, the fact that it is difficult and expensive to acquire a new customer than it is to retain a current paying customer.

In order to succeed at retaining customers who would otherwise abandon the business, organizations must be able to:

1. predict in advance which customers are going to churn through churn analysis
2. know which actions will have the greatest retention impact on each particular customer.

A churn model is a tool provides insights and outputs that drive decision making across an organization. 

A well-designed model may guide to a wide range of decisions. For example, some common use cases for a churn model are:

• Measuring feature impacts on the likelihood of churn in order to understand why customers choose to leave
• Creating churn risk scores that can indicate who is likely to leave
• Predicting the probability of churn and using it to flag customers for upcoming actions
• Integrating outputs with internal apps, such as a customer call center, to provide relevant real-time churn risk information
• And many more…