Voluntary churn occurs due to a decision by the customer to switch to another company or service provider, involuntary churn occurs due to circumstances such as a customer's relocation to a long-term care facility, death, or the relocation to a distant location.
Analysts tend to concentrate on voluntary churn, because it typically occurs due to factors of the company-customer relationship which companies control, such as how billing interactions are handled or how after-sales help is provided.
Gross attrition is the loss of existing customers and their associated recurring revenue for contracted goods or services during a particular period.
Financial institutions often track and measure attrition using a weighted calculation, called Monthly Recurring Revenue (or MRR).
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.
Customer attrition merits special attention by mobile telecom service providers worldwide.
This is due to the low barriers to switching to a competing service provider especially with the advent of Mobile Number Portability (MNP) in several countries.
Using data mining and software, one may apply statistical methods to develop nonlinear attrition causation models.
First, it should achieve good predictive performance, which is often measured using area under the ROC curve or top decile lift.
Current organizations face therefore a huge challenge: to be able to anticipate to customers’ abandon in order to retain them on time, reducing this way costs and risks and gaining efficiency and competitivity.