What is Churn Prediction? Definition of Churn Prediction, Churn Prediction Meaning and Concept
Churn prediction is a marketing technique that seeks to identify early those consumers who have a high probability of ceasing to be customers of the company.
The prediction of abandonment is an essential tool in the commercial policies of companies since it allows to identify in time which are the consumers who could stop buying the goods and services in the near future. The objective of this tool is to be able to identify the causes of abandonment in order to prevent it through campaigns, incentives and other retention measures.
Churn Prediction Source
Customers in most industries may decide to stop buying from a certain producer for various reasons such as: finding a better offer in the competition, disappointment with the quality of service, wanting to try other alternatives, momentary lack of ability to pay ( unemployment or other cause), etc.
The loss of clients is a serious problem for companies since getting new clients is usually very expensive. Indeed, retaining a customer costs between 5 and 15 times less than acquiring a new one. In order to efficiently manage their resources, companies must know what percentage of customers are susceptible to abandonment and how to stop their exit.
It is for the above that an analysis tool has been created especially focused on determining the clients that will potentially leave the company and the reasons for this abandonment. This is the origin of churn prediction.
Goal of Churn Prediction
The goal of churn prediction is to be able to identify customers who might churn and directly attack the causes of churn. This will allow a more efficient use of resources and a greater projection of life in the market.
Churn prediction methods
The prediction of abandonment is usually based on surveys and econometric models that would make it possible to identify the possible causes of abandonment and the factors that influence them.
Then an intervention model is proposed that would seek to reflect how a certain policy or measure affects the probability of abandonment.
So, for example, the churn prediction model can be based on historical customer churn data over 10 years. Possible causes could include: lack of information, constant price increases, perception of low quality, entry of competitors with better offers, poor relationship with the client, etc.
An intervention model, meanwhile, will propose measures to reduce the causes of abandonment. Thus, for example, if one of the causes is the poor quality of the service, a policy would be to improve the attention of the operators, monitor customers, respond to complaints in less time, etc.