Prediction of abandonment


The 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 abandonment prediction is an indispensable tool in the commercial policies of companies since it allows to identify in time which are the consumers that could stop buying 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.

Origin of the churn prediction

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, disillusionment with the quality of service, wanting to try other alternatives, momentary lack of ability to pay ( unemployment or other cause), etc.

Losing customers is a serious problem for companies as getting new customers is often very expensive. Indeed, retaining a customer costs between 5 and 15 times less than getting a new one. In order to efficiently manage their resources, companies must know the percentage of customers susceptible to abandonment and how to stop their departure.

It is for this reason 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 the abandonment prediction.

Aim of the churn prediction

The goal of churn prediction is to be able to identify customers who might be leaving the company 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.

Dropout prediction methods

The dropout prediction is usually based on surveys and econometric models that would allow the possible causes of dropout and the factors that influence them to be identified.

Then an intervention model is proposed that would seek to reflect how a certain policy or measure affects the probability of abandonment.

Thus, 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, little 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, follow up with customers, respond to complaints in less time, etc.

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