Anyone who knows when a customer wants to switch to another provider can take countermeasures in good time. At this point, a churn prediction model helps to prevent customer churn and to ensure customer loyalty as the primary goals of a sustainable business strategy.
About the model
A churn prediction model is helpful in identifying customers who want to cancel a contract or switch to a competitor, for example. With the help of artificial intelligence it is possible to recognize the impending loss of customers at an early stage and to defend against it with countermeasures.
Intelligent algorithms are able to distinguish annoyed or even ironic e-mails from friendly or factual e-mails. However, customer satisfaction can also be derived from the analysis of the purchase history. If the correct data is combined, the probability of a change can be determined precisely.
For companies, this therefore means that they must increasingly rely on data evaluation. This makes it possible to segment the customers according to suitable criteria and to individualize the address in order to address a customer at the optimal moment. At this point, the model is helpful in determining when a customer is thinking about changing provider.
Depending on the products or services offered, the calculation of the churn rate pays off. The rate is usually determined as the quotient of the number of total customers and the number of customers migrating. Above all, the model is worthwhile for companies that sell goods at cyclical intervals. Examples include automobile manufacturers or mobile phone providers that rely on a subscription structure.
A churn prediction model can be used to measure the impact of individual offers or services on customers. With the help of the predicted probability of churn, individual discount campaigns can be switched automatically. By analyzing the user data, customer understanding can be improved accordingly.
Churn prediction pays off
The costs of acquiring new customers exceed the costs of maintaining existing customers by far. The churn prediction model is therefore worthwhile because the causes of customer churn can be determined and not only the loss of individual customers can be prevented.
The basis for successful modeling is the combination with intelligent customer relationship management. Fast action and appropriate preparation are important when it comes to anticipating customer churn. This is the only way to implement measures to keep customers.
In the long term, the quality of the company’s own product range can be improved and adjusted to the potential customers. The churn prediction model is therefore a central component of a customer-oriented business strategy.
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