The analysis of a company's data allows us to understand why some of the clients are not loyal.
Knowing these reasons, we can take action to retain them.
The data science and machine learning platform Neural Designer helps banks, insurance, telecommunications, and retail companies to prevent the attrition of their customers and increase their loyalty.
Churn prevention has become one of the biggest challenges for all companies. Indeed, it is much cheaper to retain existing clients than to acquire new ones.
However, it is not easy to detect a client's dissatisfaction, and they often stop using our services without any previous warning.
By harnessing the power of big customer data sets, companies can develop predictive models that enable proactive intervention before it's too late.
Some of the factors that influence churn are the following:
The objective is to develop loyalty programs and retention campaigns to keep as many customers as possible.
To do that, it is essential to develop new methods that can infer who our dissatisfied clients are.
Some of the many benefits of using machine learning for customer churn prevention are the following.
Identify the reasons for attrition and fix them.
Prevent unsatisfied customers from harming the image of your company.
Reduce the loss of revenue from customer churn and save customer acquisition costs.
It allows us to find the factors that are more relevant for the churn and design the neural networks that best fits the case of study.
The following graph illustrates an example of a neural network for churn prevention.
The data science and machine learning platform Neural Designer guides you through this process so that you can focus on your business and not on the details behind machine learning.
Machine learning allows companies to find early signs of desertion before it is too late using the available data.
Neural Designer uses artificial intelligence to discover the reasons for dissatisfaction, assess the risk of churn, and improve clients' loyalty.