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Churn prevention using advanced analytics

By Pablo Martin, Artelnics.

One of the problems that currently worries most companies is to keep their customers. However, it is not easy to detect the dissatisfaction of a client sometimes they usually just stop using our services without any previous warning.

Advanced analytics

For this reason, it is essential to develop new methods that are useful to discover our dissatisfied clients. Nowadays, big data is opening new opportunities in the analysis of great amount of data. Advanced analytics and, particularly, neural networks, provide us with the tools to determine the probability that a given customer leaves our company.

The final objective of these techniques is to develop loyalty programs and retention campaigns in order to keep as many customers as possible in our company.


Determine the probability of desertion identifying your dissatisfied clients.
Study the reasons of abandonment taking into account the characteristics of the clients.
Prevent churn
Create solutions to recover their loyalty before it is too late.
Retain customers, increase sales among current clients and improve customers satisfaction.

It is much cheaper to retain clients than to acquire new ones. For that reason, retaining customers has became one of the biggest challenge for all companies, especially for banks. Advanced analytics methods allow companies to manage the large amount in order to find early signs of desertion before it is too late.

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Neural Designer is an accurate software capable of handling huge amounts of data in a precise way, performing complex mathematical processes and showing them in a simple way.

Neural Designer uses neural networks to define a predictive model capable of assessing the risk of churn of a client. It will allow us to easily set the analyze the customer features that are more relevant for the churn and design the the neural networks that best fits the case of study.

Finally, Neural Designer will also provide you with the tools to evaluate if the trained predictive model is performing well on new data and is suitable to be used for real customer campaigns.

Related solutions:

> Risk assessment.
> Customer segmentation.
> Sales forecasting.