Churn prevention

Machine learning solutions

Churn prevention logo

Data analytics allows us to understand the reasons why our clients are not loyal.

Knowing these reasons, we can take actions to retain them.

Objectives

Churn prevention has became one of the biggest challenges for all companies, specially for banks, insurance and telecommunications companies. Indeed, it is much cheaper to retain existing clients than to acquire new ones.

However, it is not easy to detect the dissatisfaction of a client, and many times they just stop using our services without any previous warning. Some of the factors that influence churn are the following:

Therefore, 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.

Advanced analytics

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

Benefits

IDENTIFY DISSATISFIED CUSTOMERS

Determine the probability of desertion identifying your dissatisfied clients.

ANALYZE REASONS OF DISSATISFACTION

Study the reasons of abandonment taking into account the characteristics of the clients.

TAKE ACTION TO PREVEN CHURN

Create solutions to recover their loyalty before it is too late.

INCREASE BENEFITS

Retain customers, increase sales among current clients and improve customers satisfaction.

Implementation

Advanced analytics allows companies to manage large amount of data and find early signs of desertion before it is too late.

Churn prevention picture

Neural Designer uses machine learning to create predictive models capable of assessing the risk of churn of clients.

It allows us to easily set the analyze the customer features that are more relevant for the churn and design the neural networks that best fits the case of study.

Finally, Neural Designer also provides 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.

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Related examples: