Explore our Solution on Customer Churn.


In Telecom companies it is more expensive and difficult to get new customers after losing their existing customers. Loss of existing customer to our competitor creates a huge loss to the organization. So analyzing the customer who is planning to move and by providing the customer a better service is the ideal plan. So the objective is to predict customer churn. So, Simple Analytics built a Machine Learning model with the data of customer usage pattern and transaction details to predict whether the customer is likely to churn or not.


Simple Analytics collected all related data are from the client. Data like customer usage pattern and transaction details were collected. Simple Analytics worked closely with the client’s team and did a series of analysis in understanding customer behaviour.

After many analysis Logistic regression technique is used to build a Machine Learning model. Machine learning model works with 50 variables and predicts churners and non-churners with a better accuracy.

In the essence, Simple Analytics predictive model come across various significant factors that make an impact on churn rate. Simple Analytics advised a client to concentrate on these factors in order to reduce their churn percentage.


Simple Analytics predicted many key factors that determine the satisfaction of the customer. So the organization must focus on these factors in order to maintain a good customer service relationship and to avoid customer churn.

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