Explore our Solution on Customer Churn.

BUSINESS CHALLENGE:

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.

SOLUTION:

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.

RESULT:

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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