Last Updated on by Kumar Raja

Data Science Predictive Modelling For Churn Prevention

The process of Predictive Modelling in Data Science is all about making accurate predictions by training the data model with data sets of known results. This trained model can be used to accurately predict values for different or new data. In the data modelling process of Predictive Modelling the obtained result are nothing but predictions that represent a probability of the target variable based on estimated significance from a set of input variables.

Predictive Modelling technique helps the businesses in predicting which customer is more like to leave, whether he/she will respond to a solicitation, & it also helps in accurately calculating the credit risk, etc. It also helps the business in analyzing their customer data & accurately predicts how much revenue a customer will generate over the next year. Owing to the numerous prominent applications of Predictive Modelling, enterprises are relying heavily on this technique in Data Science. 

If you are interested in becoming a Data Scientist then having intense knowledge & hands-on expertise in working on Predictive Modelling techniques is very crucial. Master skills in Predictive Modelling & other analytics skills in Data Science by being a part of Kelly Technologies advanced Data Science Training In Hyderabad program.

Now, let’s take a look at the Churn Prevention Model in Predictive Modelling.

Churn Prevention With Predictive Modelling-

The use of Churn Prevention model in predictive modelling is mainly to predict which customers are more likely to get disassociated with your business & what could be the possible reasons that are leading them to get disassociated.

This model is usually very efficient cost effective & as the amount that has to be spent on retaining an existing customer is far lower than the amount that has to be spent of acquiring new one.

By making the most out of the Predictive Models enterprises are now in a position to harness the power of Big Data the full extent. Thee predictive models helps the enterprises in making proactive interventions that lets them retrieve their customers at the right time.

The Churn Prevention technique in Predictive Modelling delivers results based on different attributes like Socio-Demographic Variables, Products Contracted, Engagement Variables, Product/Service Usage, Stationary Variables & Competitors Variables.

Thereby analyzing all these aspects enterprises will be in a better position to understand the reasons that are leading to churn. Based on the reasons they can  take appropriate steps that will help them to retain the customers.

Get know more in-depth about Churn prevention with Predictive Modelling by being a part of top-notch & hands-on experts driven Data Science training program.

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