Azilen Technologies Pvt. Ltd.
Jan 09, 2024
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Service categories
Service Lines
Software Development
Domain focus
Banking & Financial Services
Challenge
Large banks, finance services and insurance firms manage lakhs of customers, who bring in thousands of crores worth of assets with them.
It is essential for these businesses to engage customers by providing them with new products by upselling to existing customers or increase customer retention or acquire new customers to continue to generate more revenue.
Large banks, finance services and insurance firms manage lakhs of customers, who bring in thousands of crores worth of assets with them.
It is essential for these businesses to engage customers by providing them with new products by upselling to existing customers or increase customer retention or acquire new customers to continue to generate more revenue.
Solution
Deep learning techniques for class Nameification give better results. We have used an artificial neural network to derive our prediction. The advantages to go with deep learnings include getting rid of the feature selection process and letting the model derive data points that are of utmost importance in making the prediction.
Deep learning techniques for class Nameification give better results. We have used an artificial neural network to derive our prediction. The advantages to go with deep learnings include getting rid of the feature selection process and letting the model derive data points that are of utmost importance in making the prediction.
Results
The model was tested on 2000 customer data where in the model was given all these customer data points mentioned above, the model predicted whether the customer would churn or not and it was analyzed against the actual data point for churn.
86% accuracy was achieved, when the model was tested.
The model was tested on 2000 customer data where in the model was given all these customer data points mentioned above, the model predicted whether the customer would churn or not and it was analyzed against the actual data point for churn.
86% accuracy was achieved, when the model was tested.