Customer loyalty and retention are as important for banking institutions as for any other sector. Customer churn refers to a situation where an existing customer leaves the bank. It is always economical for banks to satisfy and retain existing customers than acquiring new ones. Prediction and prevention of churn are important for the banking institutions to know the customer sentiment to retain loyal customers and to prevent churning in time to lose any potential business. Today, major banks use data analytics and machine learning on customer data to build effective churn prediction models and prevent the clients that are likely to leave their affiliation with the bank.
Rubiscape uses ML techniques to create a prediction model to help customer churn prediction and prevention experts to understand the likelihood of a customer churn. This helps them understand intricacies of voluntary and involuntary churn and also the need to continuously improve customer satisfaction.
RubiStudio – Data Exploration, Data Joiner, Merger, Statistical Hypothesis, Code Fusion
RubiML – Classification models
Rubisight – Story and Dashboard
The dataset contains data containing information like customers details, their credit score, location country, estimated salary, credit card status and balance.