Predict and
Prevent Churn


Predict the probability of customer churn using ML techniques

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.
The Problem Canvas
  • The column names in the dataset are not consistent
The Data Question
  • Prevent attrition by acting proactively on at-risk customers and improve marketing ROI

Solution Canvas


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.

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RubiStudio – Data Exploration, Data Joiner, Merger, Statistical Hypothesis, Code Fusion
RubiML – Classification models
Rubisight – Story and Dashboard

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Business reporting
Collaboration Platforms
Business Reporting
Machine Learning
Domain Knowledge

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The dataset contains data containing information like customers details, their credit score, location country, estimated salary, credit card status and balance.

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Business Impact
  • Prevent attrition by acting proactively on at-risk customers
  • Improve marketing ROI
  • Predict expenses on customer retention so as to avoid initial investment
  • Analyze the impact of customer retention on the company expansion plans
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