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Case Study

Customer Churn

Our customer churn initiative has successfully addressed a significant challenge faced by our banking client: client retention following loan repayment. Recognizing the customers' desire for a seamless transition and continued engagement with their clients, we developed an advanced machine learning model that aimed to identify the most suitable next-best product offerings.

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By harnessing the power of comprehensive behavior and demographic data, we revolutionized our approach to customer retention within the banking sector. Our model resulted in a remarkable reduction in churn rates, empowering our customer to retain 20% more customers after incorporating our model results in the implementation of our model's insights.


While having few tagged data was an issue in the past, with most state-of-art computer vision models, that is not the case.



Our model uses a vast amount of customer data, including website visits, customer service interactions, and transaction history, to gain a thorough understanding of each individual's journey. To ensure ongoing model performance and prevent any inaccuracies over time, we have implemented a comprehensive MLOps System, that continuously improves and monitors the model, reducing the likelihood of deviations or errors in its predictions.

Time Line



Data Collection and Feature Engineering



Model Development



Deployment of Model and MLOps Solution




Key Insights

Leveraging website visit data proved to be highly pertinent for our solution. By incorporating information from diverse systems, we witnessed a significant enhancement in the accuracy and effectiveness of our churn models.

Building Churn Solutions on top of a robust data lake that offers a unified view of the customer has emerged as a critical factor in developing superior churn models. We believe that there is no Data Science without strong Data Engineering fundamentals.

The synergy between churn models and cross-sell models holds significant potential in providing actionable insights for customers. By combining these two models, we can not only identify customers with a high churn probability but also suggest specific cross-selling opportunities to mitigate the risk.