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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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Overview

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.

Challenge

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

Problem

Approach

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

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Data Collection and Feature Engineering

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Model Development

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Deployment of Model and MLOps Solution

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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.