Case Study
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.
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.
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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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