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.
01
Data Collection and Feature Engineering
02
Model Development
03
Deployment of Model and MLOps Solution
04
02/2022
03/2022-04/2022
05/2022
