AI/ML solution focused on customer churn/retention rate

Challenge:
Banking firm faced rising customer churn impacting their revenue and growth. They had vast amounts of customer data stored in their enterprise data warehouse but lacked predictive analytics capabilities to identify at-risk customers and implement timely retention strategies. Manual analysis was slow and reactive, limiting marketing and customer service effectiveness. 

Our Solution:
We developed an end-to-end AI/ML solution using Azure Machine Learning Studio to build and train churn prediction models. Our team extracted customer behavioral, transaction, and demographic data from PrimeBank’s data warehouse via secure pipelines. The model incorporated historical churn data and key features to score customers based on their likelihood to churn. Results were integrated into dashboards. 

 Key Results  

• Achieved a 20% reduction in customer churn within six months. 
• Enabled data-driven customer segmentation for personalized retention offers. 
• Improved marketing ROI by 30% through targeted outreach to high-risk segments. 
• Automated churn prediction workflows reduced manual effort and sped decision-making.