CASE STUDY — RETAIL
Customer Churn
Predict customer churn in the retail sector
Sector:
Retail
Focus:
Customer Churn Prediction
BUSINESS CHALLENGE
High churn rates increasing costs and reducing revenue
High customer churn rates in the retail sector reduce revenue and increase costs associated with acquiring new customers. The lack of insights into customer churn drivers hinders targeted interventions and proactive engagement strategies.
SOLUTION IMPLEMENTED
An ML model predicting churn and enabling targeted retention
Developed a ML model to predict customer churn based on historical transactional, behavioural, and demographic data, using MLOps principles. The solution enables targeted retention strategies by identifying at-risk customers, empowering data-driven decision-making for marketing and customer service teams.
TECHNOLOGY
Built to predict, deploy, and act on churn signals at scale
DATA PLATFORM
Azure Databricks
Enables scalable data processing and ML workflows, integrating data from transactional, customer profile, and external sources.
SERVING
MLflow
Manages model deployment, serving churn prediction results to marketing and CRM systems for actionable insights.
INTEGRATION
RETAIL DATA PIPEPLINE
Ensures seamless integration with existing retail data pipelines and operational systems for automated churn predictions.
Results & ROI
What changed once the model went live
RETENTION
Improved customer retention rates by proactively addressing churn drivers.
COST SAVINGS
Reduced costs associated with acquiring new customers by focusing on retaining existing ones.
INSIGHTS
Delivered actionable insights for customer engagement strategies, enhancing overall business performance.
