Predict customer churn in the retail sector
BUSINESS CHALLENGE
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
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.
RESULTS & ROI
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.
TECHNOLOGY
DATA PLATFORM
Databricks for scalable data processing and ML workflows, integrating data from transactional, customer profile, and external sources.
SERVING
MLflow for model management and deployment; churn prediction results served to marketing and CRM systems for actionable insights.
AI
Seamless integration with existing retail data pipelines and operational systems for automated churn predictions.