Project Highlights

• Data Ingestion and Handling: Used Dataflows Gen2 to seamlessly ingest raw call logs into the Microsoft Fabric Lakehouse, organizing both clean and raw data.
• Machine Learning Models: Developed custom models in Fabric Notebooks using Python (PySpark, Pandas, Scikit-learn) to predict call outcomes, monitor agent performance, and highlight service bottlenecks.
• Agent Performance Tracking: Built Power BI dashboards to measure KPIs like response times, resolution rates, and customer satisfaction scores.
• Customer Behaviour Analysis: Designed interactive reports showing customer behaviour, call volume trends, and recurring issues, enabling proactive improvements.
• Operational Monitoring: Automated pipelines to refresh datasets, clean data, re-run ML models, update scores, and refresh dashboards in real-time.

Business Impact

• Improved Customer Experience: Predicted call outcomes and flagged high-risk cases for faster resolution.
• Agent Productivity: Identified top-performing agents and areas for coaching to boost efficiency.
• Bottleneck Detection: Pinpointed recurring delays and inefficiencies in call flows, streamlining operations.
• Data-Driven Strategy: Empowered management with real-time insights into performance, enabling smarter workforce planning.

Tools & Technologies

• Microsoft Fabric: Lakehouse, Dataflows Gen2, Pipelines, Notebooks
• Python: PySpark, Pandas, Scikit-learn
• Power BI: Advanced visualization and storytelling

Outcome

The result is a scalable AI-powered solution that allows call centers to predict outcomes, optimize staff performance, and enhance customer satisfaction — all backed by real-time, automated analytics. By unifying data, machine learning, and BI in one platform, this project shows how call centers can move from reactive to proactive customer engagement.