The Call Centre Dashboard is a Power BI project designed to provide actionable insights into the operations and performance of a call centre. By visualizing key metrics such as call volume, agent performance, customer satisfaction, and resolution times, this dashboard enables data-driven decision-making to improve overall efficiency and customer experience.
- Call Volume Analysis: Tracks daily, weekly, and monthly call volumes to identify peak hours and days.
- Agent Performance: Measures individual agent KPIs, including average handling time, resolution rates, and customer satisfaction scores.
- Customer Feedback: Visualizes customer sentiment trends and satisfaction ratings.
- Resolution Time: Highlights trends in first-call resolution and average resolution times.
- Trend Analysis: Compares historical data to detect patterns and anticipate operational demands.
- Improve call centre efficiency by identifying bottlenecks and performance gaps.
- Monitor and enhance agent performance.
- Optimize resource allocation during peak periods.
- Increase customer satisfaction by reducing resolution times and improving service quality.
The dashboard leverages data from:
- Call Logs: Includes timestamps, call durations, and outcomes.
- Agent Records: Contains agent IDs, shift schedules, and performance metrics.
- Customer Feedback: Extracted from surveys and sentiment analysis tools.
- Total calls received, answered, and abandoned.
- Average Handling Time (AHT).
- First Call Resolution (FCR) rate.
- Customer Satisfaction Score (CSAT).
- Call wait times and queue performance.
- Power BI: For data visualization and dashboard creation.
- SQL: For data extraction and transformation.
- Excel: For preprocessing and summarizing raw data.
- Ensure you have Power BI Desktop installed on your computer.
- Download the
Callcentre_dashboard.pbixfile. - Open the
.pbixfile in Power BI Desktop. - Connect to your data sources and refresh the dashboard.
- Navigate through the different tabs to explore various insights.
- Use filters to drill down into specific agents, dates, or call categories.
- Export visuals or reports for sharing with stakeholders.
- Identify the busiest hours and optimize staffing during peak periods.
- Pinpoint agents with high resolution rates and replicate their strategies.
- Reduce call abandonment by addressing queue bottlenecks.
- Enhance customer satisfaction by tracking and addressing common complaints.
- Integrate predictive analytics to forecast call volumes and resource needs.
- Automate email reports to stakeholders with key performance metrics.
- Expand data sources to include live chat and email support interactions.
Name: Jay Dilip Varma
Email: jay01varma@gmail.com
GitHub: jay01varma
LinkedIN: jay01varma