https://github.com/abhijeet107/call-center-performance
"Call Center Performance Report"
https://github.com/abhijeet107/call-center-performance
excel excel-dashboard excel-data-analytics excel-powerquerym powerbi
Last synced: 4 months ago
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"Call Center Performance Report"
- Host: GitHub
- URL: https://github.com/abhijeet107/call-center-performance
- Owner: Abhijeet107
- Created: 2025-01-08T18:22:47.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-08T18:40:02.000Z (over 1 year ago)
- Last Synced: 2025-03-15T18:47:07.953Z (about 1 year ago)
- Topics: excel, excel-dashboard, excel-data-analytics, excel-powerquerym, powerbi
- Homepage:
- Size: 934 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
## Title
Call Center Performance Report
## Dashboard
## Project Objective
To evaluate the performance of a call center by analyzing key metrics such as call volume, agent performance, issue resolution rates, and customer satisfaction. The objective is to identify areas for improvement in operational efficiency and customer service.
## Dataset Used
### Key Fields:
Total calls (Incoming/Handled/Rejected)
Agent details (Call duration, calls resolved)
Call topics (e.g., Technical Support, Streaming, Payment, etc.)
Time period (01-01-2021 to 31-03-2021)
Customer satisfaction rates
Call status (Resolved/Not Resolved)
## KPIs
Total Calls: 5,000
Total Agents: 8
Total Calls Answered: 4,054
Total Calls Rejected: 946
Percentage of Calls Answered: 81.1%
Percentage of Calls Rejected: 18.9%
#### Resolution Rate:
#### Resolved: 3.6K
Not Resolved: 1.4K
Call Topics Distribution:
#### Streaming: 1,022 calls
#### Technical Support: 1,019 calls
#### Payment-related: 1,007 calls
#### Admin Support and Contract-related: 976 calls each
Top Agent by Calls Answered: Jim
Average Satisfaction Rate: Dan
## Process
Data Collection: Gathered data on call activities, agent performance, and resolution rates from the call center systems.
Data Cleaning: Processed and categorized data by call topics, resolution status, and agent performance.
Visualization: Created dashboards to provide a clear view of call center operations using bar charts, pie charts, and line graphs.
Analysis: Evaluated call distribution, agent efficiency, and resolution effectiveness.
## Project Insights
### Call Volume:
January had the highest call volume (1,772), followed by February (1,616), with a decline in March.
Streaming and Technical Support accounted for the most significant share of calls.
### Resolution Performance:
72% of calls were resolved, while 28% remained unresolved.
Unresolved calls were distributed across agents, with Joe and Stewart having the highest unresolved rates (11.6% each).
### Agent Performance:
Jim answered the most calls (536) and had a high resolution rate.
Dan had the highest customer satisfaction rating but answered fewer calls (523).
Agents’ call durations were consistent, ranging from 273 to 295 minutes.
### Call Topics:
Streaming issues were the most common (20.4%), followed by Technical Support (20.38%).
## Conclusion
The dashboard highlights several operational insights and opportunities for improvement:
### Agent Efficiency:
Jim is the most effective agent in terms of calls answered.
Training other agents to handle more calls and resolve issues efficiently could improve overall performance.
### Resolution Focus:
Addressing the causes behind unresolved calls (1.4K) can enhance the resolution rate further.
### Call Topic Insights:
Streaming and technical support are the primary focus areas and may require additional resources or dedicated teams.
### Call Trends:
A decrease in calls from January to March may indicate seasonal fluctuations or improved issue resolutions reducing repeat calls.
## Recommendations:
Increase resources for high-demand topics like Streaming and Technical Support.
Implement regular training sessions to standardize agent performance.
Conduct a deeper analysis of unresolved calls for systemic improvements.