**These datasets are for educational purposes only. Any misuse for illegal or unethical activities is strictly prohibited. All generated data is fictional and has no real-world validity.
This tool generates synthetic call center datasets for developers, data analysts, and AI/ML researchers. The data is perfect for application testing, process optimization, performance analysis, and machine learning training. All datasets are randomly generated and not real, ensuring safe experimentation.
Contains details about call center agents, including their work type, department, and contact details.
| Agent ID | Name | Agent Type | Phone Number | Department | |
|---|---|---|---|---|---|
| UUID1234 | John Doe | Full-time | johndoe@email.com | +1 555-678-9012 | Support |
| UUID5678 | Jane Smith | Part-time | janesmith@email.com | +1 555-345-6789 | Sales |
| Customer ID | Interaction Date | Agent ID | Issue Type | Status |
|---|---|---|---|---|
| UUID9876 | 2024-02-10 | UUID1234 | Billing Inquiry | Resolved |
| UUID5432 | 2024-01-28 | UUID5678 | Technical Support | Escalated |
| Call ID | Customer ID | Agent ID | Call Duration (mins) | Call Type | Issue |
|---|---|---|---|---|---|
| UUID2024 | UUID9876 | UUID1234 | 15 | Inbound | Technical Support |
| UUID4040 | UUID5432 | UUID5678 | 8 | Outbound | Billing Inquiry |
| Feedback ID | Customer ID | Agent ID | Rating | Feedback |
|---|---|---|---|---|
| UUID9999 | UUID9876 | UUID1234 | 5 | Great support! Very helpful. |
| UUID7777 | UUID5432 | UUID5678 | 3 | Had to wait too long for assistance. |
| Escalation ID | Customer ID | Agent ID | Issue Type | Escalation Reason | Resolution Status |
|---|---|---|---|---|---|
| UUID5678 | UUID9876 | UUID1234 | Technical Support | Agent not skilled | Pending |
| UUID2345 | UUID5432 | UUID5678 | Billing Inquiry | Customer request | Resolved |
Contains data on call center agent performance, including calls handled, average duration, resolution rate, and satisfaction score.
| Agent ID | Name | Calls Handled | Average Call Duration (mins) | Resolution Rate (%) | Satisfaction Score |
|---|---|---|---|---|---|
| UUID1234 | John Doe | 150 | 12 | 89.5 | 4.8 |
| UUID5678 | Jane Smith | 90 | 9 | 76.3 | 4.2 |
Contains information about agent work shifts, including start and end times and break duration.
| Agent ID | Shift Start | Shift End | Break Duration (mins) | Total Shift Duration (hrs) |
|---|---|---|---|---|
| UUID9876 | 2024-02-15 08:00 | 2024-02-15 16:00 | 45 | 8 |
| UUID5432 | 2024-02-15 10:00 | 2024-02-15 18:00 | 30 | 8 |
Contains data on call queue times, including wait time, queue length, and call duration.
| Call ID | Customer ID | Wait Time (mins) | Queue Length | Call Duration (mins) |
|---|---|---|---|---|
| UUID1001 | UUID2001 | 5 | 10 | 12 |
| UUID1002 | UUID2002 | 8 | 15 | 9 |
Contains details on outbound sales calls, including lead status and sales conversion.
| Call ID | Lead ID | Agent ID | Lead Status | Sales Conversion | Lead Source |
|---|---|---|---|---|---|
| UUID3001 | UUID4001 | UUID5001 | Converted | True | Web |
| UUID3002 | UUID4002 | UUID5002 | Not Interested | False | Cold Call |
Contains customer satisfaction survey results, including ratings and comments.
| Survey ID | Customer ID | Agent ID | Survey Date | Rating | Comments |
|---|---|---|---|---|---|
| UUID6001 | UUID7001 | UUID8001 | 2024-02-14 | 5 | Excellent support! |
| UUID6002 | UUID7002 | UUID8002 | 2024-02-13 | 3 | Agent was helpful, but wait time was long. |
Contains staffing levels at different times, including available agents and service levels.
| Time of Day | Available Agents | Calls in Progress | Average Handle Time (mins) | Service Level (%) |
|---|---|---|---|---|
| 2024-02-14 09:00 | 25 | 15 | 8 | 95.2 |
| 2024-02-14 13:00 | 30 | 20 | 7 | 92.8 |
Contains data on abandoned calls, including abandonment reason and duration before abandonment.
| Call ID | Customer ID | Abandoned | Abandonment Reason | Call Duration Before Abandonment (mins) |
|---|---|---|---|---|
| UUID9001 | UUID10001 | True | Long Wait Time | 4 |
| UUID9002 | UUID10002 | False | None | None |
With proper analysis, you can extract insights like:
Absolutely. You can track metrics like average handling time (AHT), first call resolution (FCR), and number of calls handled per shift to evaluate agent productivity and efficiency.
You can analyze CSAT scores alongside call resolution status, agent ID, or queue time to identify patterns that impact satisfaction. This helps highlight training needs or process bottlenecks.
Yes. Many call center datasets include timestamps and resolution flags, making it easy to calculate SLA compliance rates, such as percentage of calls answered within 30 seconds or resolved within 24 hours.
Abandonment data can reveal operational issues like long wait times or understaffing. You can analyze abandonment rates by time of day, queue length, or agent availability to improve call handling processes.
Yes. These datasets are ideal for building dashboards that visualize KPIs such as average wait time, call volume trends, resolution rates, agent performance comparisons, and customer satisfaction scores.
By linking shift schedule data with call handling and performance stats, you can identify correlations between time of day and agent efficiency, which helps optimize scheduling for better outcomes.
Yes. The escalated issues dataset allows you to analyze which types of calls get escalated, how quickly they are resolved, and whether certain agents or departments experience higher escalation rates.
Definitely. Outbound call data includes call outcomes (e.g., sale made, follow-up needed), durations, and customer interactions. You can use this data to assess conversion rates, script effectiveness, and agent performance.
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