In today’s technology-driven world, businesses of all sizes rely heavily on tech infrastructure for smooth operations. Efficient technical support is crucial to maintain uptime, solve technical issues, and ensure customer satisfaction. This dataset provides real-world data from a technical support center, allowing analysts to explore, assess, and improve technical support strategies. It offers an excellent opportunity for data enthusiasts to hone analytical skills while gaining practical insights into technical support workflows.
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Key Analysis Areas:
Ticket Volume and Patterns:
Explore daily, weekly, and monthly trends in ticket volumes.
Identify differences in ticket volume between workdays and weekends.
Examine peak ticket creation times by work hours versus after hours.
Investigate seasonal impacts or unexpected spikes in ticket generation.
Ticket Content and Resolution:
Analyze recurring issues by ticket topic and department.
Evaluate first response and resolution times, comparing them against Service Level Agreements (SLAs).
Review performance across multiple support channels like chat, phone, email, and social media.
Investigate regional or product-specific issues based on ticket submissions.
Advanced Metrics and Customer Insights:
Assess customer satisfaction trends over time, considering changes in ticket volume or issue complexity.
Evaluate the impact of training programs or new tools introduced into the technical support workflow.
Compare the effectiveness of proactive versus reactive support measures by resolution times and customer feedback.
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