Q1 2025 Customer Support Service Level Review

Quarterly analysis of customer support performance metrics focusing on service quality, response times, and team efficiency to ensure optimal customer satisfaction and operational excellence.

Report Objective

Track and analyze critical customer support metrics across Q1 2025, focusing on service level agreements (SLAs), customer satisfaction, and support team performance. This report helps identify trends, potential areas for improvement, and resource allocation needs.

Response Time and Ticket Volume Trends

Line chart showing weekly first response times against ticket volume

Questions to Consider:

2024-12-012025-01-012025-02-01week_date1000.02000.03000.0first_response_time_minutes vs. ticket_volumefirst_response_time_minutesticket_volumeHow are First Response Times Trending with Ticket Volume?Response times remain stable despite 20% increase in ticket volume
  • What is the correlation between ticket volume and response time?

  • Are there specific weeks where response times spike?

  • How does current performance compare to SLA targets?

Ticket Category Distribution and Resolution Times

Bar chart comparing ticket volumes and resolution times by category

Questions to Consider:

  • Which categories show disproportionate resource requirements?

  • Are there opportunities to reduce resolution times in high-volume categories?

  • How does category distribution align with support team structure?

Technical IssuesBillingAccount AccessFeature Requestcategory050,000100,000150,000sum(ticket_count)sum(ticket_count)Which Support Categories Require Most Resources?Technical Issues account for 35% of tickets with longest resolution times

Team Performance Analysis

Scatter plot of resolution rates vs tickets resolved by team

Questions to Consider:

2004006008001,000sum(tickets_resolved)100.0%200.0%300.0%sum(resolution_rate)Technical SupportGeneral SupportBilling SupportPremium SupportHow Do Support Teams Compare in Efficiency?Technical Support team shows highest resolution rate despite high volume
  • Which teams demonstrate best practices in balancing volume and quality?

  • Are there significant performance variations within teams?

  • Where might additional training or resources be most beneficial?

Areas for Additional Focus