2023 Technical Support Analysis

Tools used: Power BI

Project Objective:

This project involves a comprehensive analysis of our technical support system using data-driven insights to optimize ticket handling processes, enhance customer satisfaction, and improve overall operational efficiency. 

Key Observations:

1. Ticket Creation Patterns:

   • Peak Ticket Creation Time: The highest volume of tickets is created at 15:00, indicating a potential need for increased staffing or resource allocation during this period.

   • Off-Peak Time: The lowest volume of tickets is created at 14:00, suggesting a window for potential staff breaks or training sessions.

2. Weekly Ticket Distribution:

   • Weekday Dominance: 76.4% of tickets are generated on weekdays, highlighting the importance of maintaining robust weekday support.

3. Service Level Agreement (SLA) Compliance:

   • Response Time Compliance: 13.3% of tickets receive the first response later than the SLA requirement, pointing to a need for process improvements.

   • Resolution Time Compliance: 33.6% of tickets are resolved later than the SLA requirement, indicating significant room for enhancing resolution efficiency.

4. Resolution Time Analysis:

   • Average Resolution Time: It takes an average of 33.2 hours to resolve a ticket.

   • Channel-Specific Resolution: Tickets from chat are resolved the fastest, while phone tickets take the longest to resolve.

5. Ticket Topic Distribution:

   • Product Setup Inquiries: 27% of tickets are related to product setup. Improving manuals and FAQs could reduce the volume of such tickets, leading to increased efficiency and customer satisfaction.

6. Source and Satisfaction Rates:

   • Email Dominance: 53% of tickets originate from emails.

   • Satisfaction Rates: Email and chat tickets have an average satisfaction rate of 3.5, while phone tickets average at 3.4, indicating a marginally lower satisfaction rate for phone support.