Technical Support Operations Analysis & Optimization

This project focuses on analyzing and optimizing the performance of a technical support center by identifying operational inefficiencies across ticket handling, SLA compliance, and agent performance. The objective was to uncover bottlenecks impacting service quality and provide actionable recommendations to improve efficiency and customer experience.

The support operations team was facing multiple challenges:

High ticket volumes during peak months and after-hours

Significant SLA breaches in response and resolution times

Uneven performance across support agents

Increasing backlog due to unresolved and in-progress tickets

These issues were affecting service quality, customer satisfaction, and operational efficiency.

A structured data analysis approach was applied to evaluate performance across key operational dimensions:

Analyzed ticket volume trends to identify peak load periods

Evaluated SLA compliance to detect performance gaps

Assessed agent and group performance to uncover inefficiencies

Examined channel usage and geographic trends

Ticket Volume Peaks: High ticket load during specific months and working hours created backlog issues

SLA Performance Gaps: ~86% first response compliance, but 974 SLA breaches indicate resolution inefficiencies

Channel Imbalance: Email accounts for ~53% of tickets, while faster channels like phone are underutilized

Resolution Delays: Some tickets exceed 200+ hours, impacting overall performance

Agent Variability: Significant performance gap between top and low-performing agents

Regional Trends: Countries like Germany and Italy show higher ticket volumes, requiring localized focus

Identified critical bottlenecks affecting response and resolution times

Highlighted inefficiencies in resource allocation and workload distribution

Enabled data-driven decision-making for improving operational performance

Provided clear visibility into SLA risks and customer experience gaps

Adjust staffing during peak months

Introduce after-hours support coverage

Implement stricter SLA monitoring and escalation mechanisms

Prioritize high-risk and delayed tickets

Provide targeted training for low-performing agents

Assign complex tickets to high-performing agents

Promote faster channels (chat/phone) over email

Improve email handling efficiency

Focus on long-tail tickets exceeding SLA thresholds

Improve prioritization and ticket routing

Power BI (Dashboard & Visualization)

Excel (Data Analysis)

Data Modeling & KPI Analysis