Helpdesk Performance Analytics Dashboard – Agent Productivity and Ticket Resolution Insights

Project: Helpdesk Performance Analytics Dashboard – Agent Productivity and Ticket Resolution Insights

Objective:

To evaluate agent performance and operational efficiency through analysis of helpdesk ticket data, identifying patterns in resolution times and service categories, and enabling data-driven decision-making to improve service quality.

Context:

This project serves as a practical case study for analyzing helpdesk ticket resolution performance. The aim is to track the response efficiency of agents, identify high- and low-performing staff, and detect differences in complexity and time required based on ticket categories. Data is sourced from separate Excel files, with one file per operational cycle.

Tools & Technologies Used: 

• Power BI – Data visualization and dashboard creation

• Power Query (M Language) – Data transformation and multi-file integration

• DAX (Data Analysis Expressions) – Metrics and KPI calculation

• External Image Repository – Dynamic agent photos via URL

Process Overview:

1. Data Acquisition: Collected multiple Excel files, each containing ticket information for a specific operational period.

2. Data Consolidation: Used Power Query to load and combine data from multiple Excel sources dynamically, handling schema consistency.

3. Data Cleaning: Corrected errors, standardized formats, and removed duplicates.

4. Data Modeling: Established relationships between ticket fields such as agent, category, and time-to-resolution.

5. KPI Development (DAX): Calculated key metrics including:

   • Number of tickets resolved per agent

   • Average resolution time

   • Performance by category

   • Complexity-adjusted productivity score

6. Dashboard Design:

   • Agent performance ranking (ascending/descending)

   • Service category breakdown with resolution times

   • Dynamic agent profile images via URLs

   • Theme toggle: switch between light and dark mode based on user preference

Key Insights & Recommendations:

• Ticket Complexity Matters: Initial evaluations were based solely on the number of tickets resolved. However, analysis revealed that some ticket categories inherently require more time due to higher complexity.

• Reassessment Needed: Recommended adjusting the performance evaluation criteria to incorporate ticket category weight, ensuring fairness in performance metrics.

• UX Enhancement: The dark/light mode toggle improved accessibility and user experience, especially for extended usage scenarios.

Visualizations Included:

• Agent performance leaderboard

• Resolution time by service category

• Ticket volume trends by period

• Individual agent profiles with image integration

• Interactive dark/light theme toggle

Dataset: 

Excel file with data: https://drive.google.com/file/d/1NwNZBfET1lgZ2WCjxvEls1x940azw0yW/view?usp=sharing 

Excel file with agent photos: https://docs.google.com/spreadsheets/d/19lCTzG6qdhsJTS66edyXtdgcuxut2S_JKpuHithvxlo/edit?usp=sharing 

Visualizations: