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Build This Project · Portfolio Guide

Customer Support Performance Dashboard for Your Data Portfolio

Build a customer support performance dashboard that demonstrates your ability to analyze service metrics, track agent performance, and improve customer satisfaction.

Tracks
Support Dashboard · Live Interactive
Project Focus
—
What you'll build
Skills Demonstrated
—
Key competencies
Business Impact
—
Value to employer
Get Data → Analyze → Agent Metrics → Dashboard → Portfolio
Click to see the project overview — a customer support dashboard that will make your portfolio stand out.

Home / Tutorials / Project Guides / Customer Support Performance Dashboard

Build This Project · Portfolio Guide

Customer Support Performance Dashboard — Complete Project Guide

DATA KPIs AGENTS PORTFOLIO Data Support tickets Agent logs Public datasets KPIs Avg response time Resolution rate Service metrics Agent Performance Tickets resolved Satisfaction Key insight Portfolio Showcase work Get hired Offer
Customer support analytics helps companies improve service quality and agent performance — a highly valued skill.

Quick summary — build a customer support performance dashboard

Customer support is a critical business function. This project demonstrates your ability to analyze service metrics, track agent performance, and improve customer satisfaction — skills every customer-focused company needs.

In this guide you will learn:

  1. Project overview — what you'll build and why.
  2. Data source — where to get support data.
  3. Key metrics and KPIs — what to measure.
  4. Agent performance analysis — tracking individual performance.
  5. Dashboard design — visualizing support metrics.
  6. Portfolio presentation — how to show it to employers.

SECTION 01Project overview

Here's what you'll build in this project:

  • Business problem: A company wants to improve customer support quality, reduce response times, and identify top-performing agents.
  • Your solution: A support performance dashboard that tracks key metrics — response time, resolution rate, customer satisfaction, and agent performance.
  • Tools: Tableau, Power BI, Python (Streamlit/Dash), or Excel.
  • Outcome: A portfolio-ready dashboard that demonstrates customer service analytics skills.
Key insight: Customer support analytics directly impacts customer retention — companies with excellent support have higher customer loyalty.

SECTION 02Data source

Here are the best data sources for this project:

SourceDataLink
KaggleCustomer support datasetskaggle.com/datasets
Simulated dataCreate your ownUse Python to generate
ServiceNowIT support dataVarious sources
Recommendation: Create a simulated dataset — it's easy to design and ensures you have all the metrics you need.

SECTION 03Key metrics and KPIs

Here are the key metrics your support dashboard should track:

MetricWhy it matters
Average Response TimeHow quickly customers get replies — key satisfaction driver
First Response TimeTime to first reply — critical for customer experience
Resolution RatePercentage of tickets resolved — efficiency metric
Customer Satisfaction (CSAT)Direct measure of service quality
Ticket VolumeNumber of tickets — workload indicator
Agent PerformanceTickets resolved, satisfaction score by agent
Key point: Focus on metrics that matter to support leaders — response time and CSAT are top priorities.

SECTION 04Agent performance analysis

Here's how to analyze agent performance:

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

df = pd.read_csv('support_tickets.csv')

# Agent performance metrics
agent_metrics = df.groupby('agent_name').agg({
    'ticket_id': 'count',
    'resolution_time': 'mean',
    'satisfaction_score': 'mean'
}).reset_index()

# Rename columns
agent_metrics.columns = ['Agent', 'Tickets Resolved', 'Avg Resolution Time', 'Avg CSAT']

# Sort by CSAT
agent_metrics_sorted = agent_metrics.sort_values('Avg CSAT', ascending=False)

# Visualize agent performance
fig, axes = plt.subplots(1, 3, figsize=(15, 5))
sns.barplot(data=agent_metrics_sorted, x='Agent', y='Tickets Resolved', ax=axes[0])
axes[0].set_title('Tickets Resolved by Agent')

sns.barplot(data=agent_metrics_sorted, x='Agent', y='Avg Resolution Time', ax=axes[1])
axes[1].set_title('Avg Resolution Time by Agent')

sns.barplot(data=agent_metrics_sorted, x='Agent', y='Avg CSAT', ax=axes[2])
axes[2].set_title('Avg CSAT by Agent')

plt.tight_layout()
plt.show()
agent-performance.md

SECTION 05Dashboard design

Here's how to design your support performance dashboard:

  • Top section: KPI cards — Avg Response Time, Resolution Rate, CSAT Score, Total Tickets.
  • Middle section: Ticket volume trends, response time trends, CSAT trends.
  • Bottom section: Agent performance table/bar chart, ticket breakdown by category.
  • Filters: Date range, agent, ticket category.
Pro tip: Include a "Top Agents" section to highlight outstanding performers — this is valuable for management.

SECTION 06Portfolio presentation

Here's how to present this project to employers:

  • GitHub: Upload your code, data preparation scripts, and dashboard file.
  • README: Write a clear README with project overview, metrics, and key insights.
  • Executive summary: Include a 1-page summary for business stakeholders.
  • Screenshots: Add screenshots of your dashboard and agent analysis.
  • LinkedIn post: Share your project with a brief explanation of the business problem you solved.
Key point: Customer support analytics is a growing field — this project will make you stand out to customer experience employers.

SECTION 07Interview Q&A — customer support dashboard

Q1Why did you choose a customer support project?

Customer support is critical for customer retention. I wanted to show I can analyze service metrics and help companies improve customer experience.

Q2What was the most important metric in your dashboard?

CSAT (Customer Satisfaction) and response time — they directly impact customer experience and retention.

Q3What insights did you find?

I found that response time was strongly correlated with CSAT — faster responses led to higher satisfaction. I recommended optimizing staffing during peak hours.

Q4What tool did you use?

I used Tableau for the dashboard and Python for analysis. I documented everything on GitHub.

Q5What would you do differently next time?

I'd add sentiment analysis on customer feedback and predictive analytics for ticket volume forecasting.

SECTION 08Test yourself — support dashboard quiz

Five questions. No sign-up.

0 / 5

Pick an answer to see why it is right or wrong.

SECTION 09Frequently asked questions

What's the most important KPI for customer support?

CSAT and response time are the most important KPIs — they directly impact customer experience and retention.

What's a good response time?

Under 5 minutes is excellent, under 1 hour is good. It varies by industry and channel.

What's the best tool for support analytics?

Tableau or Power BI are great for dashboards. Python is excellent for analysis.

How long does this project take?

2-3 weeks with consistent effort — 1 week for data prep, 1 week for analysis, 1 week for dashboard and documentation.

Do I need experience in customer support?

Not necessarily — the data skills are transferable. Researching support metrics will help you build a better project.

Classroom & online · Noida

Build a support analytics dashboard — get hired

Our Data Analytics Training Course includes customer support and other portfolio projects with step-by-step guidance.

₹15,500 · full programme ₹24,000
  • 8 portfolio projects
  • Support analytics
  • Mock interviews
  • Weekday & weekend batches
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