Call Center Performance Dashboard

Table of Contents

1. Project Overview
2. Business Problem
3. Project Tools
4. Repository Structure
5. Dataset Overview
6. Dashboard Screenshot
7. Methodology
8. Key Insights
9. Recommendations
10. Assumptions & Limitations
11. Future Enhancements
12. Author

1. Project Overview

This project analyzes inbound call center data to evaluate call volume patterns, customer sentiment, and service response performance across multiple channels and call centers. Using operational metrics such as call distribution, response time categories, and sentiment trends, the analysis identifies peak demand periods, service inefficiencies, and customer experience gaps.

The insights generated support data-driven decisions for staffing optimization, performance improvement, and enhanced customer service delivery. This project demonstrates how Excel-based analytics can support operational decision-making in a call center environment.

2. Business Problem

The call center receives a high volume of inbound customer calls across multiple channels, locations, and time periods. This analysis aims to understand call distribution, customer sentiment, and response-time performance to identify operational bottlenecks and opportunities to improve service efficiency and customer experience.

3. Project Tools

• Microsoft Excel: (Data cleaning, PivotTables, Dashboard)
• GitHub: (Version control and documentation)

4. Repository Structure

[Call-Center-Performance-Dashboard]/
│
├── dashboard/
│   └── call_center_dashboard.xlsx
│
├── data/
│   ├── raw/
│   │   └── call_center_raw.csv
│   │
│   └── processed/
│       └── call_center_cleaned.xlsx
│
├── images/
│   └── callcenter.jpg
│
├── LICENSE
└── README.md

5. Dataset Overview

The dataset contains historical call center operations data used to analyze customer service performance, call handling efficiency, and agent activity.

Dataset Scope

• Call center interaction records
• Agent performance metrics
• Customer satisfaction indicators
• Call volume and response trends
• Operational performance analysis

6. Dashboard Screenshot

Dashboard Highlight

• Billing-related inquiries account for the majority of customer support requests, indicating potential inefficiencies in billing communication and payment workflows.

7. Methodology

Data Cleaning and Preparation

• Created a working copy of the dataset to preserve the raw data
• Checked for duplicate records (none found)
• Converted Score and Call Duration to numeric format (no decimals)
• Split call timestamp into date format using Text to Columns
• Added a helper column (Call Day) to extract day from call timestamp
• Converted the dataset into an Excel Table for easier analysis and pivoting

Exploratory Data Analysis

The exploratory analysis focused on evaluating call center operations, service efficiency, customer sentiment, and inbound call distribution patterns.

Call Volume & Distribution

• customer sentiment categories
• call reasons
• communication channels
• call center locations
• state-level call distribution
• daily inbound call trends

Service Performance Analysis

• response time categories
• SLA performance distribution
• response time performance across call centers

Customer Experience Analysis

• sentiment distribution by call center
• overall positive and negative sentiment trends

Key KPI

• Total Inbound Calls

8. Key Insights

• Customer sentiment trends skew heavily negative, indicating persistent service experience and issue-resolution challenges across multiple support interactions.
• Digital support channels handle the majority of customer interactions, reflecting increased customer reliance on self-service and online support options.
• While most calls are resolved within SLA targets, several high-volume call centers experience inconsistent response performance during peak demand periods.
• Los Angeles and Baltimore generate the highest inbound call volumes, highlighting concentrated operational demand and potential staffing pressure within those regions.
• Negative customer sentiment appears strongly linked to billing-related concerns and delayed response times, suggesting that operational inefficiencies directly impact customer experience.
• Geographic and channel-level call distribution patterns reveal opportunities to optimize staffing allocation, improve workload balancing, and strengthen service coverage across high-demand areas.

9. Recommendations

• Improve billing communication processes and payment support workflows to reduce the high volume of billing-related inquiries.
• Strengthen customer service quality monitoring in high-volume call centers to improve response consistency and reduce negative customer sentiment.
• Expand and optimize digital support channels such as chat bots and web support to improve scalability and reduce pressure on live agents.
• Implement targeted SLA monitoring for regions with high inbound call volumes to improve response efficiency and operational performance.
• Introduce customer feedback tracking and sentiment monitoring to identify recurring service issues and improve customer experience.
• Reassess staffing allocation across call centers to better align workforce capacity with regional support demand.
• Develop proactive support strategies for recurring service outage and payment-related issues to reduce repeat customer contacts.

10. Assumptions & Limitations

Assumptions

• The dataset is assumed to accurately represent overall call center operations and customer interactions.
• Customer sentiment classifications are assumed to correctly reflect customer experience during each interaction.
• Response time categories and SLA classifications are assumed to be consistently recorded across all call centers.
• Geographic call distribution is assumed to represent actual customer support demand by region.

Limitations

• The analysis is limited to the variables available in the dataset and does not include external operational factors such as staffing levels or system outages.
• Customer demographic information was not available for deeper segmentation and behavioral analysis.
• The dashboard provides descriptive historical analysis and does not include predictive forecasting or real-time monitoring capabilities.
• Sentiment analysis is limited to predefined sentiment categories and may not fully capture the complexity of customer experiences.
• Regional call distribution analysis does not account for population differences or customer base size across states.

11. Future Enhancements

• Develop an interactive Power BI version of the dashboard to enable advanced filtering, drill-through analysis, and real-time KPI monitoring.
• Incorporate predictive analytics to forecast inbound call volumes and identify peak support periods for improved workforce planning.
• Introduce agent-level performance analysis to evaluate productivity, response efficiency, and customer sentiment outcomes by representative.
• Integrate customer demographic and segmentation data to support deeper behavioral and regional analysis.
• Expand SLA monitoring by implementing trend analysis across time periods, channels, and call center locations.
• Automate data cleaning and reporting workflows using Power Query or Python to improve reporting efficiency and scalability.
• Add sentiment trend tracking over time to identify recurring service issues and measure customer experience improvements.
• Include operational metrics such as staffing levels, queue wait times, and call abandonment rates for a more comprehensive performance analysis.

12. Author

Godwin Deborah

Data Analyst

• 🔗 Linkedin
• 💼 GitHub
• 📧 Email