This project analyzes customer support ticket data to uncover operational bottlenecks, customer satisfaction trends, and actionable business recommendations.
The objective was not simply to build a dashboard but to transform raw support ticket data into actionable insights that management could use to improve customer experience, optimize support operations, and prioritize business decisions.
Customer support teams handle thousands of tickets across multiple communication channels, priorities, and issue categories.
Management needed answers to critical operational questions:
- How many tickets were received?
- What percentage of tickets were successfully resolved?
- Which ticket types generated the highest demand?
- Which support channels handled the largest workload?
- How satisfied were customers with support services?
- What operational improvements should be prioritized?
| Metric | Value |
|---|---|
| Total Records | 8,469 |
| Total Columns | 17 |
| Dataset Type | Customer Support Tickets |
| Analysis Tool | Power BI |
| Data Preparation | Excel & Power Query |
- Excel
- Pivot Tables
- Power Query
- DAX
- Power BI
The dataset was reviewed, cleaned, and transformed before analysis.
- Missing value assessment
- Validation of customer ratings
- Review of date and time fields
- Consistency checks across ticket categories
- Created Satisfaction Category field
- Created Priority Sort Column
- Standardized ticket classifications
- Prepared fields for DAX calculations and dashboard reporting
Provides a high-level summary of ticket volume, ticket status distribution, customer feedback coverage, satisfaction performance, and operational workload.
Explores customer satisfaction across:
- Ticket Types
- Ticket Priorities
- Support Channels
Summarizes critical findings and translates analytical results into business recommendations.
Documents the complete analytical process from data audit and cleaning through business analysis and dashboard development.
- Only 32.7% of tickets were successfully resolved.
- Approximately 67.3% of tickets remained unresolved.
- Pending Customer Response represented the largest ticket status category.
- Refund Requests generated the highest support demand.
- Technical Issues represented another major support driver.
- Average customer satisfaction was 2.99 out of 5.
- More than 5,700 tickets lacked customer feedback.
- Chat support achieved the strongest satisfaction performance among support channels.
- Launch a backlog reduction initiative to improve ticket closure rates.
- Improve customer follow-up processes to reduce pending-response tickets.
- Strengthen ticket resolution workflows.
- Investigate refund-request bottlenecks.
- Expand successful chat-support practices across other channels.
- Improve customer communication processes.
- Standardize feedback collection across all support channels.
- Enhance SLA monitoring and operational reporting.
- Implement routine customer-satisfaction tracking.
- Data Cleaning
- Data Transformation
- Exploratory Data Analysis
- Data Quality Assessment
- Dashboard Design
- Data Visualization
- KPI Development
- Executive Reporting
- Power BI
- DAX
- Power Query
- Excel
- Pivot Tables
- Business Storytelling
- Operational Analysis
- Insight Generation
- Recommendation Development
The analysis identified operational inefficiencies, customer-service bottlenecks, and opportunities for improving customer satisfaction.
The resulting dashboard provides management with a centralized view of support performance while enabling data-driven decisions that can improve both operational efficiency and customer experience.
Olajide Oluwafemi Eniola
Data Analyst | Power BI | SQL | Excel | Business Intelligence