My Projects
Project 1: Sales Data Analysis
This dashboard provides a comprehensive view of sales performance, profit distribution, and business trends to support data-driven decision making.
Project Overview
This project analyzes sales performance using Power BI to uncover trends, evaluate profitability, and provide actionable business insights.
Business Context
This project focuses on analyzing sales performance to help businesses understand key drivers of revenue, profitability, and customer purchasing behavior.
Problem:
Businesses often struggle to understand how sales, profit, and discounts impact overall performance and decision-making.
Tools Used:
Excel, Power BI
Process:
- Cleaned and prepared the dataset for analysis
- Built interactive dashboards to track sales, profit, and order trends
- Analyzed category performance and discount impact
- Visualized key business metrics for decision-making
Key Metrics
- Total Sales
- Total Profit
- Total Orders
- Profit Margin
- Average Discount
Key Insights
- Technology category generated the highest revenue and profit
- High discount rates were associated with lower profit margins
- Sales performance varied across product categories and time periods
- Some categories contributed less to overall profitability, indicating areas for improvement
Recommendations
- Reduce excessive discounting to improve profit margins
- Focus on high-performing product categories for growth
- Optimize pricing strategies based on category performance
- Monitor underperforming categories and improve marketing strategies
Project 2: Call Center Performance Analysis
Project Overview
This project analyzes call center operations to evaluate service efficiency, customer satisfaction, and operational performance using Power BI.
Business Context
Call centers play a critical role in customer experience. Understanding call volume, response rates, and satisfaction levels helps organizations improve service delivery.
Problem
The organization needed to assess how efficiently customer calls were handled and identify factors affecting customer satisfaction.
Tools Used
Power BI, Excel
Process
- Cleaned and prepared call center dataset
- Analyzed call volume trends across hours and topics
- Evaluated agent performance and workload distribution
- Measured customer satisfaction and resolution rates
- Built an interactive dashboard for performance monitoring
Key Metrics
- Total Calls
- Answer Rate
- Resolution Rate
- Customer Satisfaction Score
Key Insights
- Answer rate was high (81%), but some calls were still missed
- Resolution rate was strong (89%), indicating effective problem-solving
- Customer satisfaction remained relatively low despite high resolution
- Call demand peaked during midday, indicating pressure period
Recommendations
- Improve service capacity during peak hours
- Focus on improving customer interaction quality
- Provide additional training for better customer experience
- Optimize staffing based on call demand patterns
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