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RetailEdge is a SQL and Power BI analytics project that turns retail data into clear business insights. I used MySQL for data analysis and Power BI to build interactive dashboards covering sales, customers, products, stores, employees, logistics, and returns.
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RetailEdge is a SQL and Power BI analytics project that turns retail data into clear business insights. I used MySQL for data analysis and Power BI to build interactive dashboards covering sales, customers, products, stores, employees, logistics, and returns.
After a long silence — Power BI has been consuming all my hours 🎯
Just wrapped Page 1 of a multi-page sales performance dashboard for a supermarket chain client. This is what I've been building quietly instead of posting 😅
The problem:
My client was tracking sales in scattered reports — no single source of truth for MOM (Month-over-Month) trends, store performance, or daily variations. Leadership had to hunt through multiple sheets to answer basic questions.
What we built:
A live Power BI dashboard that answers:
✓ Which stores are underperforming? (Rankings updated daily)
✓ How much did revenue grow last month? (MOM % at a glance)
✓ Are we hitting targets? (Real-time KPIs with trend indicators)
✓ Which products drive revenue? (Top 10 breakdown)
✓ How do weekdays vs weekends compare? (Day-over-Day trends)
The tech stack:
Data cleaned in Excel → Loaded to Power BI → Published online → Live slicers for filtering by date, store, category
Pages 2 & 3 in progress (Store Analysis + Returns Breakdown). But honestly? This work is the kind you don't post about until it's done. Now it is.
To anyone rebuilding their portfolio:
Real client work beats portfolio templates every single time. Even if you disappear from LinkedIn for 3 months, showing actual deliverables matters more than consistent posting.
#PowerBI #DataAnalytics #Dashboard #SalesAnalysis #BusinessIntelligence #DataVisualization #PowerBIDeveloper #RemoteWork #FreelanceAnalyst #DataDriven #Retail #Analytics
Retail Store Performance Analysis | Python, Excel & Business Analytics
I analyzed 10 days of retail store performance data to uncover the factors driving sales, profitability, margins, and staff efficiency.
Using Python, Pandas, Excel, data cleaning, KPI analysis, and data visualization, I transformed raw business data into a structured analysis and clear, actionable insights.
🔍 Key Insights
Store D achieved the highest operational efficiency and profitability.
Store B showed low productivity despite higher staff hours.
Store C generated strong sales but struggled with lower profit margins.
Operational efficiency had a stronger relationship with profitability than sales volume alone.
🛠️ What I Worked On
Data cleaning & preprocessing
Exploratory Data Analysis (EDA)
KPI identification & calculation
Sales & profit analysis
Margin analysis
Staff productivity & efficiency analysis
Business insight generation
Data visualization & reporting
💡 Business Value
The analysis demonstrates how businesses can move beyond simply tracking sales revenue and use profitability, margins, productivity, and operational KPIs to identify improvement opportunities and make better data-driven decisions.
Tools: Python • Pandas • Excel • Data Analysis • Data Visualization
Data Analyst, Business Analytics, Retail Analytics, Data Analysis, Exploratory Data Analysis, Python Data Analysis, Pandas, Excel Analysis, KPI Analysis, Sales Analysis, Profit Analysis, Data Cleaning, Data Visualization, Business Intelligence, Performance Analysis, Retail Data Analysis, Dashboard & Reporting, Actionable Insights
