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E-commerce Sales Analytics Dashboard
Developed an interactive Power BI dashboard to analyze e-commerce sales performance and generate actionable business insights.
• Cleaned and transformed messy sales data using Excel & Power Query
• Analyzed 10,000 Rows of revenue, orders, profit, products, categories, customers and regional performance
• Created interactive KPIs, DAX measures, slicers and visualizations
• Identified sales trends, top-performing products and key business performance areas
Tools: Power BI | Excel | Power Query | DAX | Data Cleaning | Data Analysis
#PowerBI #DataAnalytics #DataAnalysis #EcommerceAnalytics #SalesAnalytics #BusinessIntelligence #DataVisualization #PowerBIDashboard #Excel #PowerQuery #DAX #DataCleaning #DataTransformation #BusinessAnalytics #Dashboard #RetailAnalytics #SalesDashboard #DataAnalyst #FreelanceDataAnalyst #Analytics
The network for creativity
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Creatives on Contra have earned over $150M and we are just getting started
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Too many businesses try to build complex predictive AI models before their basic data pipelines are even functioning. You cannot skip the fundamentals.
The Data Hierarchy of Needs for scaling SMBs:
Level 1: Stable Collection (SQL) — Your database health and data pipelines. If your raw data is broken, everything built on top of it will fail.
Level 2: Data Governance (Python) — Enforcing uniform definitions, cleaning messy data, and ensuring total integrity.
Level 3: Automated BI (Power BI) — Moving away from manual spreadsheets to automated executive views and daily KPI tracking.
Level 4: Predictive Insights (AI / ML) — Churn models and forecasting. This only generates ROI if levels 1 through 3 are rock solid.
Stop wasting time on manual data entry and broken reports. If you are stuck at Level 1 or 2, I can help you scale up. Check out my Contra services to get your SQL pipelines automated and your Power BI dashboards built right the first time.
Amazing Information
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
A Power BI sales dashboard designed to turn sales data into clear business insights. It tracks sales, profit, orders, profit margin, monthly trends, product performance, customer segments, and sales by category.
