Superstore Excel Case Study

Photo by Blake Wisz on Unsplash

📊 Project Overview

This case study analyzes the Superstore dataset (~10K rows) using Excel to uncover insights about sales, profit, discounts, returns, and shipping speed.
The goal is to demonstrate data cleaning, DAX measures, exploratory data analysis (EDA), and dashboard storytelling directly in Excel.

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📂 Data Preparation

• Profit Column:

  • Converted accounting-style negatives (e.g., (325) → -325).
  • Replaced dash - with 0 to handle missing values.

• Discount Column:

  • Values ranged from 0.00 to 0.80.
  • Grouped into bins for clearer analysis:
    • 0–10%
    • 10–20%
    • 20–30%
    • 30–40%
    • 40%+

• Date Columns:

  • Extracted Year and Month from Order Date for trend analysis.

• Shipping Speed:

  • Created Days to Ship column.
  • Categorized into buckets:
    • Fast (0–2 days)
    • Standard (3–5 days)
    • Slow (>5 days)

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📈 KPIs & Metrics

• Total Sales & Profit across categories and regions.
• Return Rate by category.
• Discount Impact on profit margins.
• Shipping Speed Analysis (Fast vs Standard vs Slow).
• Yearly Trends in sales and profitability.

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🔍 Insights

• Discounts above 40% strongly correlated with negative profit.
• Certain categories (e.g., Furniture) had higher return rates.
• Faster shipping (0–2 days) improved satisfaction but increased costs.
• Yearly trends showed steady growth in sales, but profit margins fluctuated due to discounting strategies.

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📑 Recommendations

• Limit discounts above 30% to protect profit margins.
• Investigate high-return categories and improve product quality or communication.
• Optimize shipping strategy: balance speed with cost efficiency.
• Provide managers with dashboards to track KPIs and act on performance.

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📊 Visuals

Excel dashboards include:

• Sales & Profit by Category and Region
• Discount vs Profit Analysis
• Shipping Speed Distribution
• Yearly Sales & Profit Trends
• Returns by Category

(Screenshots to be added in repo)

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📜 Storytelling Flow

1. Problem: Profit margins inconsistent, high returns, unclear discount impact.
2. Process: Cleaned dataset, created KPIs, built dashboards in Excel.
3. Analysis: Explored discounts, returns, shipping, yearly trends.
4. Insights: Identified key drivers of profit loss and inefficiencies.
5. Recommendations: Strategic discounting, quality checks, shipping optimization.

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🚀 Conclusion

This project demonstrates how a junior data analyst can transform raw transactional data into actionable insights using Excel alone.
It highlights skills in ETL, KPI creation, dashboards, and storytelling, making it recruiter-ready and business-relevant.

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📌 Author

Ahmed Moheb Ali (Moheb)
Junior Data Analyst | BI Developer
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