📊 Superstore Sales Analysis (2020–2023)

End-to-end sales analysis on 1,200 real orders across 4 years

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🖼️ Dashboard

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🎯 Problem Statement

A retail company wants to understand its sales and profitability across product categories, regions, and customer segments from 2020–2023. As the Data Analyst, I was asked to:

• Identify top-performing categories and regions
• Analyse the impact of discounts on profitability
• Track year-over-year revenue trends
• Deliver actionable business recommendations

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📌 Key Results

KPI	Value
💰 Total Revenue	$5.24 Million
💵 Total Profit	$726,029
📈 Profit Margin	13.8%
📦 Total Orders	1,200
✅ Profitable Orders	79.6%
🏆 Top Category	Technology (56% of revenue)
🌍 Top Region	East
👥 Best Segment	Consumer

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🔍 Key Findings

#	Finding	Business Impact
1	Discounts above 30% cause losses on almost every order	Capping discounts saves margin
2	Technology drives 56% of revenue but needs protection	Avoid heavy discounting here
3	East region outperforms all others consistently	Replicate East's strategy in West
4	Corporate segment has highest profit margin	Prioritise corporate retention
5	Revenue peaked in 2021 and has slightly declined	Pricing or competition review needed

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🛠️ Skills & Tools Used

Python

Library	Usage
Pandas	Data loading, cleaning, feature engineering, groupby
NumPy	Numerical operations
Matplotlib	Custom dark-theme 6-panel dashboard
Seaborn	Statistical visualization
SQLite3	Running SQL queries on Pandas DataFrames

SQL (6 Queries in sales_queries.sql)

Concept	Where Used
GROUP BY + SUM, AVG, COUNT	Category, Region, Segment aggregations
CASE WHEN	Discount banding, profit classification
ROUND()	Formatting output
LAG() Window Function	Year-over-Year growth calculation
RANK() Window Function	Sub-category profit ranking

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📁 Files

sales-data-analysis/
│
├── Sales_Analysis.ipynb    ← Jupyter Notebook (full analysis, step by step)
├── analysis.py             ← Python script version (run directly)
├── sales_queries.sql       ← All 6 SQL queries with comments
├── superstore_sales.csv    ← Dataset (1,200 rows × 12 columns)
├── sales_dashboard.png     ← 6-panel dashboard output
├── requirements.txt        ← Python dependencies
└── README.md

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🚀 How to Run

# 1. Clone repo
git clone https://github.com/YOUR_USERNAME/sales-data-analysis.git
cd sales-data-analysis

# 2. Install dependencies
pip install -r requirements.txt

# 3a. Run Python script
python analysis.py

# 3b. OR open Jupyter Notebook
jupyter notebook Sales_Analysis.ipynb

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💡 Recommendations

1. Cap discounts at 20% — data shows 31–40% discount results in losses on ~80% of orders
2. Expand Technology sales in West region — highest-margin category, lowest penetration there
3. Invest in Corporate segment retention — highest profit margin, most valuable customer base
4. Review 2022–23 pricing strategy — sales peaked in 2021 and have slightly declined since
5. Double down on top sub-categories — focus resources on what's already working

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📬 Contact

ACHAL WAKADE | Data Analyst
📧 achalwakade29@gmail.com
🔗 LinkedIn
💼 All Projects

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