Retail Sales Performance Analysis (2022 - 2023)

A data analysis case study examining 1,200 retail transactions across two years to identify profit leakages and growth opportunities across product categories, regions, and customer segments.

Key Findings

KPI	Value
Total Revenue	$1.09M
Net Profit	$104K
Profit Margin	9.5%
Total Orders	1,200
Avg Order Value	$912

1. Furniture Is Destroying Value

Furniture generates $329K in revenue but posts a net loss of -$35,401 (-10.8% margin). Every single Furniture sub-category operates at a loss:

Sub-Category	Revenue	Profit	Margin
Tables	$93,588	-$11,425	-12.2%
Chairs	$82,842	-$9,392	-11.3%
Bookcases	$89,300	-$8,280	-9.3%
Furnishings	$63,561	-$6,304	-9.9%

Root cause: Furniture carries a structurally low gross margin (~8%) combined with heavy average discounting (~28%), resulting in guaranteed losses on nearly every transaction.

2. Aggressive Discounting Destroys Profit

Discount Level	Avg Profit per Order
No Discount	+$208
Low (up to 15%)	+$122
Mid (16-30%)	-$22
High (above 30%)	-$315

Orders discounted above 15% become loss-making in aggregate. A 15% discount cap could recover an estimated $20K+ annually.

3. Technology Drives All Real Profit

Technology delivers 19%+ margins across all four sub-categories (Copiers, Phones, Computers, Accessories) and accounts for 127% of total business profit -- the excess is offset by Furniture losses.

4. Regional Performance Is Consistent

All four regions operate at similar margins (9.3% - 10.0%), confirming that profitability issues are driven by product mix and discount policy, not geography.

Region	Revenue	Profit	Margin
East	$336,130	$31,392	9.3%
North	$280,573	$26,182	9.3%
West	$279,875	$26,993	9.6%
South	$198,083	$19,817	10.0%

Strategic Recommendations

1. Fix or Exit Furniture -- Review supplier costs and retail pricing starting with Tables (-12.2% margin). If costs cannot be restructured, reduce or exit the Furniture category entirely.
2. Cap Discounts at 15% -- Implement a formal discount ceiling with management approval required for exceptions. This single policy change could recover $20K+ in annual profit.
3. Double Down on Technology -- Shift marketing budget, sales focus, and inventory investment toward Technology, especially targeting Corporate and Home Office segments.

Project Structure

.
├── sales_analysis.py                        # Python analysis script (8 steps: load, clean, analyze, visualize)
├── superstore_data.csv                      # Source dataset (1,200 transactions, 9 columns)
├── dashboard.html                           # Interactive HTML dashboard with Chart.js visualizations
├── retail_sales_analysis_2022_2023.pdf       # Full PDF report with charts and findings
└── README.md

File Descriptions

• sales_analysis.py -- End-to-end analysis pipeline: data loading, cleaning, KPI computation, category/region/discount breakdowns, and chart generation (4 Matplotlib visualizations with dark-themed styling).
• superstore_data.csv -- Raw dataset with columns: Order Date, Region, Segment, Category, Sub-Category, Sales, Quantity, Discount, Profit.
• dashboard.html -- Self-contained interactive dashboard built with Chart.js featuring: KPI cards, monthly revenue trend (2022 vs 2023), category profitability, regional performance, discount impact analysis, sub-category leaderboard, and strategic recommendations.
• retail_sales_analysis_2022_2023.pdf -- 7-page PDF report summarizing all findings with embedded charts and data tables.

Tools & Technologies

• Python 3 -- Core analysis language
• Pandas -- Data manipulation and aggregation
• Matplotlib & Seaborn -- Static chart generation
• ReportLab -- PDF report generation
• Chart.js -- Interactive browser-based visualizations
• HTML/CSS -- Dashboard layout and styling

How to Run

# Install dependencies
pip install pandas matplotlib seaborn

# Run the analysis (generates charts and prints findings to console)
python sales_analysis.py

# View the interactive dashboard
open dashboard.html    # macOS
xdg-open dashboard.html  # Linux

Dataset Overview

• Period: January 2022 -- December 2023
• Records: 1,200 transactions
• Categories: Technology, Furniture, Office Supplies
• Regions: East, North, West, South
• Segments: Consumer, Corporate, Home Office

Author

Gbolahan Akande -- Data Analyst

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Portfolio case study | Data Analysis