☕ Cafe Sales Analytics — Revenue Growth Case Study

🚀 An end-to-end business analytics project analyzing 10,000+ transactions to identify revenue drivers, customer behavior, and growth opportunities.

💡 Focus: Turning raw data into actionable strategies that increase revenue and retention

💰 This project identifies data-driven strategies that can increase café revenue by 10–20% through better product focus, pricing, and customer targeting.

📊 Project Preview

📁 Project Structure

cafe_analysis/
├── raw_data.csv                              ← Original dataset (place here)
├── cleaned_cafe_data.csv                     ← Auto-generated by Notebook 1
│
├── 01_Data_Audit_and_Cleaning.ipynb          ← Run FIRST
├── 02_EDA.ipynb                              ← Run SECOND
├── 03_Insights_and_Recommendations.ipynb     ← Run THIRD
│
└── README.md                                 ← You are here

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💰 Business Impact

This analysis identifies opportunities to:

• Increase Average Order Value (AOV) by 8–15%
• Improve customer retention by 15–25%
• Boost seasonal revenue (Q1) by up to 20%
• Enhance decision-making through improved data quality

This project demonstrates how data can directly drive revenue growth.

🎯 Who This Project Is For

• Café / Restaurant Owners
• Small Business Owners
• Data-driven decision makers
• Freelance analytics clients

If you have sales data, similar insights can be generated for your business.

🚀 How to Run

1. Place raw_data.csv in the same folder as the notebooks
2. Open and run 01_Data_Audit_and_Cleaning.ipynb — generates cleaned_cafe_data.csv + data quality chart
3. Open and run 02_EDA.ipynb — generates 6 EDA visualisation charts
4. Open and run 03_Insights_and_Recommendations.ipynb — generates insights charts + executive summary

✅ Each notebook saves its charts as .png files in the same directory

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📊 Dataset Overview

Field	Description
Transaction ID	Unique transaction identifier
Item	Menu item sold (Coffee, Tea, Juice, Cake, Cookie, Salad, Sandwich, Smoothie)
Quantity	Units ordered
Price Per Unit	Unit price (£)
Total Spent	Order total (£)
Payment Method	Cash / Credit Card / Digital Wallet
Location	In-store / Takeaway
Transaction Date	Date of transaction

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🔍 Key Findings

1. Revenue Concentration — Top 3 items (Juice, Coffee, Salad) drive ~38% of revenue
2. Location Gap — Takeaway = 50% of transactions but lower average order value than In-Store
3. Seasonality — Q4 and Q2 are peak quarters; Q1 is consistently the weakest
4. Payment Shift — Digital Wallet is now the #1 payment method (~30% of clean transactions)
5. Data Quality Issue — 32% of Location and 26% of Payment Method data is missing or corrupt

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✅ Top Recommendations

Priority	Action	Expected Impact
🔥 P1	Takeaway combo upsell prompt	AOV +8–15%
🔥 P1	Q1 Winter campaign (hot-drink bundles)	Q1 revenue +10–20%
🏗️ P2	Digital loyalty programme	Repeat visits +15–25%
⚡ P3	Fix POS data collection (eliminate ERROR/UNKNOWN)	Data quality 95%+

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🛠️ Dependencies

pandas >= 1.5
numpy >= 1.23
matplotlib >= 3.6
seaborn >= 0.12

Install with: pip install pandas numpy matplotlib seaborn

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💼 Work With Me

I help businesses turn their data into revenue-driving insights.

If you have:

• Sales data
• Customer data
• Business performance issues

I can deliver: ✔ Data cleaning & analysis
✔ Business insights
✔ Growth strategies

📬 Contact

If you want a similar analysis for your business:

📧 Email: mohdsahibraza8@gmail.com
🔗 LinkedIn: https://www.linkedin.com/in/mohdsahibraza

Mohd Sahib Raza - Data Analyst