🍛 Rohan's Rasoi – Restaurant & Sales Analytics

SQL-Based Restaurant Analytics Project

The Rohan's Rasoi project focuses on restaurant operations, menu performance, and customer preferences using SQL analytics. Advanced queries, joins, and aggregation functions help identify best-selling dishes, customer order patterns, and peak dining hours. These insights assist management in menu optimization, staffing decisions, and revenue maximization.

Rohan's Rasoi SQL Project

Project Highlights

  • Menu Performance

    Tracked top-selling dishes, combos, and seasonal trends using SQL aggregation and ranking functions.

  • Customer Order Analysis

    Segmented customers based on frequency, order value, and preferred dishes using SQL CASE and grouping.

  • Revenue & Profit Insights

    Analyzed revenue contribution by menu items and customer segments for business decision-making.

  • Peak Hours & Operations

    Identified busy hours and low-traffic periods using SQL date and time functions to optimize staffing.

  • Customer Retention

    Analyzed repeat vs new customers and loyalty patterns to improve marketing and retention strategies.

About the Project

Rohan's Rasoi Overview

Overview

The Rohan's Rasoi project provides insights into restaurant operations and customer dining patterns. By integrating orders, menu items, and customer data, SQL analytics uncover trends in dish popularity, revenue, and customer behavior. This helps management make informed decisions about menu offerings, staffing, and marketing strategies.

SQL Process & Data Modeling

  • 📥 Extract: Combined orders, menu items, and customer tables for analysis.
  • 🧹 Transform: Standardized menu names, handled missing orders, and cleaned timestamps.
  • 🔍 Analyze: Applied SQL aggregation, ranking, and partitioning for insights on sales and peak hours.
  • 📊 Model: Built a data mart linking orders, menu items, and customers for efficient analytics.
Rohan's Rasoi SQL Model
Rohan's Rasoi Insights

Key Insights

  • 🍛 Top 10 dishes contributed to 65% of total revenue.
  • 📈 Peak dining hours were 12 PM – 2 PM and 7 PM – 9 PM, suggesting optimal staffing schedules.
  • 👥 Repeat customers spent 25% more on average than new customers.
  • 🌍 Regional order analysis helped optimize delivery zones and marketing campaigns.

When:
2025

Mode:
SQL Data Analysis

Dataset:
Orders, Menu Items & Customer Data

Focus:
Restaurant Operations & Customer Analytics

Project Snapshots

Business Impact

📊 Optimized menu offerings and increased revenue through dish popularity analysis.

💬 Improved staffing and service efficiency during peak hours using SQL insights.

🚀 Enhanced customer retention by identifying repeat customer patterns and preferences.

Challenges & Learnings

⚙️ Handling multiple data tables and cleaning timestamp and order data efficiently.

🧩 Creating accurate segmentation and revenue insights from transactional data.

💡 Strengthened skills in SQL, restaurant analytics, and operational optimization.

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