Analyzing Taste of the World Restaurant’s Sales Data: Key Insights & Strategic Recommendations

Introduction

Restaurants thrive on understanding customer preferences, optimizing menus, and improving operational efficiency. As a data analyst, I recently worked on a project analyzing sales data from Taste of the World Restaurant, a dining establishment offering diverse international cuisine.

Using SQL, I explored their menu performance, order trends, and customer spending habits to uncover actionable insights. Here’s a breakdown of my findings and strategic recommendations.

Check the project details on my GitHub.

Project Overview

Business Context

Taste of the World Restaurant serves a mix of American, Italian, Asian, and Mexican dishes. The management wanted to:
✔ Identify best-selling and underperforming items
✔ Analyze high-spending customer behavior
✔ Optimize menu engineering and staffing

Dataset

• Time Period: March 2023 (1 month)
• Total Orders: 5,370
• Menu Items: 32 (across 4 categories)
• Data Sources:
  • menu_items (item details, pricing, category)
  • order_details (order timestamps, items purchased)

Key Insights from the Analysis

1. Most & Least Popular Menu Items

Using SQL joins and aggregations, I identified:

✅ Top Seller: Hamburger (American) – 622 orders
❌ Least Ordered: Chicken Tacos (Mexican) – only 52 orders

Why This Matters:

• The American category outperformed others, suggesting strong customer preference.
• The Mexican category struggled, possibly due to taste, pricing, or competition.

2. High-Value Orders: What Big Spenders Buy

The top 5 highest-spending orders averaged $185+, featuring:

• Premium proteins (steak, lobster, shrimp scampi)
• Alcohol pairings (wine, cocktails)
• Larger quantities (avg 4 items vs. 2.28 overall)

Business Takeaway:
👉 Upselling premium combos could boost revenue.
👉 Loyalty programs for high-spending customers may improve retention.

3. Order Timing Trends

• Peak Hours:
  • 🕚 11AM–1PM (Lunch rush – 32% of orders)
  • 🕔 5PM–7PM (Dinner – 28%)
• Slowest Day: Tuesday (only 12% of weekly sales)

Operational Recommendations:
📌 Increase staffing during peak hours.
📌 Introduce Tuesday promotions (e.g., “Taco Tuesday” discounts).

Strategic Recommendations

1. Menu Optimization

• Expand American offerings (burgers, sandwiches).
• Revamp Mexican dishes (test new recipes or pricing).
• Bundle high-margin items (e.g., steak + wine pairings).

2. Improve Operations

• Dynamic staffing during peak hours.
• Inventory adjustments (stock more burger ingredients).

3. Marketing & Promotions

• Happy Hour deals (4PM–6PM) to boost off-peak sales.
• Loyalty rewards for frequent customers.

Technical Approach

SQL Analysis Workflow

1. Exploratory Queries
   • Counted menu items, checked price ranges.
   • Analyzed order volume trends.
2. Combined Data Analysis
   • Joined order_details and menu_items to track sales performance.
   • Aggregated spending by order to find high-value customers.
3. Time-Based Trends
   • Grouped orders by hour/day to detect busy periods.

🔗 See the full SQL code: [GitHub Repo Link]

Future Analysis Opportunities

This project focused on sales data, but deeper insights could come from:
📊 Customer segmentation (Who are the high-spenders?)
💰 Profitability analysis (Which items have the best margins?)
📅 Seasonal trends (How do sales change across quarters?)

Conclusion

By analyzing Taste of the World Restaurant’s sales data, I uncovered:
🔸 Popular dishes to expand (burgers, premium proteins)
🔸 Underperforming categories to improve (Mexican)
🔸 Peak hours to optimize staffing

Data-driven decisions like these can help restaurants increase revenue, reduce waste, and enhance customer experience.

Check the project details on my GitHub.