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USA – Pizza Orders Analysis – Power BI dashboard Tutorial

This is a Power BI dashboard titled “USA – Pizza Orders Analysis”, and it presents a comprehensive overview of pizza order trends across various dimensions. Here’s a breakdown and explanation of each visual and how businesses can benefit from such a dashboard:

USA - Pizza Orders Analysis - Power BI dashboard Tutorial

Dashboard Breakdown

Top KPIs (Top Row Cards)

  1. Orders Count – 1004 total orders.
  2. Avg Delivery Duration – 29.49 minutes.
  3. Avg Toppings Count – 3.36 toppings per pizza.
  4. Avg Distance – 4.95 km per delivery.
  5. Average Topping Density – 0.71 (likely a derived metric of toppings per km or per slice).

Get the Dataset here: https://github.com/slidescope/data/blob/master/Enhanced_pizza_sell_data_2024-25.xlsx

Orders Count by City (Bar Chart)

Shows cities with the highest number of pizza orders.
Top City: Atlanta (78 orders)

Count & Avg Delivery Duration by Restaurant (Bar + Line Combo)

Combines:

  • Bar: Number of orders per restaurant (Dom, Pap, Lit, Mar, Piz)
  • Line: Average delivery duration.
    Insight: ‘Dom’ has highest orders but also the longest delivery time.

Count & Avg Delivery Duration by Category (Donut Chart)

Shows:

  • Count of orders by pizza category.
  • Avg delivery duration visually hinted.
    Majority of orders (71%) fall under one major category.

Order Count by Payment Method (Bar Chart)

Reveals customer preference in payments:

  • Highest: Card
  • Digital methods like UPI & Wallets also prominent.

Orders Count by State (Map)

Choropleth map showing regional distribution of orders.
Darker states = more orders

Orders by Month & Pizza Size (Stacked Bar)

Shows seasonality in pizza orders and distribution by pizza size.
Trend: Orders are higher between August to January.

Filter Panel (Right Sidebar)

Users can slice data by:

  • City
  • Pizza Type
  • Restaurant
  • State
  • Payment Method

Business Benefits of This Dashboard

1. Geo Targeting & Expansion Planning

  • Identify top-performing cities/states (e.g., Atlanta, Georgia).
  • Spot underperforming regions needing marketing or operational improvement.

2. Delivery Optimization

  • Compare delivery durations across restaurants.
  • Improve logistics where delays are high (e.g., Domino’s 30.3 mins).

3. Menu Strategy

  • Understand topping trends and pizza category preferences.
  • Launch new combos or pricing based on topping density insights.

4. Payment System Enhancement

  • Track user preference for payment modes.
  • Promote underused but cost-effective methods like Wallets or loyalty points.

5. Seasonal Campaigns

  • Use monthly trends to plan campaigns during order peaks (e.g., holidays).

6. Restaurant Benchmarking

  • Track individual restaurant performance.
  • Optimize operations or renegotiate contracts with low-performing outlets.

7. Customer Experience Improvement

  • Analyze delays per restaurant or category.
  • Increase customer satisfaction with data-driven improvements.

📌 Conclusion

A dashboard like this enables real-time, data-driven decisions in a competitive food delivery environment. It combines sales, customer behavior, geography, and operations to give a 360° view, which helps pizza chains or delivery partners boost performance, reduce costs, and improve customer satisfaction.