Restaurant Performance & Menu Optimization

This dataset provides detailed information on menu items offered by restaurants on the platform, useful for pricing, availability, and performance analysis.

• Contains each menu item's ID, name, description, price, and availability status.
• Linked to the restaurant_id to support restaurant-level product analysis.
• Enables evaluation of pricing strategies and menu diversity across restaurants.
• Helps identify top-performing and underperforming items based on sales (when joined with order data).
• Useful for assessing item availability, seasonal offerings, and product lifecycle.
• Supports menu optimization by analyzing popularity, pricing trends, and item attributes.
• Ideal for building recommendation systems, menu engineering dashboards, and profitability models.

This dataset contains structured order-level data used to analyze transaction patterns, revenue generation, and customer activity across a food delivery platform.

• Captures each order placed on the platform, including order ID, customer ID, restaurant ID, order date, and total price.
• Includes important status fields such as payment_status and is_cancelled for identifying successful transactions.
• Enables time-series analysis using the order_date field to explore daily, weekly, or seasonal trends.
• Supports revenue tracking and performance benchmarking across customers and restaurants.
• Useful for calculating metrics like average order value, order frequency, and churn indicators.
• Can be joined with customer, restaurant, and order_items tables for richer insights.
• Ideal for demand forecasting, customer behavior analysis, and operational reporting.

This dataset contains customer-generated reviews and ratings, enabling analysis of satisfaction and service quality across restaurants.

• Includes fields like review_id, customer_id, restaurant_id, rating, and review text.
• Supports evaluation of customer sentiment and dining experience.
• Enables tracking of average ratings and feedback trends over time.
• Useful for identifying top-rated restaurants and flagging service issues.
• Can be joined with orders and restaurant data to enrich analysis.
• Ideal for sentiment analysis, quality assurance, and performance benchmarking.
• Helps improve customer satisfaction, retention, and operational feedback loops.

This dataset contains structured order-level data used to analyze transaction patterns, revenue generation, and customer activity across a food delivery platform.

• Captures each order placed on the platform, including order ID, customer ID, restaurant ID, order date, and total price.
• Includes important status fields such as payment_status and is_cancelled for identifying successful transactions.
• Enables time-series analysis using the order_date field to explore daily, weekly, or seasonal trends.
• Supports revenue tracking and performance benchmarking across customers and restaurants.
• Useful for calculating metrics like average order value, order frequency, and churn indicators.
• Can be joined with customer, restaurant, and order_items tables for richer insights.
• Ideal for demand forecasting, customer behavior analysis, and operational reporting.

This dataset contains filtered restaurant information based on actual customer order activity, focusing on restaurants that served a sample of 5,000 distinct customers.

• Contains data on restaurants that served 5,000 unique customers who placed at least one order.
• Filtered using a subquery to include only restaurants tied to active customer transactions.
• Each row is a restaurant record from the app.restaurant table.
• Enriched via a JOIN with app.orders to ensure restaurants have real customer interactions.
• Useful for analyzing:
  • Customer-restaurant relationships
  • Restaurant exposure and customer reach
  • Performance benchmarking for active restaurants
• Ideal for downstream analysis such as:
  • Customer preferences
  • Marketing segmentation
  • Restaurant engagement scoring

This dataset captures operational details of food delivery orders and is designed to support performance analysis and logistics optimization:

• Provides structured delivery data from a food delivery application.
• Contains unique identifiers for orders and delivery partners.
• Includes timestamps for pickup and drop-off events.
• Tracks delivery status (is_delivered) and delay reasons.
• Offers estimated vs. actual delivery time comparisons.
• Enables analysis of delivery efficiency and operational bottlenecks.
• Useful for performance monitoring of delivery operations and partners.
• Supports use cases in logistics optimization, SLA tracking, and time-series analysis.