Food-Delivery Statistics

Dotlas maintains a high-frequency and near real-time measurement dataset that captures estimated delivery times and fees charged by restaurants on food delivery platforms across all observable neighborhoods in a city.

For each restaurant, we simulate a customer located in various neighborhoods and collect:

• Estimated Time of Arrival (ETA)
• Delivery Fee charged by the platform
• Neighborhood of the simulated customer location
• Time of Day (breakfast, lunch, dinner slots)

This dataset enables powerful analysis of delivery experience across:

• Brands and cuisines
• Time slots and service load
• Neighborhoods and zones
• Delivery aggregators and pricing strategies

Unlike static attributes, these signals reflect dynamic platform conditions including batching, traffic, surge pricing, and operational efficiency.

Use-Cases

• Average delivery times per brand, cuisine, or zone
• Fee competitiveness across platforms and locations
• Identify underserved neighborhoods with high demand but slow delivery
• Compare delivery performance across time of day
• Benchmark competitor SLAs by area
• Inform fulfillment strategy and ghost kitchen expansion
• Coordinate promo timings to match fastest service windows

Sample Dashboards or Analyses reflective of this data:

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Data Dictionary

• Access on Databricks

Each record represents the delivery stats of a restaurant to a specific neighborhood at a given time of day.

Column Name	Data Type	Description
restaurant_id	string	Unique Dotlas-assigned identifier for the restaurant
brand_id	string	Dotlas-assigned brand/group the restaurant belongs to
neighbourhood_id	string	Dotlas-assigned ID for the neighborhood receiving the delivery
city	string	City in which the neighborhood and restaurant are located
delivery_eta_minutes	integer	Estimated delivery time to this neighborhood (in minutes)
delivery_fee	float	Delivery fee charged for the given route and time
currency	string	Currency of the delivery fee
delivery_service	string	Platform from which the ETA and fee were collected (e.g. Talabat, Jahez)
meal_period	string	Time window when measurement occurred: breakfast, lunch, dinner
snapshot_at	timestamp	Timestamp when the ETA and fee were recorded

Delivery Radius Data Product

For spatial modeling of restaurant reach, Dotlas also offers a polygonal dataset that maps the delivery footprint of each restaurant by time of day and platform. The Delivery Radius dataset contains per-restaurant service areas that define where delivery is offered (and where it isn't), based on real-world ETA coverage.

Each polygon includes:

• restaurant_id, meal_period, delivery_service, and geometry
• Areas where ETAs and fees are actually reported by the platform

This allows visualization of:

• Reach expansion or contraction by daypart
• Delivery zone gaps and opportunities
• Platform-specific coverage differences