Unified Restaurant Establishments with Rich Metadata¶
Dotlas maintains a unified and collection of restaurant Points-of-Interest (PoIs) across multiple online and offline data sources linked together as a single-source-of-truth places dataset. This dataset consolidates and enriches records from delivery platforms, review aggregators, licensing authorities, brand directories, and proprietary field research using advanced entity resolution techniques.
Each restaurant entity is matched across sources using a combination of geospatial proximity, name similarity, brand hierarchy, and contextual metadata—ensuring a high-confidence, non-redundant view of physical outlets and their associated brands.
The end result is a ready to use collection of restaurant records with enhanced details from multiple sources as one.
Potential Sectors for Use-Cases¶
- Food & Beverages (F&B)
- GIS (Geographic Information Systems) and Geocoding Services
- Marketing and Advertising Leads
- Competitor Monitoring & Market Analysis
- Financial tickering of F&B brand performance
- Franchise network tracking and portfolio management
- Site Selection & Suitability Analysis
- Real Estate Proximity Analysis
Data Dictionary¶
Standard Schema¶
The following fields are available as persistent data points irrespective of which market or country is chosen
| Column Name | Data Type | Description |
|---|---|---|
| restaurant_id | string |
Unique Dotlas-assigned identifier for the restaurant. Useful for tracking the same physical restaurant over time. |
| brand_id | string |
Unique Dotlas-assigned identifier for the chain / brand of the restaurant |
| brand_name | string |
The name of the chain or brand when applicable, else the name of the restaurant or outlet |
| country | string |
The country in which the restaurant is located |
| city | string |
The city where the restaurant is located |
| subregion | string |
The state / district / territory in which the restaurant is located. Example: California, Normandy, Baden-Württemberg, etc. |
| zipcode | string |
The postal code of the restaurant, if available |
| area | string |
The Dotlas-assigned colloquial neighbourhood of the restaurant, if available for our supported urban boundaries. Example: Downtown, Marina, Morningside Heights, etc. |
| latitude | float |
The exact location coordinate of the restaurant (with customizable projections) |
| longitude | float |
The exact location coordinate of the restaurant (with customizable projections) |
| address | string |
The full address of the restaurant, including street, city, state, and postal code |
| rating | float |
The average rating of the restaurant, split by source if required |
| review_count | int |
The number of reviews for the restaurant, split by source if required |
| categories | string[] |
The cuisine tags that the restaurant lists itself for keyword search |
| restaurant_website | string |
The official website of the restaurant, if available |
| telephone_number | string |
The desk phone number of the restaurant, if available |
| email_address | string |
The email address of the restaurant, if available |
string |
The Instagram handle of the restaurant, if available. Other social handles can be made available on request |
Specific Schema¶
The following fields can be made available for specific markets or countries, based on the sources available.
| Column Name | Data Type | Description |
|---|---|---|
| description | string |
A description of the restaurant or brand from online sources or their own site |
| facilities | string[] |
The facilities available at the restaurant, such as Wi-Fi, Parking, Outdoor Seating, etc. |
| operating_hours | string |
The operating hours of the restaurant, including opening and closing times for each day of the week |
| dietary_restrictions | string[] |
The dietary restrictions that the restaurant caters to, such as Vegetarian, Vegan, Gluten-Free, etc. |
| delivery_options | string[] |
The delivery options available for the restaurant, such as In-House, Third-Party, Pickup, etc. |
| payment_methods | string[] |
The payment methods accepted by the restaurant, such as Cash, Credit Card, Mobile Payment, etc. |
| legal_entity | string |
The legal entity under which the restaurant brand operates, such as local franchising company names or parent companies |
Bespoke Schema¶
The following fields are made available for any market on-request using Dotlas market algorithms and economic transformation pipelines. Contact us for more details.
| Column Name | Data Type | Description |
|---|---|---|
| restaurant_type | string |
A categorization of the restaurant based on natural language understanding of their menu offering, service type, and other factors. Example: Fast Food, Casual Dining, Fine Dining, etc. |
| outlet_dnps | float |
An NPS-like metric that measures the likelihood of visiting customers recommending the restaurant to others, based on sentiment analysis of reviews and social media mentions. See more info about dNPS |
| nearby_competitors | string[] |
A list of nearby competitors for the restaurant, based on geospatial proximity and similarity to the current outlet's offerings by price, services, location and more. A list of restaurant_id are used to populate this field. |
| visitation_trends | float[] |
A time series of visitations to the restaurant over time, based on geospatial data and other sources. This can be used to track the performance of the restaurant over time and identify trends in customer behavior. |
| search_ranking_index | float |
A score that indicates the visibility of the restaurant in search results, based on its online presence, delivery apps, and other places where customers discover this restaurant. This can be used to track the performance of the restaurant in search engines and identify opportunities for improvement. |
| promotion_index | float |
A score that indicates the effectiveness of the restaurant's promotions and advertisements, based on customer engagement and conversion rates. This can be used to track the performance of the restaurant's marketing efforts and identify opportunities for improvement. |
Products¶
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🇺🇸 United States of America
A collection of over 1M restaurant venues in the United States, across all states and territories with rich information around facilities, operating hours, contact details, and other attributes.
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🇨🇦 Canada
A collection of over 120k restaurant venues in Canada, with information from multiple popular online sites and sources.
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🇦🇪 United Arab Emirates
A collection of over 40k restaurant venues in the UAE, including restaurants in Dubai, Abu Dhabi, Sharjah, etc.
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🇧🇭 Bahrain
A collection of over 5k restaurant venues in Bahrain, including all governorates.
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🇰🇼 Kuwait
A collection of over 12k restaurant venues in Kuwait, including all major restaurant sources, delivery aggregators and other sources.
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🇶🇦 Qatar
A collection of over 8k restaurant venues in Qatar, including all major restaurant sources, delivery aggregators in Doha, Lusail and other areas.
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🇸🇦 Saudi Arabia
A collection of over 45k restaurant venues in Saudi Arabia, including all major restaurant sources, delivery aggregators in Jeddah, Riyadh, Dammam, etc.
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🗺️ Other Countries
Contact us if you're interested in dataset of other countries. Based on the sources available, the schema of new markets are quite likely similar to existing samples.
Don't see a listed country for which you require restaurant data?