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Vendor Order Performance Analysis
Food & Beverage Consumption
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Free
About
Online food orders for various restaurant vendors are detailed in this data, which includes information on both successful and unsuccessful orders. It offers valuable insights for analysing restaurant activity, order patterns, and operational efficiency within the online food delivery market. This information can be used to identify key performance trends and understand consumer preferences.
Columns
- date: The date of the orders.
- vendor_id: A unique identifier for the restaurant.
- chain_id: A unique identifier for the restaurant chain.
- city_id: An identifier for the city where the restaurant is located.
- spec: The specialisation or type of cuisine the restaurant offers (e.g., Sushi, Pizza).
- successful_orders: The total count of successful orders for that day.
- fail_orders: The total count of unsuccessful orders for that day.
Distribution
The dataset is provided in a single tabular CSV file named
orders.csv, with a file size of 4.65 MB. It contains 96,100 records across 7 columns.Usage
Ideal applications for this dataset include:
- Time series analysis of order volumes.
- Performance analysis of different restaurant vendors and chains.
- Market analysis of food preferences and specialisations by city.
- Developing predictive models for order success rates.
- Business intelligence dashboards for the food delivery industry.
Coverage
This dataset covers a time range from 2 June 2019 to 30 September 2019. It contains data for several cities, identified by
city_id. The restaurant specialisation column (spec) has 35 unique values, with "Суши" (Sushi) and "Пицца" (Pizza) being the most common, while 385 records have missing specialisation information.License
CC0: Public Domain
Who Can Use It
- Data Analysts: Can perform trend analysis and explore order patterns over time.
- Business Strategists: Can assess vendor performance and identify market opportunities.
- Data Scientists: Can build predictive models for demand forecasting and order failure.
- Restaurant Consultants: Can use the data to provide insights to clients on operational efficiency.
Dataset Name Suggestions
- Restaurant Vendor Online Food Orders
- Daily Food Delivery Order Statistics
- Vendor Order Performance Analysis
- Online Restaurant Order Success Rates
Attributes
Original Data Source: Vendor Order Performance Analysis
QUALITY