Food Delivery Dataset
March 5, 2025
The data set contains a month’s worth of sample delivery data from the earliest months of operation of a food delivery company. This isn’t the complete set, but it’s comprehensive enough where you should be able to get a good look at the business. Feel free to make any assumptions you need.
Your task is the following:
- Provide a set of recommendations on how to improve the business or product based on this data. This is intended to be fairly open-ended – there’re no right or wrong solutions! We’re more concerned with your approach and insights you uncover.
- Choose one of the recommendations/insights you uncovered (in #1) and outline an experiment you would like to run to test your suggested product/business recommendation. Please state your hypothesis, describe how you would structure your experiment, list your success metrics and describe the implementation.
- Let’s assume that the experiment you ran (in #2) proved your hypothesis was true. How would you suggest implementing the change on a larger scale? What are some operational challenges you might encounter and how would you mitigate their risk?
Below is a glossary of definitions for the variables included in the data set.
- Customer placed order datetime: Time that customer placed the order; the format is [day hour:minute:second]
- Placed order with restaurant datetime: Time that restaurant received order; the format is [day hour:minute:second]
- Driver at restaurant datetime: Time that driver arrives at restaurant; the format is [day hour:minute:second]
- Delivered to consumer datetime: Time that driver delivered to customer; the format is [day hour:minute:second]
- Driver ID: Unique identifier of driver
- Restaurant ID: Unique identifier of restaurant
- Consumer ID: Unique identifier of customer
- Delivery Region: City where restaurant is located
- Is ASAP: Equals TRUE for on-demand orders; FALSE for scheduled deliveries (e.g., a customer places an order at 10am for 12noon)
- Order total: Amount customer spent (including delivery fee); units are in dollars
- Amount discount: Amount of discounts redeemed (e.g., for referrals); units are in dollars
- Amount of tip: Amount of tip given; units are in dollars
- Refunded amount: Amount refunded to customer; units are in dollars
- Times: Time is in UTC and we operate on PDT (daylight savings)
The data you’ll get:
