DoorDash is the largest food delivery service in the US, holding more than half of the market share. It’s also the second-largest globally. While many companies provide food delivery, DoorDash has managed to stay ahead by ensuring customers are satisfied. The secret behind this success? A lot of smart machine learning.

Let’s dive into how DoorDash’s technology works and why it continues to lead the food delivery industry.

DoorDash: A Quick Overview

Founded in 2013 by a group of Stanford students, DoorDash quickly became a top player in the food delivery market, eventually surpassing competitors like UberEats and GrubHub. Today, it operates in over 7,000 cities and has:

• 25 million monthly users
• 10 million paying subscribers
• 2 million delivery drivers (known as Dashers)
• 450,000 restaurant partners

In 2021, DoorDash generated nearly $5 billion in revenue, completing almost one billion deliveries.

How DoorDash Makes Money

DoorDash serves three key groups: customers who place orders, restaurants that prepare the food, and Dashers who deliver it. The company takes a commission from each order, in addition to revenue from ads and branding services for restaurants.

What really sets DoorDash apart, though, is its focus on enhancing the user experience through machine learning.

How Machine Learning Helps DoorDash

DoorDash uses machine learning in several ways to optimise the delivery process and keep customers happy. Here’s how:

1. Smarter Order Management

Once you place an order, DoorDash uses machine learning to notify the restaurant and find the nearest Dasher to pick up the food.

2. Personalised Recommendations

DoorDash collects data from users, such as their previous restaurant choices, to suggest personalised options for future orders. This encourages users to return.

3. Solving Delivery Challenges in Real-Time

One of the trickiest aspects of food delivery is navigating traffic, roadworks, or parking issues. DoorDash uses machine learning to assess these variables in real time and calculate the fastest routes, ensuring timely deliveries.

4. Constant Improvement with New Data

Every time DoorDash gathers new data, it updates its machine learning models. These models are tested in ‘shadow mode’ to compare performance, and if the new version performs better, it replaces the old one.

5. Forecasting Demand

To prepare for busy periods, DoorDash uses machine learning to predict when and where orders will be placed. This helps the company manage resources, such as drivers and restaurant staff. It can even adjust restaurant menus in real time to highlight items that are expected to be in high demand.

The Technology Powering DoorDash’s Success

To support all of this, DoorDash relies on technologies like Python and machine learning libraries such as LightGBMs. For data visualisation, it uses tools like Tableau, and for data processing, it uses Apache Airflow.

In summary, DoorDash’s success isn't just about delivering food; it’s about using technology to continuously improve its services for both customers and restaurant partners. By leveraging machine learning to optimise every aspect of its business, DoorDash ensures it remains ahead of the competition.