DoorDash uses Machine Learning and Optimization Models To Enhance Customer Experience: Here’s How!

DoorDash, America’s largest food delivery platform, has more than 56% market share and is the 2nd largest in the world.

DoorDash is one of many food platforms/apps that deliver restaurant food to customers. However, there are plenty of others. The platform has carved a niche in this highly competitive market by only doing one thing: Delighting customers consistently.

It won’t be wrong to mention that DoorDash foodies are known for being loyal and dedicated to their orders.

How is it possible for DoorDash, a 9-year-old company, to consistently delight its customers and vendors? What magical recipes were used to decode the needs of their customers and solve their problems?

Machine Learning and Optimization Models are the key to this answer.

We’ll be discussing that in the next few minutes, but let’s first look at DoorDash. It will amaze you!

DoorDash is one of the most popular food delivery platforms!

After a complaint from a local shop owner about poor delivery, the co-founders of DoorDash began to work on a food and grocery delivery app. In exchange for a 7% share, they were awarded seed funds of $120,000 from the Y-Combinator, a renowned incubator for startups. This started a remarkable story of growth, expansion, and expansion that continues today. According to some reports, their growth rate was an amazing 20% per week within the first few months after they were incorporated.

They overtook UberEats within 5 years of their launch to become America’s 2nd largest food delivery app. Next year, they defeated GrubHub and became the #1 food delivery service in America.

Impressive, isn’t it?

The Working Model and Business Mechanism of Doordash

DoorDash is a middleman. Delivery partners pick up the delivery from restaurants, and then deliver it to the buyer.

It is the most prominent online food ordering and delivery company that acts as an intermediary between potential buyers and local vendors. These vendors are available to fulfill the needs of customers who want their food delivered right to their door.

When we look at DoorDash’s business model, there are three main entities: customers who place orders; vendors or restaurants that cook the food; and dashers, or delivery executives who pick up food orders from restaurants to deliver them to customers.

It is very simple to generate revenue: The restaurant earns a commission on each order.

DoorDash appears to be like any other food delivery service, with a straightforward business model and revenue model.

What is the appeal of ML and Optimization Models to consumers?

These are just a few examples of machine learning that they use to delight customers.

1. Once the customer places an order, the process begins immediately

Machine Learning is activated right from the beginning of the user journey, when the customer places an order.Two processes begin immediately after the order has been placed. a) The order details are shared with the vendor (restaurant), so they can prepare the food. b) The algorithm searches for the closest Dasher (delivery executive), which can quickly pick up the order from the restaurant.

2. Transactional data is moved to an analytics database

All key events, such as customer orders, delivery pickups or drop-offs are stored in a central database. The transactional data is then moved to an analytics database for the sole purpose of delighting customers.

Machine Learning is also integrated into DoorDash so that DoorDash understands the needs and wants of customers.

3. Elimination of Routing Issues with ML

DoorDash has found a solution to the Last-mile delivery issue. This is the Holy Grail for ecommerce. Many food orders need to be delivered; thus, there is a finite number of Dashers available and many stops between.

The platform is different from FedEx and UPS. The food must be delivered in 30-40 minutes.

How can the platform guarantee timely delivery while using minimal resources?

Again, AI and ML works its magic! To calculate the best route to deliver food, the tech uses a variety of data points, including the time it takes to prepare the food, the location of the closest Dasher, parking issues and current traffic. It also considers customer locations and previous interactions.

4. Updation of ML Models

It is easy to create machine learning models using transactional data, but it can be difficult to update them with new data.

DoorDash uses historical data to train its models. After the model is trained, they use historical data to backtest it. Then, gradually, they put the model into production as a “shadow”.

There are currently two machine learning models in use, but only one of them is producing predictions in run time, which will have a direct impact on DoorDash’s delivery process.

5. Machine Learning and Demand Prediction

DoorDash has developed powerful Machine Learning models that can predict the demand, and allocate resources accordingly to achieve the best results.

They have a centralized analytics group, which will include a Machine Learning Engineer (backend), Data Scientist, Product Engineer and a Data Scientist. They will work together in a single room to understand the data generated by a subset of customers and create prediction models for future demand.

6. DoorDash has tools for machine learning

For machine learning, they rely on Python-based open libraries like LightGBMs. Keras, which is a package that they use to optimize the User Interface based on user behavior and predictive analytics, is another important one.

They use a mix of Python and R for exploratory analysis and visualization and Charteo or Tableau for business reporting.

DoorDash can also deploy machine learning in many other areas, such as marketing initiatives and payment confirmations, discounts to be displayed, moment marketing initiatives, restaurant ranking and profiling of dishes. This ensures that customers get what they want, at their convenience and time.

To Sum Up!

How intrigued are you by the information?

Techugo: a food delivery app development company is the place to go if you want to learn more about Machine Learning and Data Optimization Models that can be used to create a pleasant user experience and make customers happy.

If you’re looking to launch an app like DoorDash, consult us and we will help you create a great success story!