Visualizing the Transformation of Commerce & Communities through On-demand Services
Technology Category
- Platform as a Service (PaaS) - Application Development Platforms
- Sensors - GPS
Applicable Industries
- Cities & Municipalities
- Transportation
Applicable Functions
- Logistics & Transportation
- Procurement
Use Cases
- Last Mile Delivery
- Smart City Operations
About The Customer
Postmates' customers are diverse, ranging from busy parents, senior citizens, to people with mobility issues. The company covers 1/3 of US households, which is a lot of location data. The preferences of their customers change throughout the year, the week, and within a city itself. For example, they are healthier in summer than in winter, and their data can reveal when the flu is breaking out, and when ice coffee season is about to start. They also observe how what their customers want changes throughout the week. For instance, 30% of all bagel deliveries happen on Sunday and it’s a little bit more evenly distributed in New York City.
The Challenge
The challenge faced by Postmates, a leader in on-demand delivery in the US, was to efficiently connect consumers with merchants and deliver goods from any merchant to the customer's door in minutes. The company had to deal with the complexities of location data, as location carries weight and meaning. The company had to understand the preferences of customers which varied throughout the year, the week, and within a city itself. They also had to deal with the challenge of delivering a wide variety of items, from furniture to late-night medicine runs and diapers, across 300 cities in the US and Mexico.
The Solution
Postmates developed a solution that enabled anyone to tap a button on their phones and get anything from any merchant delivered to their door in minutes. They believed in the philosophy that their cities, towns, and communities are their warehouses. The company was able to deliver over 175 million items across 300 cities and Mexico, at all hours of the day. They also developed an e-bike and scooter program in multiple cities, and a robotics unit to increase efficiency in moving food products and parcels. They also used location data and demand patterns to help their larger partners plan how and where to launch new stores and penetrate new markets.
Operational Impact
Quantitative Benefit
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