Tomorrow.io Case Studies Driving Revenue with Predictive Weather: A Case Study on Uber and Tomorrow.io

Edit This Case Study Record

	Driving Revenue with Predictive Weather: A Case Study on Uber and Tomorrow.io Tomorrow.io

Driving Revenue with Predictive Weather: A Case Study on Uber and Tomorrow.io

Tomorrow.io

Technology Category	Platform as a Service (PaaS) - Application Development Platforms Sensors - Level Sensors
Applicable Industries	Transportation
Applicable Functions	Procurement Sales & Marketing
Use Cases	Demand Planning & Forecasting Real-Time Location System (RTLS)
Services	System Integration Training
Challenge	Uber, a leading ride-sharing company, was seeking ways to enhance its operational efficiency and drive revenue growth. The company recognized the significant impact of weather conditions on its operations, including factors such as rain, snow, winds, fog, temperature, and air pollution. However, the challenge lay in accurately predicting these weather elements and integrating this data into their decision-making process in real-time. The company also faced issues with weather data coming from multiple sources, leading to inconsistencies and confusion. Furthermore, Uber needed a solution that could provide historical weather modeling to understand past trends and predict future weather impacts on their business. Read More
About Customer	Uber is a multinational ride-hailing company offering services that include peer-to-peer ridesharing, ride service hailing, food delivery, and a micromobility system with electric bikes and scooters. The company is based in San Francisco and has operations in over 900 metropolitan areas worldwide. It is one of the largest firms in the gig economy, with millions of workers worldwide. Uber's platform is accessible via its websites and mobile apps. The company's mission is to ignite opportunity by setting the world in motion, and it aims to transform the way people connect with their communities and revolutionize the way people move. Read More
Solution	Uber entered into a multi-year agreement with Tomorrow.io, a predictive weather platform. Tomorrow.io's weather API was integrated across Uber's platform, providing high-resolution, minute-by-minute weather intelligence at a hyperlocal level. This allowed Uber to make decisions in advance and in real-time, automating workflows and business decisions. The solution also provided a single source of weather data, eliminating confusion and ensuring everyone in the company was working off the same data, recommendations, and protocols 24/7. Additionally, Tomorrow.io's Weather for AI (WAI) enabled Uber to generate accurate historical weather information for millions of locations globally. This data was then used to train AI-driven models to predict future weather impacts on Uber's operations. Read More Log in to view content
Contents

Technology Category

Platform as a Service (PaaS) - Application Development Platforms

Sensors - Level Sensors

Applicable Industries

Transportation

Applicable Functions

Procurement

Sales & Marketing

Use Cases

Demand Planning & Forecasting

Real-Time Location System (RTLS)

Services

System Integration

Training

Challenge

Uber, a leading ride-sharing company, was seeking ways to enhance its operational efficiency and drive revenue growth. The company recognized the significant impact of weather conditions on its operations, including factors such as rain, snow, winds, fog, temperature, and air pollution. However, the challenge lay in accurately predicting these weather elements and integrating this data into their decision-making process in real-time. The company also faced issues with weather data coming from multiple sources, leading to inconsistencies and confusion. Furthermore, Uber needed a solution that could provide historical weather modeling to understand past trends and predict future weather impacts on their business.

About Customer

Uber is a multinational ride-hailing company offering services that include peer-to-peer ridesharing, ride service hailing, food delivery, and a micromobility system with electric bikes and scooters. The company is based in San Francisco and has operations in over 900 metropolitan areas worldwide. It is one of the largest firms in the gig economy, with millions of workers worldwide. Uber's platform is accessible via its websites and mobile apps. The company's mission is to ignite opportunity by setting the world in motion, and it aims to transform the way people connect with their communities and revolutionize the way people move.

Solution

Uber entered into a multi-year agreement with Tomorrow.io, a predictive weather platform. Tomorrow.io's weather API was integrated across Uber's platform, providing high-resolution, minute-by-minute weather intelligence at a hyperlocal level. This allowed Uber to make decisions in advance and in real-time, automating workflows and business decisions. The solution also provided a single source of weather data, eliminating confusion and ensuring everyone in the company was working off the same data, recommendations, and protocols 24/7. Additionally, Tomorrow.io's Weather for AI (WAI) enabled Uber to generate accurate historical weather information for millions of locations globally. This data was then used to train AI-driven models to predict future weather impacts on Uber's operations.

Impact #1	The integration of Tomorrow.io's predictive weather platform significantly enhanced Uber's operational efficiency and decision-making process. The ability to predict weather impacts in real-time allowed Uber to optimize routes and staffing for maximum efficiency, leading to cost savings and increased revenue opportunities. The solution also enabled Uber to calculate offers in advance, leading to more effective marketing and customer product offering campaigns. Furthermore, the use of a single trusted source of weather data eliminated confusion and ensured seamless communication within the company. The historical weather modeling feature provided valuable insights into past trends, enabling Uber to better predict future weather impacts on their business.

Impact #1

The integration of Tomorrow.io's predictive weather platform significantly enhanced Uber's operational efficiency and decision-making process. The ability to predict weather impacts in real-time allowed Uber to optimize routes and staffing for maximum efficiency, leading to cost savings and increased revenue opportunities. The solution also enabled Uber to calculate offers in advance, leading to more effective marketing and customer product offering campaigns. Furthermore, the use of a single trusted source of weather data eliminated confusion and ensured seamless communication within the company. The historical weather modeling feature provided valuable insights into past trends, enabling Uber to better predict future weather impacts on their business.

Benefit #1	Decreased Customer Acquisition Cost (CAC) by 20% using real-time and predictive weather for direct response.
Benefit #2	Increased Average Revenue Per User (ARPU) by 10% with weather-based user programs.
Benefit #3	Improved customer Estimated Time of Arrival (ETAs) by 25% during specific weather events.

Benefit #1

Decreased Customer Acquisition Cost (CAC) by 20% using real-time and predictive weather for direct response.

Benefit #2

Increased Average Revenue Per User (ARPU) by 10% with weather-based user programs.

Benefit #3

Improved customer Estimated Time of Arrival (ETAs) by 25% during specific weather events.

Download PDF Version

Overview

Driving Revenue with Predictive Weather: A Case Study on Uber and Tomorrow.io

Operational Impact

Quantitative Benefit