Technology Category
- Functional Applications - Transportation Management Systems (TMS)
- Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
- Automotive
- Transportation
Applicable Functions
- Logistics & Transportation
- Quality Assurance
Use Cases
- Autonomous Transport Systems
- Transportation Simulation
Services
- Hardware Design & Engineering Services
- Testing & Certification
About The Customer
goUrban's customers range from businesses that use their technology to manage a fleet of vehicles to the individuals who actually ride and drive them. Their growing global customer base includes massive corporations, state municipalities, and individual consumers. They have expanded their roadmap to three different continents and counting. They also have a partner who is using their platform to provide mopeds for gig economy workers who use their mopeds for the day to make their deliveries and then return them. They have also been exploring corporate sharing for companies who want to provide this service internally for the benefit of their own employees.
The Challenge
goUrban, a company that started out renting mopeds to Vienna users, soon realized the third-party software they were using to run their shared mobility business could be created more efficiently in-house. They began developing and testing their own transportation sharing software on their own fleet. However, as they expanded their customer base globally, they faced challenges in scaling their software and ensuring its reliability. They also had to overcome the arduous aspects of rideshare software with seamless, easy-to-use, secure technology. The company found that the slow verification times and finicky software were hindering their growth. They also needed to implement better hardware, remove unnecessary steps for their consumers, and ensure top-notch safety and security.
The Solution
goUrban turned to Twilio for support in improving their platform. They implemented Twilio's Programmable Messaging API to send notifications and alerts to their users. This helped in improving the customer experience by providing instant ease of use and simplicity. For instance, a user could receive a notification reminder that they’ve left their vehicle parked for a long time and it’s still charging the user’s account. goUrban also used SMS to help users sign up and sign in, as well as a means of verification for new users. This helped in avoiding the creation of multiple accounts and ensured security. As goUrban continues to grow its global footprint, they’re also expanding their value to other areas beyond transportation, using Twilio technology to scale their services for both gig economy workers and corporate company sharing.
Operational Impact
Quantitative Benefit
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
Related Case Studies.
Case Study
Integral Plant Maintenance
Mercedes-Benz and his partner GAZ chose Siemens to be its maintenance partner at a new engine plant in Yaroslavl, Russia. The new plant offers a capacity to manufacture diesel engines for the Russian market, for locally produced Sprinter Classic. In addition to engines for the local market, the Yaroslavl plant will also produce spare parts. Mercedes-Benz Russia and his partner needed a service partner in order to ensure the operation of these lines in a maintenance partnership arrangement. The challenges included coordinating the entire maintenance management operation, in particular inspections, corrective and predictive maintenance activities, and the optimizing spare parts management. Siemens developed a customized maintenance solution that includes all electronic and mechanical maintenance activities (Integral Plant Maintenance).
Case Study
Airport SCADA Systems Improve Service Levels
Modern airports are one of the busiest environments on Earth and rely on process automation equipment to ensure service operators achieve their KPIs. Increasingly airport SCADA systems are being used to control all aspects of the operation and associated facilities. This is because unplanned system downtime can cost dearly, both in terms of reduced revenues and the associated loss of customer satisfaction due to inevitable travel inconvenience and disruption.
Case Study
IoT-based Fleet Intelligence Innovation
Speed to market is precious for DRVR, a rapidly growing start-up company. With a business model dependent on reliable mobile data, managers were spending their lives trying to negotiate data roaming deals with mobile network operators in different countries. And, even then, service quality was a constant concern.
Case Study
Digitize Railway with Deutsche Bahn
To reduce maintenance costs and delay-causing failures for Deutsche Bahn. They need manual measurements by a position measurement system based on custom-made MEMS sensor clusters, which allow autonomous and continuous monitoring with wireless data transmission and long battery. They were looking for data pre-processing solution in the sensor and machine learning algorithms in the cloud so as to detect critical wear.