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Low-Fare Ridesharing Options

A New Uber Low-Fare Option is Being Tested Internally

— June 22, 2018 — Tech
A new Uber low-fare option is offering riders a more affordable option when choosing between all of Uber's various ride options. The new feature is still being tested but aims to offer a lower fare at the cost of a longer wait time. The new Uber low-fare option has not been officially announced by the company but was instead leaked by an Uber employee in a tweet showing a screenshot of the cheaper fare option.

Uber has made a statement regarding the low-fare option and has stated that affordability is a prime concern of the company and it is attempting " to save money in exchange for a later pickup.” The new Uber low-fare option will allow Uber to better control the fluctuating prices of rides, depending on time of date, and will aim to take pressure off the service during busy periods.


Image Credit: AP Photo/J. Scott Applewhite
Trend Themes
1. Affordable Ridesharing Options - Disruptive innovation opportunity: Develop a ridesharing platform that offers consistently low fares and shorter wait times without compromising quality.
2. Dynamic Pricing Solutions - Disruptive innovation opportunity: Create a technology platform that accurately predicts demand and adjusts prices in real-time, providing customers with cost-effective options.
3. Optimized Ride Distribution - Disruptive innovation opportunity: Implement an algorithmic solution that efficiently matches riders and drivers, reducing wait times and maximizing utilization.
Industry Implications
1. Transportation - Disruptive innovation opportunity: Revolutionize the transportation industry by leveraging technology to offer affordable and efficient ridesharing services.
2. Technology - Disruptive innovation opportunity: Develop innovative technological solutions that facilitate dynamic pricing, optimization, and improved customer experiences in the ridesharing market.
3. Data Analytics - Disruptive innovation opportunity: Utilize data analytics and machine learning to analyze ride patterns, optimize routes, and improve overall efficiency in the ridesharing ecosystem.
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