UberPool, a service where a user shares a ride and splits cost with another traveler(s) who happens to be going in the same direction, is revolutionizing transportation by making it affordable to everyone, reducing congestion and pollution. Launched in Aug’14 in selected cities, UberPool has already created significant impact. UberPool makes half of all trips in San Francisco and in the first eight months of operations in Los Angeles, it cut the number of miles driven across town through ride-sharing by 7.9 million and prevented CO2 pollution by 1,400 metric tons.
Biggest value to users is cost saving. Riders can save up to 50% by adding a few minutes of time per trip. With lower prices, people can move past car ownership, as taking Uber becomes less expensive than using and maintaining a personal vehicle.
Users are price sensitive, a behavior that is seen often when users do not call an Uber during price surge. Uber’s business model is to get exponential increase in number of rides even though at lower price points to increase overall revenue and stickiness. For drivers, more users and ride requests result into higher overall revenues. Drivers’ utilization increases and driving cost/user reduces through optimal routes to minimize commute and maximize riders. More number of drivers ensures higher availability and lower turn-around times for users and in-turn gets more users, creating a self-reinforcing virtuous cycle.
Another business goal of Uber is expand and own the transportation market by capturing users across multiple segments, from those who want affordable UberPool to those who want premium experience through UberBlack, becoming the go-to transportation service for everyone.
- High liquidity: For a match, many users need to going in the same direction at any given time. By lowering prices, Uber is gaining a large share of users. Its market leadership and strong brand also helps.
- Technology: Uber’s data centric platform is key its success. Given a set of ride requests, it is very difficult to find the optimal route to minimize time and commute for a driver (Traveling Salesman Problem). UberPool is solving this in real time across millions of users while ensuring reliability and quick service. Uber applies machine learning and data science to learn user behavior and predict rides as well as predict macro trends based on the day of the week and prevailing price points.
- Built on existing infrastructure and network: UberPool uses the same network of drivers, reaches users by a small icon in the existing app by providing the same user experience (instead of launching a new app), uses the same brand and is built upon the existing technology infrastructure. UberPool is leveraging everything that is successful with Uber, thus increasing Uber’s network effects even further. The marginal costs of launching UberPool for Uber are much lower than a new competitor.
- Leveraging external factors: People’s work hours are similar to a large extent. Many cities have concentrated business hubs and residential areas, as a result of which large number of users move from same source location to same destination around the same time frame. The fact that real estate is not increasing at the same rate as population is helping UberPool. Uber is utilizing these factors in choosing urban cities with dense working population to launch (eg. San Francisco, New York, Paris) and also, positioning drivers at right locations at right times.
Alignment of business and operating models
UberPool’s operating model leverages technology to be able to find ‘n’ users, say 3, to share a ride, thus reducing the cost per user by 3 resulting in lower prices for users and lower cost/user for drivers. This creates business value for both users and drivers. High liquidity or scale in terms of number of users is key for ride sharing to work. Lower prices enabled by its operating model enable it to get the critical mass of users needed for UberPool. Its technology is complex and advanced, hard for competitors to replicate. As with machine learning, its technology gets much better with time as they are able to train their algorithms better and get more information about users and drivers.