Uber’s New BHAG: UberPool

In their seminal 1994 book Built to Last: Successful Habits of Visionary Companies, Jim Collins and Jerry Poras coined the term BHAG (pronounced BEE-hag) — an acronym that stands for “Big Hairy AudaciousGoal.” Collins and Porras suggest that the very best companies set an audacious, very long-term goal that shines a light towards “an envisioned future.” BHAGs serve as a rallying cry for the company culture, an ambitious target for the future, and a focusing tool for corporate decision-making. As we turn the page to 2015, Uber has a new BHAG, and it’s name is UberPool.


Before we dive deep on UberPool and explain why this program is worthy of being the company’s BHAG, let us first look back at some of the key strategic decisions in the company’s history. If you understand the journey that the company has taken up until now, the more clear it will be why UberPool is the obvious next step.

Uber’s founding tagline was “everyone’s private driver.” Today, the company’s mission statement is “transportation as reliable as running water, everywhere for everyone.” The common component of both the original tagline and the new mission statement is the word “everyone.” In order for Uber to serve everyone it is critical that Uber not only achieve price leadership, but that the company continually search for new ways to deliver transportation at lower and lower price points. This goal – to deliver the highest possible value to the customer – is a key catalyst for UberPool.


Of course, Uber is not the first company to choose a corporate strategy of price leadership. Sam Walton and Jeff Bezos both espoused the benefits of rewarding customers with the highest possible value by delivering to them the lowest possible prices. What is really interesting is that both make the exact same non-obvious argument – that low prices are the very best way to maximize cash flow, and therefore equity value.

“… But this is really the essence of discounting: by cutting your price, you can boost your sales to a point where you earn far more at the cheaper retail price than you would have by selling the item at the higher price. In retailer language, you can lower your markup but earn more because of the increased volume.”
– Sam Walton, founder of Wal-Mart

“We’ve done price elasticity studies, and the answer is always that we should raise prices. We don’t do that, because we believe — and we have to take this as an article of faith — that by keeping our prices very, very low, we earn trust with customers over time, and that that actually does maximize free cash flow over the long term.”
– Jeff Bezos, CEO of Amazon

In a separate comment, Bezos was more direct in his commitment to delivering the lowest possible price point to Amazon’s customers, noting “There are two kinds of companies, those that work to try to charge more and those that work to charge less. We will be the second.” Like Wal-Mart and Amazon, this is Uber’s philosophy as well.


UberPool is actually Uber’s second major initiative targeted at lowering consumer prices. When Uber launched its low-cost UberX offering in the summer of 2012, the company quickly realized that the demand for its transportation services is HIGHLY elastic. As the company achieved lower and lower per-ride price points, the demand for rides increased dramatically. A lower price point delivered a much better value proposition to the consumer, yet still remained a great business decision due to the remarkable increase in demand.

Armed with this new data, Uber leaned on its legendary “math department” to help drive prices even lower. This is the name that founder and CEO Travis Kalanick has given to his team of scientists and hardcore mathematicians who own the back-end routing algorithms for Uber. Uber’s technology goes well beyond its client side smartphone applications; there is also a server-side intelligence system that provides demand prediction, congestion prediction, supply matching, supply positioning, smart dispatch, and dynamic pricing. These are the systems that help balance the more than one million rides per day that are matched on the Uber system.

The “math department” and management realized that if they could increase driver utilization (the number of rides per hour for a driver), then they could lower the price for the end user while maintaining earnings quality for the driver. Higher efficiencies through higher volumes and better algorithms could help deliver the desired lower price points and better cash flows. Interestingly, these lower price points would lead to more demand, even more liquidity, an even higher utilization, and then another incremental price decrease. Pretty quickly UberX passed UberBlack to become the highest volume service on the Uber platform.


Uber repeated this circular pattern so many times in so many different cities that some cities witnessed more than six price cuts in a brief two-year period. While these highly successful initiatives have lead to prices that can be as much as 40-50% below that of a taxi, this sheer number of price changes can be confusing to the ecosystem. This January, the company took an even bolder moveannouncing simultaneous cuts in 48 cities, and backing these up with income guarantees for drivers. How could they make such an aggressive move? Basically, the company’s immense historical database of supply and demand curves at different price points makes it easy to predict how these markets will evolve. This allows the company to “forward invest” capital to help these markets achieve lower consumer prices even faster. You might call this “betting on the math department.”


Which brings us to UberPool. The concept behind UberPool is rather straightforward (see graph below). Basically a single driver picks up not one, but two passengers who are headed in the same direction. She then drops off one passenger, and perhaps picks up a third before the first is dropped off. In this scenario, customers are literally “ride-sharing.” If you can manage the system such that each driver averages more than a single rider per trip, you can achieve an even HIGHER level of efficiency, and deliver even lower prices to the consumer. As you can see, this is the natural evolution after UberX and UberX price optimization.

This program is already up and running in San Francisco, New York, and Paris, and the company is already seeing habitual behavior with many riders using UberPool on the same route, five days a week.

Screen Shot 2015-01-27 at 11.18.09 PM

The idea of UberPool may seem simple, but the implementation is unquestionably not. If you previously studied combinatorial optimization in theoretical computer science or operations research you may be familiar with the “traveling salesman problem,” or TSP. TSP problems require computationally intensive integer linear programming techniques to find exact solutions. Since there are too many permutations for these problems to be accurately solved, sophisticated heuristic approximation methods have to be developed. It turns out that writing the algorithms for UberPool is quite a bit more complex than a simple TSP, because (1) you have many more than one “salesman,” (2) the destinations are dynamic, (3) new “salesmen” are constantly entering and leaving the system, (4) vehicles have limited seating capacity, and (5) new requests for rides are continually streaming in. This is not just closer to the dynamic mTSP (multiple TSP) problem, which falls under the intimidating classification of “NP-hard,” but moreso under the broader and even more formidable VRP (Vehicle Routing Problem). The math department has plenty to do.

So what makes UberPool worthy of being the defining initiative at Uber?

Read it here : http://abovethecrowd.com/2015/01/30/ubers-new-bhag-uberpool/

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