Kamakshi Duvvuru Uber Case Study & Feature Proposal

Research:

Sources To gain a better understanding of Uber’s business and key performance indicators (KPIs), I used the following sources:

· Uber Data Blog (http://blog.uber.com/tag/uberdata/) • Uber’s internal deck from December 2013 (http://www.businessinsider.com/uber-revenue-rides-drivers-and-fares-2014-11?op=1) • Wall Street Journal: Riding in Uber’s Car Pool Turns Into a Lonely Trip (http://www.wsj.com/articles/riding-in-ubers-car-pool-turns-into-a-

lonely-trip-1418776119) • Bill Gurley’s blog, Above The Crowd

o Uber’s new BHAG: UberPOOL (http://abovethecrowd.com/2015/01/30/ubers-new-bhag-uberpool/) o Uber’s TAM (http://abovethecrowd.com/2014/07/11/how-to-miss-by-a-mile-an-alternative-look-at-ubers-potential-market-size/) o Uber’s Dynamic Pricing Model (http://abovethecrowd.com/2014/03/11/a-deeper-look-at-ubers-dynamic-pricing-model/)

· Travis Kalanick interviews o DLD15 (https://www.youtube.com/watch?v=iayagHygV0Q) o TechCrunch Disrupt 2014 (https://www.youtube.com/watch?v=s5zXIjGlzDU) o Code Conference 2014 (http://recode.net/2014/06/08/the-17-billion-man-full-code-conference-video-of-ubers-travis-kalanick/) o Brainstorm Tech 2013 (https://www.youtube.com/watch?v=vGbuitwkZiM) o LeWeb 2014 (https://www.youtube.com/watch?v=vnkvNQ2V6Og)

Takeaways

Network Effect Uber’s business model is a two-sided marketplace that is strengthened by a virtuous cycle, simple enough to be illustrated on a napkin by PayPal COO David Sacks:

By keeping this cycle in motion, Uber is able to continually push the boundaries of accessibility and affordability until demand is universal—the point at which “transportation [is] as reliable as running water, everywhere for everyone.”

Key Perfomance Indicators On October 29, 2014, the blog post “THREE SEPTEMBERS OF UBERX IN NEW YORK CITY” showed us how this network effect took shape in the real world. Over the course of 3 years, as the number of consumers and drivers on the platform increased, Uber provided more value to both riders and drivers.

The KPIs that drive value for riders are pick-up times and prices:

The KPI that drives value for drivers is wages / hour:

The remarkable thing about Uber’s network effect is that by improving utilization, it is able to improve wages to the driver while continuing to reduce average fare to the rider, a true win-win.

For the n+1 rider or driver, they simply cannot afford to choose any other platform but Uber. The rider will get the best price and the best service (as measured by pickup time) while the driver will get the most utilization and consequently the highest wage.

UberPOOL

UberPOOL continues down this path of lowering the price for the rider and importantly adds another dimension to utilization: seats occupied in a car in addition to minutes on trip. This theoretically allows a driver to make some multiple (≤2x) per every minute of on trip time. In addition, with its 2-minute pick up time limit, it actively tries to reduce the drivers pick up/wait time.

Lastly, UberPOOL also helps improve the weekly active rider to driver ratio, which as of the December 2013 internal deck was about 11-13 riders to every driver and 20-30 fares per driver per week. As seat utilization improves, each driver will be able to handle more fares, reducing the onboarding pressure on Uber, as incremental drivers will be difficult to find after a certain point.

Product Opportunity

As described above, UberPOOL is currently the product with the greatest potential impact on KPIs. However, its net impact is ultimately contingent on how many users regularly engage with it. While the lower fare is strong enough of an incentive for some riders, concerns including uncertainty of trip time, safety, and discomfort lead others to choose competing alternatives.

Addressing these concerns is an opportunity to build and solidify the user base for UberPOOL in its most formative period.

Brainstorm:

Top Ideas:

A) Uber Prime Description: a subscription based pricing model that gives riders a set amount or an unlimited amount of UberPOOL rides per month as determined by economics of the price per subscription and the cost of an a la carte UberPOOL ride.

Impact:

· Creates habitual dependence on Uber for the rider · Improves loyalty; reduces riders for competitors pushing them into a negative virtuous cycle · Helps the rider think about the cost of Uber vs. car ownership on a like for like basis · Reduces the mental tax of having to pay after every ride · Introduces more predictable demand. E.g. the rider that pays on a subscription basis will likely take the Uber to and from work everyday

from the same locations at a similar time. The more predictable demand can improve the match rates and seat utilization · Improves cash flow dynamics due to pre-pay characteristics of subscriptions, reduces the higher credit card processing fees on a la carte

fares due to their lower ticket price and creates a recurring revenue stream B) UberPOOLmeet

Description: An enhancement to UberPOOL, which allows the rider to select who they would be open to being matched with from a list of nearby poolers and their info (added trip time, ratings, and one-line bios). UberPOOL then matches riders who share a mutual interest in one another, and have the shortest combined trip time.

Impact:

· Eliminates the uncertainty around added trip time • Places control in rider’s hands, making them feel more responsible for and tolerant of added trip time and drop-off order · Relieves common concerns around safety and discomfort of riding with a stranger · Provides added value (making new connections) for UberPOOL, beyond low fares. Could be further targeted for dating or professional

networking. · Creates a deeper ridesharing community · Improves UberPOOL utilization by expanding demand; i.e. brings on riders who were unwilling because of safety and efficiency concerns

and riders who needed more value to justify the added time.

C) Uber Hubs

Description: a central location where riders can come for the fastest pick-up times and a special, even lower than normal UberPOOL price. Inspired by Lyft Hotspots, but with the added twist that the hotspot be a place of business. I imagined a partnership with Starbucks that would be very beneficial to all parties. A rider could request an UberPOOL, walk over to the Hub that happens to be a Starbucks and order a latte through the Uber app that will be quickly served in a separate window for Uber customers. Starbucks benefits from incremental foot traffic and sales, Uber benefits from increased UberPOOL adoption and the rider benefits from being able to grab a coffee while waiting for a ride. The driver could also benefit if some portion of the commission that Starbucks will give Uber for a coffee transaction is shared with the driver.

Impact:

· Enables higher match rates and lower idle times (currently a little more than 1/3rd of trip time) for the driver which should increase wages · Allows rider to feel more certain about the duration of the ride given higher likelihood of matching at the hub · Allows riders the increased convenience of commerce at place of pick-up · Allows drivers to increase their wages through the shared commissions from the partner business

Most Impactful Feature Today: UberPOOLmeet

• UberPOOLmeet is increasing the user base for UberPOOL by reducing the primary fears surrounding the product, while Uber Prime and Uber Hubs are adding value to existing UberPOOL users by enhancing functionality.

• Since UberPOOLmeet is altering the perception of UberPOOL, it is also more crucial during UberPOOL’s formative stages and therefore more impactful on Uber as a business today.

• UberPOOLmeet builds the foundation for a second virtuous cycle on. E.g. UberPOOLmeet could particularly indicate when a past match, or “friend”, is nearby during the rider’s next UberPOOL request. Since riders often travel from and to the same areas, they will frequently be matched with the same group of people. Over time, the development of these connections creates a virtuous cycle.

UberPOOLmeet :

Feature Components (Prioritized)

P0 (Feature cannot ship without): • User is able to view a list of UberPOOL riders in their area and select preferences for who they would like to meet, informed by details such as

added trip time, ratings, and brief bios • User’s info (which they fill out on their account settings page for a promotion of $10 credits) appears on each of those riders’ lists as well • If there is a mutual preference for one another, the user is matched with that rider; if there are multiple mutual preferences, the match with the

shortest combined trip time is chosen; If there are zero mutual preferences, or the user did not indicate any preferences, UberPOOL reverts to its normal functionality.

P1 (Important but feature can ship without): • Once the match has been made, the user can view an “info card” with the matched rider’s details and contact information (LinkedIn,

Facebook, Twitter) at the bottom of the app • After the ride is over, the user can view the matched rider’s info in their trip history

Success Criteria

• 50% of riders who see the promotion write their one-line bio • Of those who write their bio, 75% use the feature to rank one or more match preferences • 75% of the match attempts are successful (mutual and <15 min added trip time)

Future Iterations

The minimum viable product would only consist of the items described above, but if it tests well, we should consider the following enhancements:

• User is able to mark a rider that they do NOT want to be matched with again • User is able rate the rider they were matched with, just as they do with drivers • User is able to save a rider to a list of “Uber Friends”, and have a special indication when anyone on the list is nearby during subsequent

UberPOOL requests • User is able to include contacts from their phone to the “Uber Friends” list as well

Mockups of the feature flow are included in the following pages.