This is a more built out version of a 15min breakdown I did with my prep partner Denise (who in turn answered the Improve Twitter question).
Uber is ride-sharing application connecting riders interested in short-notice transportation and crowd-sourced drivers interested in monetizing their time and fixed vehicle assets. To improve the Uber service, I’d like to focus on retention of riders as more frequent riders are more likely to refer friends and drivers will drive for the platform that offers the highest chances of rides and by association the larger user base.
Identifying Uber segments:
Commuters Commuters are people with a daily routine who are interested in getting to and from destinations in a predictable, regular pattern. They are interested in reliability and predictability in pickup to hit a flow-state for getting the day or evening started or wind-down from work. Commuters as it relates to predictability, may be more inclined to cut time short, as there is less natural uncertainty to budget and running late 3min, could cost them an hour in commute.
Recreational Users – Short Trip Rec users have less predictability in their ride planning and may be early or late. They may be at work and need to get to a dentist appointment within a close proximity. Recreational users will be more sensitive to the experience of the ride as it is less routine. Ease-of-use will be important in terms of setting up the logistics.
Recreational Users – Long Trip Users without ready-access to vehicles or transportation options for rides of a significant duration >30min. This could include airport rides or weekend getaways. Time is budgeted for the trip, but the idea of ride-sharing for a long duration is uncomfortable and awkward.
Reflecting on our goal of improving engagement through retention, commuters will be the most active Uber user and by association the most likely to refer and invest in the process. A seamless end-to-end ride offers substantial value to these customers and will keep them invested over exploring alterantives. Solutions to achieve this goal:
Automated Pickups and Route Planning Allow commuters who will run on a fixed schedule to set a recurring pick-up (+-5min) and preferred route. The feature would be paid-for premium feature feature a driver outside waiting. Uber can use this data to inform drivers where the demand for the following day is likely to be to allow the market to adjust to fill gaps instead of the current word of mouth, secret strategies each seem to have. The preferred route, could be compared with traffic conditions on a day-to-day to alert the user early to delays and optimize route. Integration to alexa and google home would support. Drivers would be encouraged to not deviate from app established route and would be compensated for the time spent waiting as a percentage of the premium feature addition. The waiting driver would act as a buffer against being late and offer momentum to the user who walks outside vs open app, assess driver proximity, etc.
Driver Ratings & Passenger Preferences Offer better matching of drivers with riders to ensure more pleasant rides. Most people would prefer to wait another 1-3minutes to ensure their driver is compatible and people love sharing bad uber stories, which is negative word of mouth on the company. The current model of passive ratings could be a more active experience to score drivers on features of cleanliness, small, talkativity, thoughtfulness, speed, forethought, etc. These metrics could be compared with rider profile preferences. Riders would be offer an immediate ride or an optimized ride.
Playlist pairing Allow the driver to indicate if they’re willing to accept music streaming of riders at a preset volume to allow commuters to ease into their ride and mental flow-state. The phones would connect through the application and buffer songs ahead to ensure dropoffs don’t interrupt.
Automated pickups with route planning will boost retention by minimizing unexpected events along the journey, which could lead to churn, especially if other commuting alternatives are available. Scheduling is already a feature, as well as optimization. The route planning feature similar to Google Maps pick and drop would need to built out, as well as the driver demand gap forecasting, and premium subscription purchase UI. The predicates for use route planning would lead to expedited development – 2-4 weeks for beta testing, whereas the demand tool, which I suspect exists internally, would have to have a UI built out, which could phase in based on initial success after 2-3 months of dev time and testing. The luxury experience of having a driver waiting for you is a feature reinforced as a luxury and premium in our culture so monetizing this feature is likely if priced in a way where drivers can make more, riders won’t cancel on account of price, and the platform can increase revenues a fraction and benefit from scale.
Driver rating and passenger preferences offers a way of avoiding what can be a horrible experience and putting the power of choice in the users hands, which comes with responsibility for outcome – If I had just waited I could have avoided that. This would increase the seamlessness of the commuters ride and could trickle down to other recreational categories in a scaled rollout as multiple uncomfortable rides will result in churn to another platform and possibly leave platforms all together. Bundling this feature on top of the premium pickup as an initial platform makes a lot of sense given the additional wait can be budgeted for in the advance notice.
Playlist pairing is tricky feature because of the interplay between rider and driver and setting expectations for the ride with variety of taste and preferences. If rolled out in a way where the user is alerted todays ride offers a pairing feature that would be a small win to start the day. Similar to two, I think this makes a lot of sense to rollout in a premium feature as part of a premium subscription for commuters where the driver can monetize more of the service. Control of the environment is one of the few variables that the driver has control of to make their experience on the platform pseudo-enjoyable and this option being forced, could result in driver churn.
Summary In summary, improving Uber through feature development for retaining of commuters is the most likely to grow the platform and introduce new customers through referral. Introducing a premium option for commuters to have a waiting car, preferred routes, predictive traffic indication along route for the rider, for a small increase will let commuters into their routine easier, benefit drivers through demand localization and monetization. Additional features including premium driver matching and playlist syncing would help the commuter enjoy a flow-state and differentiate the experience from other platforms. With rider volumes comes more drivers and positive reinforcement loop.