UBER HOMEWORK ASSIGNMENT
Uber Driver Preferences Feature
Proposal
Prepared for: Uber Recruiting Team
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EXECUTIVE SUMMARY
Objective
To bring an Uber feature to market that has a lot of potential, but has not been implemented yet.
Overview
The largest problems that Uber faces today revolve around lawsuits and trust + safety issues. This
proposal will recommend a new safety feature that will help improve rider safety and also provide a
financial benefit for drivers and Uber.
Project Outline
This proposal will be structured as follows:
• Market, Customer, and Competitor Research
• Opportunity + Proposed Solution (description + mockups)
• Value Propositions
• Other Considerations (potentials downsides)
• Launch Strategy (marketing + metrics)
• Other Features (other features that I thought)
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PROBLEM
Market Overview
Over the last 11 years, the ride-sharing industry has grown significantly. As a pioneer in this space,
Uber has helped make cities more accessible by matching riders and drivers. However, this growth has
not come without problems. The largest problems that Uber faces today revolve around lawsuits and
trust + safety issues. There have been many cases where Uber drivers have acted rude to passengers,
and some more severe cases where they have committed crimes. This is a large problem in the ridesharing industry, with 91 alleged assaults by drivers, 350 alleged sexual assault & harassment cases, 16
alleged kidnappings, and 47 deaths to date1.
Because getting passengers from point A to B safely and reliably is the single most important thing Uber
does, the Trust and Safety team has invested heavily in features to improve safety for passengers and
riders before (background checks), during (GPS ride tracking & sharing with features like RideCheck),
and after (rate drivers and passengers) each ride. This team exists to reduce safety incidents and is
judged by this metric.
Customer Research
To analyze how safe riders feel when using Uber, I made a survey using Google Forms and ran the
survey around the Cornell Campus + my Facebook groups. I was able to collect 122 responses in the
span of 2 days. Note that the survey that I conducted has too small of a sample size to be statistically
significant. For future work on this proposal, a survey of a larger randomized sample should be
conducted.
In the survey, I first gathered background information and presented questions based on whether the
participant had used a ride-sharing service in the last year. I then asked participants why they chose to
use or not use a ride-sharing service in the past year, and presented them with a series of safety
questions (rate popular transportation services in terms of safety, factors that would make them feel
safer, and any safety concerns they may have with ride-sharing services).
1
http://www.whosdrivingyou.org/
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Based on the survey data, I was able to find the following interesting results:
1. 78% of women ranked Lyft (average score: 4.3) has being more safer than Uber (average score:
2.8)
2. 26% of all participants preferred Lyft to Uber and ranked Lyft as being more safer than Uber.
3. The main reasons why women feel that Lyft is more safe than Uber are:
a. There are more women drivers (30% of Lyft drivers are female, compared with 27% of Uber
drivers2)
b. Uber is branded to be professional and reliable (reflected in the slick, black design), while Lyft
is branded to be happy, safe, and fun (reflected in the pink, bright design).
4. Men view Lyft (average safe score: 4.2) and Uber (average safe score: 3.9) as being equally safe
Below you will find the screenshots of the survey that I conducted:
2
https://www.bbc.com/news/technology-46990533
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Customer Personas
After analyzing the customer research, I categorized the survey participants into 3 personas that many
of them fell into:
1. Alana is a 25 year old single woman who works as a marketing manager at a high-tech
company. Every weekend, Alana and her friends go out to eat and then go clubbing in the city
where she and her friends use either a ride-sharing service or a taxi-service to go home. Alana
doesn’t mind paying a little extra for the taxi, because she feels safer when riding it, but if she felt
safe riding an Uber, she would definitely switch over.
2. Mark is a 30 year old male who is in a relationship with Brittany, a 31 year old female. Mark and
Brittany are both working professionals - Mark works as an account manager at a large CPG
company and Brittany works as a software engineer at a finance firm. Every month, Brittany and
her girlfriends have a girl’s night out. Mark worries about Brittany’s ride back - in the past Brittany
has had rude Uber drivers when she rides alone. Mark and Brittany are both looking for a feature
to allow them to filter their potential Uber drivers, so that when Brittany has to ride an Uber
alone, she can still be safe, a feature they would not need when riding together.
3. Eric is a 45 year old male who is married to Alisa, a 44 year old female. Eric and Alisa are both
working professionals - Eric works as a neurosurgeon at the local hospital and Alisa works as a
CEO of her own consulting firm. Eric and Alisa are busy professionals, and cannot pick up their
son Evan (15) from school every day, but Evan also cannot take the bus because he is involved
in extracurricular activities and sports. Eric and Alisa currently have Evan call a cab to pick him
up from school, but this is not reliable nor safe. They recently found Uber’s filtering feature and
have asked Evan to use it because they like how they can personalize the driver preferences.
Competitors
There have been a lot of apps that have emerged to specifically target women users to use their safe
and reliable ride-sharing service. The company websites listed below all are ride-sharing services for
women riders that only allow women-drivers:
1. SheTaxis (http://shetaxis.com/)
2. SafeHer (http://www.safeher.com/)
3. SheRides (http://sheridesnyc.com/home.html)
4. See JaneGo (http://seejanego.co/)
5. Mum’s Taxi (https://www.gofundme.com/mumstaxi)
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PROPOSED SOLUTION
Opportunity
Based on the customer, market, and competitor research, it can be seen that there is an opportunity for
Uber to target users who are sensitive to perceived ride safety, who may currently be using competitor’s
products. By creating a driver safety profile for all Uber drivers, we can provide a filtering feature inside
the app that will allow users to specify constraints for the drivers that they can be matched with. Users
will feel safer using Uber since they will only be matched with a driver meeting their preferences.
Feature Overview
The driver profiles will be updated with a safety score based on their rating and their number of
completed rides. After these scores have been created, riders will be able to choose to only be paired
with a driver that:
1. Has a high rating (above 4 stars)
2. Is a specific sex (M/F)
3. Has completed 100+ rides
4. Has driven your friends or friends of friends in the past
This filtering process may take more time, since the user is limiting the number of potential drivers that
he/she can be matched with. So, if the user is willing to make this tradeoff, then Uber can provide a
more personalized match. The filtering process can be below in the mockups section below
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Mockups
Below are a few visual sketch ups of the safety filter feature:
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VALUE PROPOSITIONS
Financial
Since the user will be given the option to choose preferences for their Uber driver, this tailored
experience will cost the user an extra fee. This fee can be either a flat fee (i.e. $2.00 for using the
preferences filter) or 1.2x - 1.5x of the ride fee based on how long the ETA of the ride is. For the
financial projections, I assumed that the fee will be 1.2x the ride fee. I showed the calculations for other
cost structures for comparison.
With the additional revenue that this fee will generate, there should be a spilt of the revenue between
Uber and the drivers. This way, drivers will have an additional incentive to hit the performance metrics in
the safety preferences filters (100+ riders, 4+ rating, etc).
Assumptions:
1. 5% of all Uber rides would use this preferences filter feature (assuming 30% of all Uber rides are
at night-time and ~17% of those
riders prefer a safety feature -> so, 30% * 17% = 5% of all Uber
rides
a. Assuming Uber spends $149 million/year34 on legal fees (lawsuits, settlements, lobbying),
and we are able to reduce 5% of these legal fees based on the safety preference feature,
that yields a savings of $7.45 million/year.
2.
Uber’s revenue in 2019 from Rides was $10.75 billion5.
Revenue generated from existing users:
Assuming Uber retains a revenue from Rides of 10.75 billion, and the company charges 1.2x the rider
fee for 5% of rides, the revenue should increase by $107.5 million/year. If gross profit is 30% and
additional profit for drivers is 70% , the addition of this filtration feature should yield a gross profit of
approximately $32.25 million/year for Uber and approximately $75.25 million/year in additional income
for drivers.
3https://theoutline.com/post/7418/uber-has-132-million-dollars-earmarked-for-settling-lawsuits-from-its-own-drivers?zd=1&zi=fim4j4oe
4
https://s23.q4cdn.com/407969754/files/doc_financials/2019/ar/Uber-Technologies-Inc-2019-Annual-Report.pdf
5
https://s23.q4cdn.com/407969754/files/doc_financials/2019/ar/Uber-Technologies-Inc-2019-Annual-Report.pdf
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In addition to the the revenue generated by the fee, Uber will be able to convert users from competitors.
Assumptions:
1. The number of Lyft users is 21.2 million6 and the average user takes 1 ride every 2 weeks.
2. Based on the customer survey data, we know 26% of all participants preferred Lyft to Uber and
ranked Lyft as being more safer than Uber. Assume that 10% of these users will switch over to
Uber because it now has the added safety preferences feature.
3. The average Uber ride costs $16.16.
Revenue generated from converting competitor’s users:
Cost
Number
Percentage Averag
Structu of Weekly of Users
e Ride
re
Rides
Switching
Cost
to Uber
Preferen
ces
Filter
Fee
Uber
Weekly
Revenu
e
Uber Weekly
Gross Profit
(30%)
Driver
Additional
Weekly
Income
(70%)
Flat Fee 10,600,000
of $2.00
2.6% US$16.16 US$2.00 US$21,200,000
US$6,360,000 US$14,840,000
1.2x of
Ride
Fee
10,600,000
US$10,277,760US$23,981,440
2.6% US$16.16 US$3.23 US$34,259,200
1.5x of
Ride
Fee
10,600,000
2.6% US$16.16 US$8.08 US$85,648,000
US$25,694,400 US$59,953,600
In the table above we can see that if Uber charges 1.2x of the ride fee, that Uber’s revenue from
customer conversion is $34.3 million/week = $147.5 million/month = $1.77 billion/year. Uber’s
gross profit (30%) is $10.3 million/week = $44.3 million/month = $531.5 million/year. The additionally
income created for drivers (70%) is $24 million/week = $103.1 million/month = $1.24 billion/year.
6
https://www.sfchronicle.com/business/article/Lyft-grows-rider-base-revenue-despite-coronavirus-15251664.php
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Combining these sources of revenue, we get the following numbers:
1. Uber’s total revenue earned from this feature would be approximately $1.88 billion/
year.
2. Uber’s total gross profit from Rides would be $563.75 million/year.
3. The total additionally income created for drivers (70%) would be approximately $1.99 billion/year.
4. A savings of $7.45 million/year in legal fees (lawsuits, settlements, lobbying).
Rider Experience
The target audiences, which were defined in the customer personas section (women requesting an Uber
at night, significant other’s of those request an Uber at night, and parents requesting an Uber for their
children), will now feel safer when they or their family/friends use the Uber app since there is an added
preferences filter.
Driver Experience
Drivers will benefit from the preferences filters because they have the opportunity to earn an extra $2.24
for every filtered ride. If a driver is able to make 20 filtered rides every week, that amounts to $44.72/
week = $193.77/month = $2,325.23/year.
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OTHER CONSIDERATIONS
Uber’s Matching Algorithm
Since the added preferences filter will affect the pool for Uber drivers that the rider can be matched with,
Uber’s matching algorithm will need to be modified such that:
1. The riders using the preferences filter will be matched only with a pool of Uber drivers that meet
the criteria (same matching rules can be applied to this subset of Uber drivers)
2. The riders not using the preferences filter will be matched normally using Uber’s current
matching algorithm
Legal Issues
There are legal issues to consider since filtering by demographic information (sex), may be deemed as
sexist. Many of the competitors in the women only ride-sharing industry (only women drivers and
women passengers), are facing lawsuits since they don’t hire male drivers or allow males to ride their
service solely because of their sex7. By including multiple performance metrics (number of rides, rating,
etc), this feature will not exist to primarily to request a female driver, but rather an overall well-performing,
safe Uber driver, avoiding this legal risk.
Brand Dilution
Since this preferences feature will be branded as a safety feature, we have to make sure that the Uber
brand does not get diluted. Specifically, we need to make sure that passengers do not think that the
current Uber platform is unsafe, and the only way to get a safe ride is through the preferences filter. This
distinction will need to be made when marketing the app. The preferences filter feature should only be
marketed to users who are sensitive to perceived ride safety and may currently be using competitor’s
products. These users should be targeted through multimedia marketing campaign and through special
promotional events (explained in marketing strategy below).
7
https://www.fastcompany.com/3036570/strong-female-lead/can-this-women-only-taxi-service-overcome-legal-hurdles
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LAUNCH STRATEGY
Marketing Plan
The preferences filter feature should be A/B tested with a small sample of users that is statistically
significant and representative of the overall Uber population. The goal during A/B testing it to get 5% of
the sample to use the preferences filter, continuously monitoring the demographic of users who are
engaging with the feature.
If A/B is successful then a multimedia marketing campaign should be conducted to target users who
are sensitive to perceived ride safety, who may currently be using competitor’s products. Research on
this target audience should be conducted so that high profile bloggers that are popular with this group
can be contacted to write an article about Uber’s new safety feature. Youtube advertisements should
be placed for videos that this target audience is likely to watch. The feature should be marketed to
users of the app through special promotional events (this is explained below).
First, the app should target users who have the Uber app downloaded, but have not used the app to
make a ride in the last 3 months or longer. This demographic will have used the app once and have
moved on to a competitor product (another ride-sharing service, taxi, public transit, or personal vehicle).
By sending push notification or email notifications to this demographic, we will attempt to bring them
back to Uber and make more frequent rides with us.
We should target this demographic with targeted notifications. For users who are between 21 and 40
years of age, we should show them special promotional events that are going on in their area. These
promotional events will be advertisements with local venues - say of instance, a nightclub that is hosting
a Friday night event. The notification that will be sent will say “Planning a Night Out? Come to Club 21
this Friday courtesy of Uber”. These free or discounted Uber rides, will be a way to get this
demographic to open the app again. Once the app is open, we can show them a pop-up banner that
says “Introducing Uber’s new safety preferences filter feature” and walk them through how to use it.
This will cover the target user education of the feature. After this point we will need to monitor these
users to see if they use the app more frequently.
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Key Performance Metrics
During A/B testing we will monitor:
1. How many users are engaging with the safety preferences filter
2. What is the demographic information about the users are engaging with the preferences filter
3. What filters are being used the most often?
4. What time of day/week is the feature being used?
In order to measure the success of our launch strategy, we will need to track:
1. How many rides are made using the safety preferences filter (target is 350,000 rides/week)
2. The current non-active users who start to make Uber rides using the preferences filter (weekly,
monthly, yearly)
3. The new users that join the app and start making Uber rides using the preferences filter (weekly,
monthly, yearly)
a. See what the demographics of these new users are and compare to our target audience
personas
We will monitor these metrics to make sure that we are engaging existing non-active users to take more
Uber rides (who previously felt unsafe using the app). At the same time, we will try to convert users from
other platforms (Lyft, other ride-sharing services, taxi’s, etc.) to join Uber and take rides using the
preferences filter.
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OTHER FEATURES
While doing this assignment, I brainstormed other problems/opportunities that I noticed with the Uber
app. I wanted to include them below with some potential solutions:
1. Opportunity: Partner with corporate trip booking agencies (i.e. Cartus) to automatically schedule
an Uber as your method of transportation after your flight lands.
2. Problem: Dropping a pin outside of the surge zone. Since surge zones are very localized, one
street could be 2x and another could be regular pricing. Some riders could move the pin around
until they get out of a surge zone and then make their request. They can then call the driver and
let them know their real address or wait for the driver to pick them up and tell them “their phone
GPS isn’t accurate”.
a. Potential Solution: Require riders to confirm their address when their dropped pin doesn’t
match their GPS. This confirmation could be taking a picture of a street sign/landmark to
confirm their location.
3. Problem: Dropping a pin outside of the airport pickup zone to avoid waiting and fees. Same as
above - riders can drop a pin outside of the airport geo-fence to not wait in line and avoid fees.
a. Potential Solution: Require riders to confirm their address when their dropped pin doesn’t
match their GPS. This confirmation could be taking a picture of a street sign/landmark to
confirm their location.
4. Problem: Unlimited Referrals. A rider can keep creating new accounts to get a promo balance
every time. The rider can use the code from previous accounts to get the promo balance in both
the new and old accounts (the rider will need a lot of email addresses and phone numbers - can
use apps like Burner).
a. Potential Solution: Track the number of accounts that are used per device (phone, computer,
etc). That way if multiple accounts are used on the same device repeatedly, Uber can block
that device.
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