Session – 6 : Case of Sharing
Economy & Business Model
Canvas
Case of Uber and Big
Data
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Motivations for Sharing
• Economic
• Social
• Environmental
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Case of Uber and Big Data
Uber is part of a larger digitally enabled gig economy, where work is assigned
and performed through on-demand labor platforms (De Stefano, 2016). By 2018,
there were three million active Uber drivers globally (Rosenblat, 2018).
Big data on supply/demand patterns helps to fine tune the sharing and improve
the value creation.
uberPool is a service that is based on predicting demand. It pools several
passengers into one ride further improving asset and resource utilisation and
reducing costs for the passengers.
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Q3’20 has heralded better news for Uber, compared to the quarter prior, when it saw gross
bookings and adjusted net revenue plunge 35% and 33% YoY (year on year), respectively.
Source: Uber
Today, it operates in 68 countries and 10,000+ cities, and non-US markets
account for 80% of all trips (including rides and meal deliveries) as of Q3’20.
Source: Uber
To measure if the app is reliable at all
times, Uber measures three KPIs:
Availability – were riders able to
hail a ride and complete a trip.
Latency – the time needed to go
through the entire process of
calling a ride, the system flow, the
screen transition, the tabs
functionality.
Accuracy – of the information, the
map, price, discount.
Uber maps every city into granular hyperlocal
zones which are basically small hexagonal blocks.
When demand in an area increases the block will
start changing the color.
The colored areas of the map range from light
orange to dark red. Light orange denotes a low
surge while dark red suggests an area of a high
surge.
Quantitatively, the surge is denoted by multipliers
of X.X.
A rider in a surging area may accept a surged price
for a ride if he/she wants a cab immediately.
uberPool is a service
that is based on
predicting demand.
Use of Big data and
analytics to get useful
insights.
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Uber and Nosko have pulled data on driver supply and ride
requests from two specific events.
First, a roughly five-hour window around the end of an Ariana
Grande show at Madison Square Garden, New York City in late
March (2015).
Second, this past New Year’s Eve in New York City.
Use of Big data
Conclusion is that when the Ariana Grade concert ended, surge
pricing did what it was supposed to—
increased the supply of drivers filling requests in the area,
while keeping the amount of people actually requesting rides far
below the much more substantial number who had opened the
app.
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Uber says this is good for
customers because it
“allocate[s] rides to those
that value them most.”
It’s good for Uber
Incentive for Drivers during
peak time
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Finding by researcher, 2016 :
(Peter Cohen, Robert Hahn, Jonathan Hall, Steven Levitt, and Robert Metcalfe1 )
Using almost 50 million individual-level observations and a regression
discontinuity design, they estimated that in 2015 the UberX service
generated about $2.9 billion in consumer surplus in the four U.S. cities.
For each dollar spent by consumers, about $1.60 of consumer surplus is
generated.
The overall consumer surplus generated by the UberX service in the United
States in 2015 was $6.8 billion.
In an interview for Hyperight, Ritesh Agrawal, Tech Lead Manager
at Uber, highlighted Uber’s ML-based features that help to
enhance riders’ experience:
Personalised destination suggestions based on ride history and frequently
travelled destinations.
One-Click chat – a smart reply system which allows riders and drivers to
communicate easily with in-app messaging. The system uses machine
learning and NLP to anticipate responses to frequent riders’ questions.
Drivers can reply with just one click of a button.
Bridging the supply-demand gap – Uber’s system predicts time periods and
area are going to have increased demand and alerts drivers accordingly.
Meeting the demand in pick hours helps Uber keep customers happy and
increase its customer retention rate.
Who must lead it?
• CEO, CDO or CIO? – Purpose driven
• Governance policies and structure
• Communicating the objectives & goals
• Right to give input + right to make decisions
What is the Business Model Canvas ?
Business Model Canvas:
Value Proposition - Using big data has led to many innovative products and services
Example: Location based services using GPS enabled devices.
Customer Segments - More tailor-made services or goods provide value to different customer
segments.
Channels - When using big data the relationships that companies build with their customers often
rely on self-service interfaces
Example: maps being used to locate businesses of specific types.
Key Resources - Resources for creating value propositions could be tangibles
Example, scalable infrastructures and distributed platforms
Key Partners
Cost Structure –Includes all spending. In the context of big data the variable costs for
infrastructure are particularly important to consider.
Big data can affect one or a few building blocks or all of them.
Willingness to
participate in a
sharing
community
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Who are the actors of the sharing economy?
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