Analyzing business dynamics of Ride-hailing services : Uber

1. ANALYZING
BUSINESS DYNAMICS OF
RIDE-HAILING SERVICES
ECSTASIMULATION

3. • Representation of useful tools to drive decision-makers
in designing competitive strategies & measuring resulting
outcomes
• Useful measures of performance + Strategic levers
• Weak understanding of impact of current decisions on
future growth and the strategy to undertake to cope with
major changes
Conventional PM
(Performance Management)
• Understanding and leveraging the feedback interrelationships of
complex management systems
• Identifying and analyzing both determinants and implications of
a given business phenomenonSD(System Dynamics) Modelling
COMBINED
Capturing causal relationships underlying the functioning of a business system
Simulate performance behavior over time and contributes in evaluating the trade-offs between
short and long term outcomes related to the adoption of a given strategy
1
2

4.  System dynamics (SD) + Performance management (PM)
 Adds dynamic and non-linear perspective in designing and implementing
PM system in complex and unpredictable contexts
 Strategic resources → Value drivers → End-results
 Overcomes limitations set by conventional PM approach, which is based
on linear, static and bounded point of view

5.  Strategic resources
• Uber’s perceived price per ride (compared to competitors’)
• Uber’s perceived pickup time (compared to competitors)
• Uber’s perceived service value
 Drivers
• Relative price
• Relative pickup time
 End results
• Change in perceived service value (positive or negative)
• Change in Uber’s customers (positive or negative)
Feedback
DPM Model #1

6. DPM Model #1

7.  Strategic resources
• Uber’s commission percentage
• Uber’s bonus spending
• Bonus spending: financial resources provided to attract drivers, including
car loans & performance-based bonuses
 Drivers
• Relative commission (between Uber and competitors)
 End results
• Number of drivers leaving Uber
• Number of drivers tempted to leave Uber (positive or negative)
DPM Model #2

8. DPM Model #2

9.  Potential customers in NYC become interested in using ride-hailing service
when they hear about the service from current users
 Potential customers become Ride-hailing Customers (Uber, Gett, Lyft, etc.)
 Some of the customers may use the service more often than the others in
the population (have different effect on new potential users)
 Some stop to use the service (the “Regretters”)
 Customer’s Perception of Service Value determines number of Uber’s
customers (dependent on relative price & pickup time)

12.  Likewise, assumes word of mouth effect
 Potential Drivers that eventually will become Uber Drivers in NYC
 Uber Drivers attract more Potential Drivers to become a driver-partnership
 a reinforcing loop  total ridership [figure 5]
 [figure 2] : [figures 6] ; Customer’s Perception of Service Value determines
number of Uber’s customers (dependent on relative price & pickup time)
 flip side model
 Potential drivers in NYC / Awaiting Drivers / Active Uber Drivers in NYC
 To capture the change(due to surge/plunge pricing policy in effect) in
service capacity(the number of Uber drivers) of Uber platform

13.  Describes the dynamics of service requests by Uber customers
 Price Threshold ratio
= (Effective Price per Ride) / (Uber’s Normal Price per Ride)
 “Effect of Price Threshold ratio on closing app”
 “Matched Rides” & “Rides Arriving”
 “Effective Pickup Time”[Uber’s performance indicator]
 Customer’s Perception of “Pickup Time”
 the Service Value  Service Demand Form Uber
 n(“Matched Rides” delivered by Uber’s drivers) = income for each drivers
and gross revenues for Uber

16. The structure where customers perception of service price
become updated [figure 10]
• Another performance driver Relative Price(Service Charge loop)
Perceived Price
• Uber’s Fare Price = Base Fare + (Cost per minute * time in ride)
+ (Cost per mile * ride distance) + Booking Free
Perceived Pickup Time
• Customer hailing a ride ~ Driver arriving
Perceived Service Value
• Factors: Service Price per Rice and Pickup Time
• Service Price : Waiting Time (weights) = 75%:25%
Commission Percentage: 20% to 25%

18. Service Demand for Uber =
Reported number of customer X Standard Rides Need per customer

19. Price Threshold Ratio = Effective Price per Ride / Uber’s Normal Price per Ride
Price Threshold Ratio on closing app :
Those who opened the app but closed without requesting

20. Normal Price per Ride
=Base Fare + Booking Fee
(+cost per minute) (+cost per mile)
25%
Perceived Pickup Time
= time between a moment a
customer hails a ride and a moment
the drive arrives
75%

21. Uber’s Price
Competitor’s Price

22. Q&A