Investigative Study: Bike Subscription Modelling Feasibility for Blue Bikes (Boston) & Divvy Bikes (Chicago). Completed as part of General Assembly Data Analytics Immersive Program, January 2021. For the full voiceover narration presentation, view at: https://youtu.be/bAAJn-dnN2o

1. INVESTIGATIVE STUDY:
BIKE SUBSCRIPTION
MODELLING
FEASABILITY FOR BLUE
BIKES & DIVVY BIKES
Analysis by Mitch Leung

2. EXECUTIVE SUMMARY
• This investigative study explores the bike sharing companies of Blue Bikes in Boston and Divvy Bikes in
Chicago which aims to uncover insights specifically around the subscription modelling supported by data over
a three-year period between 2017-2019.
• Through the exploration of insights and trends for both Blue Bikes and Divvy Bikes, this paper was designed to
assist in future business and commercial decisions which may be related to customer acquisition initiatives,
subscription programs and campaign optimisation efforts.
• From my analysis, there were some key takeaways from the 6 distinct questions the businesses wanted
addressed, as well as a data-driven recommendations on the feasibility of the subscription model, and if
weather influences bike hiring activity.

3. TRENDS & DESCRIPTIVE ANALYTICS – TOTAL NUMBER OF TRIPS
0
100000
200000
300000
400000
500000
600000
700000
Number
of
trips
Total Number of Trips
trips_bluebike trips_divvy
A business focus upon ramping up brand initiatives & personalised campaigns during the lead up to the busy
summer peak period geared towards subscribers and customers will maximise trip activity. Marketing
programs/budgets can also be adjusted and optimised to reflect the peaks and troughs of seasonality.

4. TRENDS & DESCRIPTIVE ANALYTICS – SUBSCRIPTION GROWTH
Bluebikes experienced a 80% growth when benchmarking subscription growth in 2019. In looking ahead for 2020,
explorations into new campaigns driving brand awareness with a subscription call-to-action messaging to reach
consumer top of mind can be explored to yield an optimal subscribership growth with a combination of both organic and
paid advertising efforts for the bike subscription model for Boston.
1104738
1436677
1988467
2992135 2925926 2937367
0
500000
1000000
1500000
2000000
2500000
3000000
3500000
2017 2018 2019
Total
count
of
subscribers
Total Subscribers (Absolute Terms)
bb_total_subscribers divvy_total_subscribers

5. TRENDS & DESCRIPTIVE ANALYTICS – SUBSCRIPTION GROWTH
Bluebike should look to centralise focus on membership programs which speak and target residents of
the Boston borough and neighbouring boroughs to focus on corporate programs, incentivised income-
eligibility programs and tertiary students studying in the Boston borough and wider neighbouring
boroughs of Massachusetts.
84%
16%
Bluebike 2017 (Proportional of
sharing)
subscribers customers
81%
19%
Bluebike 2018 (Proportional of
sharing)
subscribers customers
79%
21%
Bluebike 2019 (Proportional of
sharing)
subscribers customers

6. TRENDS & DESCRIPTIVE ANALYTICS – SUBSCRIPTION GROWTH
Similar to Divvy Bikes’ absolute terms over 2017-2019, the data presents no strong
indication that they are experiencing subscriber growth in the subscription side of the
business.
78%
22%
Divvy Bikes 2017 (Proportional of sharing)
subscribers customers
81%
19%
Divvy 2018 (Proportional of sharing)
subscribers customers
77%
23%
Divvy 2019 (Proportional of sharing)
subscribers customers

7. TRENDS & DESCRIPTIVE ANALYTICS – DIFFERENCE IN GROWTH
BETWEEN HOLIDAY ACTIVITY & COMMUTING ACTIVITY
There is an evident difference between holiday activity and commuting activity outside of the
holiday activity times.
0
200000
400000
600000
800000
1000000
1200000
Winter
Winter
Spring
Spring
Spring
Summer
Summer
Autumn
Autumn
Autumn
Winter
Winter
Winter
Spring
Spring
Spring
Summer
Summer
Autumn
Autumn
Autumn
Winter
Winter
Winter
Spring
Spring
Spring
Summer
Summer
Autumn
Autumn
Autumn
Winter
Jan Feb Mar Apr May Jun Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Aug Sep Oct Nov Dec
2017 2018 2019
TOTAL
SUBSCRIBERS
Total Subscribers vs Time
Sum of divvy_total_users Sum of bluebikes_total_users

8. GEOSPATIAL – LONGEST JOURNEY (BLUE BIKES)
The longest trip was 16.1km journey for Bluebikes which occurred in
2019. It was a trip from Glendale Square (Ferry St at Broadway),
Everett to Belgrade Ave at Walworth St, Boston. The starting location
had 15 docks, and ending location had 15 docks.

9. GEOSPATIAL – LONGEST JOURNEY (DIVVY)
The longest trip was a
36.81km journey that occurred
in 2018. It was a trip from
Wabash Ave & 87th St to
Central St & Girard Ave. The
starting location had 11 docks,
the ending location had 15
docks.

10. GEOSPATIAL – LONGEST JOURNEY (DIVVY VS BLUEBIKES)
Divvy has recorded a consistently longer journey when
compared to Bluebikes over 2017-2019 period.
5
10
15
20
25
30
35
40
2017 2018 2019
Kilometres
Longest Journey by Year
Bluebikes Divvy

11. GEOSPATIAL – FREQUENCY OF BIKE RELOCATION
Divvy Bikes are trending
downwards in terms of
requiring bike relocations,
with a drop of 8.23% to
7.13%. Conversely,
Bluebikes’ relocations have
steadily increased from
6.84% to 7.08% over
2017-2019 which highlights
organisational inefficiency.
Strategies should be explored
how to better manage this
with ops/logistics to increase
capacities at docking stations.
0.06
0.065
0.07
0.075
0.08
0.085
2017 2018 2019
%
of
bike
moves
Bike Relocations expressed as % of Total Trips
Divvy % of moves Bluebike % of moves

12. GEOSPATIAL – HOW FAR IS A TYPICAL JOURNEY?
A typical journey for
Divvy Bikes is
2.04km.
A typical journey for
Bluebikes is 1.93km.
1
1.2
1.4
1.6
1.8
2
2.2
2.4
2.6
Autumn
2017
Spring
2017
Summer
2017
Winter
2017
Autumn
2018
Spring
2018
Summer
2018
Winter
2018
Autumn
2019
Spring
2019
Summer
2019
Winter
2019
Kilometres
A typical journey by season
Bluebike Divvy

13. BUSINESS & COMMERCIAL – HOW DOES WEATHER IMPACT BIKE
HIRING
• Warmer months are correlated with longer average trips.
• Colder months are associated with short average trips.
• Weather data from Wunderground on recording historic
weather activity in Chicago Winter, 2017 where average trip
recorded 1.71km in December.
There is an evident
correlation on bike
hiring based on
weather. Warmer
months are
correlated with
longer average trips.
This can assist with
planning and
coordinating best
times to take bikes in
for maintenance,
retiring bikes,
upgrade docks etc.
Source: Wunderground weather – Chicago, Winter 2017

14. BUSINESS & COMMERCIAL – DO SUBSCRIPTION SYSTEMS
WORK?
• The answer is both Yes and No!
• Community attitudes, cultural and shifting norms and values
towards the value of membership towards such a service
coupled with pricing and product/service offering.
• Santander Cycles offers a low barrier to entry with low
annual membership at 90 pounds
• Normalised the practice of cycling in city life
• Encourages women to participate
• Scheme is subsidized by Transport for London aligned
with Mayor’s policy to drive transport system
transformation.
Depending on
subscription service
rollout city location,
attitudes and values
of customers will vary
and ultimately impact
subscription program
success.
Source: How to Save Bike-Sharing: An Evidence-Based Survival
Toolkit for Policy-Makers and Mobility Providers, Alexandros
Nikitas

15. APPENDIX

16. GEOSPATIAL – FREQUENCY OF BIKE RELOCATIONS – MONTH
BREAKDOWN TABLE
Figure 1. Divvy Bike Relocations Figure 2. Bluebike Relocations

17. BUSINESS & COMMERCIAL – HOW DOES WEATHER IMPACT BIKE
HIRING
Figure 3. Divvy
Typical Journey
Length by season
Figure 4. Bluebike
Typical Journey
Length
By season