Analysis on the Marketing Strategy to convert Casual Users of Cyclistic bik-share into Annual Members based on the latest 12 month data on ride usage

1. Case Study:
How Does a
Bike-Share
Navigate
Speedy
Success?

2. Background of the
Case Study
• Cyclistics Bike-share has a fleet of 5,824 bikes
over 982 stations in Chicago
• The company has a flexible pricing plan that
appeals to all types of consumers from those
who wants to subscribe annually to those
who just wants to use it occasionally
• Considering that annual memberships are
more profitable for the company, they want
to direct their marketing efforts from
converting casual users( those who use
single-ride and full-day pass) to members.

3. Executive Summary
• 47% of the total users of Cyclistic bike-share are casual
users, this means that converting these 47% casual
users to member will benefit the company
• Key Metrics from the latest 12 months Ride Usage
Data for Cyclistics:
• 68% of the ride usage constitutes to the use of
classic bikes, followed by 27% usage of electric
bikes and 5% usage of docked bike
• 38% or 1.2M of the total users of classic bikes are
casual users, this accounts to 60% of the total
casual users of Cyclistic bike-share
• Members and Casual users has opposite ride
usage trend during the week, Members use the
bike-share more during weekdays while Casual
users use the bike-share more during the
weekend
• Cyclistic’s marketing strategy on converting casual
users to members should focus on targeting Casual
Users of Classic Bikes on weekends, this segment
represents 26% of their current users and would
potentially increase their membership to 83%

4. With the casual
users accounting to
47% or 2,007,048 of
the total users of
Cyclistic’s bike-
share, converting
casual users to
members will be
impactful for
Cyclistic’s profit.
47%
53%
0
500,000
1,000,000
1,500,000
2,000,000
2,500,000
3,000,000
casual member
Number
of
Rides Rider Type
Table 1: Number of Rides as per Rider Type

5. Narrowing down
the usage of the
casual users, it is
worth considering
the bike type they
used the most.
Considering the
ride usage in the
past 12 months,
casual users have
opted more on
using the classic
bike
1213738
252014
541296
1968104
703623
0
500000
1000000
1500000
2000000
2500000
classic_bike docked_bike electric_bike
Table 2: User Type as per Bike Type
Casual Member

6. To achieve
maximum
penetration of the
target market,
knowing when and
where to launch the
marketing strategy
is an important
factor to consider.
Considering the
trend in the past 12
months, market
strategy should be
implemented
between the 2nd
and 3rd quarter of
the year
0
50000
100000
150000
200000
250000
300000
350000
400000
Jul-21 Aug-21 Sep-21 Oct-21 Nov-21 Dec-21 Jan-22 Feb-22 Mar-22 Apr-22 May-22 Jun-22
Table 3: Ride Usage by Rider Type by Month
casual member

7. The inverse
relationship of the
member’s and
casual user’s riding
usage shows that
casual users use the
bike-share more
during the weekend
while members use
it more during the
weekday 0
50000
100000
150000
200000
250000
300000
350000
400000
450000
500000
Sun Mon Tue Wed Thu Fri Sat
Table 5: Ride Usage by Rider Type by Weekday
casual member

8. Observation
What: Marketing Strategy to convert casual users to Annual
Members
Who: Target Market are the casual users using Classic Bike
When: During the 2nd and 3rd Quarter of the year with focus
on the weekend
Where: Digital Marketing and OOH Marketing
How: Digital Marketing (Social Media, Cyclistic App, Ads) and
OOH Marketing (Stations frequented by the target market)
Why: To increase Annual membership which equates to
increase in profit for the company

9. Recommendation
Introduction of a Weekend Annual Plan – to even increase usage on
weekend, we should introduce a weekend annual plan where consumers
can use the bike-hire as much as they want during the weekend
Digital Marketing campaign - Social Media (Facebook,
Instagram,Tiktok,Twitter), Ads on website, quick surveys
OOH Marketing -Billboard on high traffic areas and on the station of the
bike-hires as well
in-APP Marketing - Marketing on the app itself whenever used
Introduction of a special discount for Casual Users of Classic Bikes to
upgrate to Member – This will launched on the app whenever the user
starts using the app

10. Index: Codes
used in R
#Installing the necessary packages required
install.packages("tidyverse")
install.packages("lubridate")
install.packages("ggplot2")
install.packages("anytime")
#Uploading the library of functions that will be used
library("tidyverse")
library("lubridate")
library("ggplot2")
library("anytime")
#Uploading the 12 inidividual data frame
july_2021 <- read_csv("C:/Users/Mark Ferrera/Desktop/Data Analysis Portfolio/Data Source/202107-
divvy-tripdata.csv")
august_2021 <- read_csv("C:/Users/Mark Ferrera/Desktop/Data Analysis Portfolio/Data Source/202108-
divvy-tripdata.csv")
september_2021 <- read_csv("C:/Users/Mark Ferrera/Desktop/Data Analysis Portfolio/Data
Source/202109-divvy-tripdata.csv")
october_2021 <- read_csv("C:/Users/Mark Ferrera/Desktop/Data Analysis Portfolio/Data
Source/202110-divvy-tripdata.csv")
november_2021 <- read_csv("C:/Users/Mark Ferrera/Desktop/Data Analysis Portfolio/Data
Source/202111-divvy-tripdata.csv")
december_2021 <- read_csv("C:/Users/Mark Ferrera/Desktop/Data Analysis Portfolio/Data
Source/202112-divvy-tripdata.csv")
jan_2022 <- read_csv("C:/Users/Mark Ferrera/Desktop/Data Analysis Portfolio/Data Source/202201-
divvy-tripdata.csv")
feb_2022 <- read_csv("C:/Users/Mark Ferrera/Desktop/Data Analysis Portfolio/Data Source/202202-
divvy-tripdata.csv")
mar_2022 <- read_csv("C:/Users/Mark Ferrera/Desktop/Data Analysis Portfolio/Data Source/202203-
divvy-tripdata.csv")