Case study for Google Data Analytics Certificate

1. Case Study 1-Bicycle Riding
Prepared by: Li Zhang
Date: August 22, 2022

2. Contents:
 1. Business task
 2. Data process
 3. Data analysis result
 4. Recommendation

3. 1.Business Task
 Business Background
 Business Goal
 Stakeholders for this project

4. Business Background
 Cyclistic launched a bike-share offering in 2016
 Business Revenue Module:
A. Single-ride pass, full-day pass -referred as casual riders.
B. Annual memberships.
C. Annual members are more profitable.

5. Business Goal
Marketing strategy is to convert casual riders into annual
members.
 Better understand the difference of annual members and
casual riders' behavior.
 Why casual riders would buy a membership.
 How digital media to affect their marketing tactics.

6. Stakeholders for This Project
 Customer- ride for leisure and commute to work
 Lily Moreno-The director of marketing and my manager,
responsible of media marketing
 Cyclistic marketing analytic team-collecting, analyzing and
reporting data to help marketing strategy
 Cyclistic executive team-approve the recommended
marketing program

7. 2.Data Process
 The data downloaded from this link:
https://divvytripdata.s3.amazonaws.com/index.html.
 The data is from Chicago city public data and the data is
reliable.
 2020 April to 2021 March data were used to do this case.
 I used excel to clean and transform the data.
 SQL tool were not used because of the purchase cost

8. 3.Data Analysis Result
 Calculation result :
 Saturday is the date riders used the bicycle mostly often
 The mean of rider-length is about 30 minutes
Year-Month 2021-03 2021-02 2021-01 2020-12 2020-11 2020-10 2020-09 2020-08 2020-07 2020-06 2020-05 2020-04
Mean of ride_length 0:22:52 0:24:25 0:15:16 0:16:40 0:19:42 0:19:59 0:25:16 0:29:47 0:38:16 0:33:33 0:33:23 0:35:51
Max of ride_length 22:34:06 22:09:14 18:25:55 18:20:59 22:54:00 19:24:23 16:43:21 23:59:49 16:45:27 15:51:00 22:28:44 18:40:02
Mode of day_of_week 7 7 7 4 7 7 7 7 6 7 7 1

9. 3.Data Analysis Result
Year-month 2021-03 2021-02 2021-01 2020-12 2020-11 2020-10 2020-09 2020-08 2020-07 2020-06 2020-05 2020-04
casual 0:38:10 0:49:22 0:25:41 0:27:30 0:31:45 0:30:09 0:38:07 0:44:46 0:59:49 0:51:37 0:51:11 1:13:04
member 0:13:58 0:18:01 0:12:52 0:13:27 0:13:32 0:13:57 0:15:28 0:16:45 0:17:41 0:18:42 0:19:45 0:21:28
Grand Total 0:22:52 0:24:25 0:15:16 0:16:40 0:19:42 0:19:59 0:25:16 0:29:47 0:38:16 0:33:33 0:33:23 0:35:51
0 0.01 0.02 0.03 0.04 0.05 0.06
Casual
Grand Total
Member
Average of 2021-03
Average of 2021-02
Average of 2021-01
Average of 2020-12
Average of 2020-11
Average of 2020-10
Average of 2020-09
Average of 2020-08
Average of 2020-07
Average of 2020-06
Average of 2020-05
Average of 2020-04
Member riders length is much shorter than casual riders, seems current members use bicycle
to commute and casual riders are more likely to use bicycle for leisure in weekend.

10. 3.Data Analysis Result
0
50000
100000
150000
200000
250000
300000
350000
casual member
2020-04
2020-05
2020-06
2020-07
2020-08
2020-09
2020-10
2020-11
2020-12
2021-01
2021-02
2021-03
Casual riders right now is account almost half of total riders, casual riders and member riders have very
similar behavior during different season of the year. Riders prefer to use bike to commute and leisure in
warm weather.

11. 4. Recommendation
 Our goal is to convert casual riders to members.
 Casual riders use bike much longer than members and
usually in weekend
 We can change revenue model:
change single-ride pass and full-day pass to charge money every 30
minutes usage, since most casual riders ride more than 30 minutes, this
charge model will push them to buy membership.
add half-year-membership pass to attract casual riders,since most riders
use bicycle from June to November.

12. 4. Recommendation
 How to do marketing:
 Casual riders already have some contact information in our
company, we can use text and email to send them the new
revenue charge model to help them make decision to buy
membership.