My Google Data Analytics Project | Cyclist Bike

Cyclists Bike Sharing Company — Leveraging data to Convert Casual Users To Annual Subscribers

About the Company

Cyclistic introduced a popular bike-share program in 2016. The initiative has expanded since then to include a fleet of 5,824 bicycles that are geo-tracked and locked into a system of 692 stations throughout Chicago.

Up to this point, Cyclistic’s marketing approach focused on raising public awareness and appealing to a wide range of consumer groups. There were two different user kinds. Casual riders are customers who buy single-ride or full-day passes, whereas Cyclistic members are customers who buy annual memberships.

Action point:

Moreno has set a clear goal: Design marketing strategies aimed at converting casual riders into annual members. In order to do that, however, the marketing analyst team needs to better understand how annual members and casual riders differ, why casual riders would buy a membership, and how digital media could affect their marketing tactics. Moreno and her team are interested in analyzing the Cyclistic historical bike trip data to identify trends.

Three questions will guide the future marketing program:

1. How do annual members and casual riders use Cyclistic bikes differently
2. Why would casual riders buy Cyclistic annual memberships?
3. How can Cyclistic use digital media to influence casual riders to become members?

My Task

You will produce a report with the following deliverables:

1. A clear statement of the business task
2. A description of all data sources used
3. Documentation of any cleaning or manipulation of data
4. A summary of your analysis
5. Supporting visualizations and key findings
6. Your top three recommendations based on your analysis

Business Objectives

1. Analyze available company data for each ride from June 2021 to May 2022, looking at the similarities and differences in how both user types use the company’s bicycles.
2. Find out what motivates casual riders to use Cyclists on a daily basis.
3. Building a marketing campaign hinged on technology that aims to convert casual or one-time riders into subscription members — members

Description of all data sources:

Following the Data Wrangling steps, the data was gathered from here. It is always updated. We were to gather for 12 months and make analysis of them.

Assess: I made use of the R programming language to Assess the data programmatically and to join all data together. I also made use of Excel spreadsheet for visual assessment to ensure the data met necessary standards.

Documentation of any cleaning or manipulation of data

Some of the cleanings that I did were:

1. Merged 12 data frames into one.
2. Change the Started_at, Ended_at and ride_length column to date-time column
3. Remove duplicates
4. Remove NA’s and so on

A summary of your analysis

After gathering the dataset — prepare, I merged the datasets, and then I checked for inaccuracies in the dataset such as missing values, wrong datatypes, duplicates and so on — Process. I did this by taking a look at the dataset rows, summary, columns, and statistical summary. This was done with the use of Excel and R functions.

Analyze:

This fourth phase is to analyze the data. The primary goal in this phase is to find the relationships, trends, and patterns that will help you solve your business problem more accurately

What are the relationships between the usertypes?

As we can see, the member users are the most users. Recall one of the goal is to build a marketing campaign that converts casual riders into members.

Next, what are the ride service types offered?

So, we have the classic bike, docked bike and electric bike types of ride.

Further exploration to see which ride type each usertype uses mostly:

So, the casual riders make use of the docked bike and then both the casual and member users use others.

What can one infer from this?

The Classic bike is a gasoline bike, while the Electric bike is an electric ride while Docked bike means a bike that is designed to be locked or secured from unauthorized use by being locked or secured to a dock, rack, sharing station, or another object that is provided under the program.

So we can assume that the casual riders are those who make use of the services for a shorter distance and while the member riders make use of the services to cover longer distances since there are mostly either electric or classic. These are ease free and can cover longer distances.

Gasoline bikes are the classic bikes and are used by mostly the subscribed members and it most ride minute is between 4–15 minute

While that of electric bike, it most ride minute is between 5–10 minutes

And for docked, it goes for longer minute between 10–30 minutes.

What time do they use the ride mostly?

By this, we can see that most rides occur during the afternoon to evening hours. Also, they seem to be those who make use of this service at 8 am. This means some make use of the service to maybe their workplace or school.

How many minutes are rides taken?

Rides are mostly taken for 6 minutes distance.

We see that the casual customer types use the ride service for a longer time than the member customer types. So we can say that the member subscribers are those whose destinations are within 4 minutes ride.

These among others are some of the analyses done on this project and other analysis are done on Tableau

Referrals

Click here to take you to the tableau visuals

Click here to be linked to my github

Tools & Datasets

1. Excel — For basic data analysis and exploration
2. R Studio — For advanced data analysis.
3. Tableau — For data visualization.

Recommendations

1. Run a campaign highlighting the cost-saving and health benefits of being a subscribed member than a casual member: This would help convince the casual riders on why it’s best to be a subscribed member. There could also be advertisements done to show how easier it is to quickly hop on a bike than waiting for a bus or ride and also, the health benefit of cycling.
2. Create a new annual subscription Plan aimed at Casual Riders: We noticed that even with the low total number of rides compared to subscription members, they ride for longer minutes. We can create a subscription plan that favors longer rides. This plan would be cheaper than the regular annual plan but would only be accessible to riders who exceed a daily average ride time of 30 minutes.
3. Discount for morning rides: As seen that the member uses the rides in the morning at a good number. There could be a discount price for morning users to encourage the casual riders to consider the services for their morning movement and thereby causing the casual riders to want to subscribe to be a member.

Limitations

1. I couldn’t extract the weekdays to make an analysis as it appears the ‘started_at’ columns gave time only and not date-time. Whenever I tried extracting the date, it gives my current date of analysis.
2. No idea on the occupation of the users

My take on the project:

I decided to want to make use of R to make the analysis as I am a beginner in R. This project has made me understand that the analysis process are the same, and the programming languages or tool used doesn’t really matter. it’s just the same process.

Thank you for reading