Google Data Analytics Capstone Project: Cyclistic Case Study
Behavioural differences between Casual riders and Annual Members
How does a bike-share navigate speedy success?
Introduction:
Cyclistic is a fictional bike-sharing company that operates in a city. Let us examine Cyclistic’s bike-share data as a case study and discuss some conclusions and suggestions that may be drawn from the data presented.
Scenario:
My primary goal as a junior data analyst on the marketing analyst team at Cyclistic is to increase the quantity of yearly memberships. In order to do this, I must examine the ways in which annual members and casual riders use Cyclistic bikes differently. I can contribute to the creation of a new marketing plan targeted at turning casual riders into yearly members by learning more about their usage habits. My advice will be backed by strong data insights and expert data visuals in order to persuade Cyclistic leaders.
Stakeholders:
Lily Moreno is a team manager for data analysts and the director of marketing. In charge of marketing initiatives and making decisions using data insights.
Data analysts that specialize in gathering, analyzing, and reporting data for marketing strategy comprise the Cyclistic Marketing Analytics Team.
Cyclistic Executive Team: Group responsible for evaluating and approving marketing initiatives based on data insights.
Business Task:
The business task is to create a marketing plan that will turn casual riders into annual members by understanding how they utilize cycling bikes differently from annual members.
About the Company:
Cyclistic introduced a successful bike-sharing program in 2016. Since then, the initiative has expanded to include 5,824 bicycles that are locked into a network of 692 stations throughout Chicago and geotracked. The bikes can be unlocked at any station in the system and then returned to any other station at any time.
Up until now, Cyclistic’s marketing approach has focused on increasing brand recognition and attracting a wide range of consumer types. One strategy that made these things possible was the price plans’ flexibility, which included annual memberships, full-day permits, and single-ride passes. Riders who buy full-day or single-ride passes are called casual riders. Cyclistic members are customers who buy yearly memberships.
The financial researchers at Cyclistic have determined that annual members generate significantly higher profits than casual riders. Moreno thinks that increasing the number of yearly members will be crucial for Cyclistic’s future success, even though the pricing flexibility helps the company draw in more clients. Instead of launching a marketing effort aimed at attracting brand-new clients, Moreno thinks there’s a strong possibility of turning casual riders into members. She points out that casual riders have already chosen Cyclistic for their mobility needs and are already aware of the program.
Designing marketing tactics to turn infrequent riders into yearly members is Moreno’s well-defined objective. But to achieve that, the marketing analyst team must have a deeper understanding of the distinctions between casual riders and yearly members, the reasons behind the purchase of a membership by casual riders, and the ways in which digital media may influence their marketing strategies. The Cyclistic historical bike trip data is of interest to Moreno and her team for trend analysis.
Ask:
How do annual members and casual riders use Cyclistic bikes differently?
Prepare:
Data Sources: Cyclistic’s historical trip data for the years 2022–2023 will be utilized. Information on bike rides, such as start and end times, ride duration, user type (annual member or casual rider), and other pertinent facts, are included in the publicly accessible data.
Data Organization: The information is offered as CSV files, one for every month during the preceding twelve months. Each file has columns that each correspond to a particular bike trip attribute.
Data Credibility and Integrity: Motivate International Inc. provides the data under an appropriate license. However, certain restrictions prohibit the use of personally identifying information because of privacy concerns.
Data Privacy: To ensure privacy compliance, the data does not contain any personally identifiable information.
Process:
I began the data processing by making a table out of each CSV file. Since there were more than 5 million rows total, the 12 CSV files were imported using the ETL tool Power Query. Blank rows and unwanted columns (ended_at, start_station_id, end_station_id) were removed, text columns were capitalized, and custom columns to calculate ride length, day of the week, start time, and end time were added. Next, each column’s data type was altered, the column names were adjusted, and the transformed data was fed into Power Bi as a connection.
These are the steps taken to transform the raw data with Power Query.
With 14 columns and 4482556 rows, here is how the data appears following cleaning and manipulation.
Analyse and Share:
The analysis stage of the process begins after the data has been cleaned and modified. Choose a visual in Power Bi that is appropriate for your analysis, then drag and drop the columns into the Visualization pane’s field area.
Total number of rides:
Average Ride Length in minutes:
In comparison to Members, Casual riders typically ride their bikes for longer periods of time.
Ride Distribution:
(a) by User Type
The graphic indicates that there are almost 20% more Members (2710044) than Casual riders (1772512).
(b) by Bike Type and User Type
The Casual Riders use all three varieties of bikes, as can be seen below. The most popular bikes among casual riders are classic bikes, followed by electric bikes and docked bikes. On the other hand, the members exclusively ride two types of bikes: electric and classic.
Peak Days of Week:
To display the distribution of rides by day of the week, I have utilized Treemap. The number of rides taken on a specific day of the week is represented by the size of each rectangle in the treemap.
The weekends — Saturday, Sunday, and Friday — are the most popular times for casual riders to use bikeshare.
Tuesday, Thursday, and Wednesday are the busiest days for bikeshare rides among Members.
Rides by Month:
The area chart above demonstrates how the number of rides for each user type rises in the spring and then falls in the summer and fall.
Top 10 Busiest Stations:
We can infer from the preceding charts that casual riders travel between stations for pleasure or business. However, the Members usually commute on their normal routes using the bikes.
Act:
Insights:
- While casual riders use Cyclicistic bikes for enjoyment, annual members utilize them for commuting.
- Compared to annual members, casual riders ride for longer periods of time.
- Weekend riding is more common among casual cyclists than among yearly members.
- It is more common for casual cyclists to begin and end their trips in well-known tourist locations.
Recommendations:
Certainly, here are some suggestions based on the findings of the Cyclistic bikeshare capstone project.
- Casual Rider Promotions: Encourage casual riding during off-peak or slower seasons by giving discounted rates, promotions, or package deals to make the service more appealing.
- User Education: Provide specific information regarding the advantages of being a member, such as cost savings, convenience, and additional features such as bike tracking or app integration.
- Ride Duration Incentives: Create incentives for members to go on longer rides, such as rewards or discounts for rides that last longer than a specific amount of time.
- Partnerships: Establish partnerships with local businesses, events, or attractions to offer joint promotions or discounts, enhancing the overall experience for both casual riders and members.
