Divvy Trips Cyclistic Bike Share

OBJECTIVE
This case study explores usage patterns of casual riders and annual members at Cyclistic, a Chicago-based bike-share company. Using R and advanced visualization techniques, I identified key differences in riding behavior. These findings inform actionable strategies to convert casual riders into annual members, supporting Cyclistic’s growth objectives.

Company Background
Cyclistic is a Chicago-based bike-share company with over 5,800 bicycles and 600+ docking stations. It differentiates itself with inclusive bike options, including reclining bikes and hand tricycles. While most riders use bikes for leisure, 30% commute daily. The company’s flexible pricing plans include single-ride passes, full-day passes, and annual memberships. Annual members are more profitable than casual riders, making their conversion a strategic priority.

Ask
Under the direction of marketing director Lily Moreno, the team seeks actionable insights to:

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?

However, the specific focus is to address the first question: How do annual members and casual riders use Cyclistic bikes differently?

Prepare
I downloaded Cyclistic’s historical trip data Divvy_Trips_2019_Q1.csv and Divvy_Trips_2020_Q1.csv for purposes of analyzing and identifying trends. I was able to identify how the data was organized and determine the credibility of the data.

Process
In this phase, the aim is to refine the data. The process is to find and eliminate any errors and inaccuracies that can get in the way of results. I utilized RStudio to clean and transform the data, ensuring it was structured in a more usable and analysis-ready format. Additionally, I integrated two separate datasets to create a more comprehensive and complete dataset for the analysis.

Analyze
The analysis focuses on understanding ride duration patterns for Cyclistic’s users, comparing casual riders and annual members. The key steps and findings include:

1.Descriptive Statistics:
Calculated the mean, median, maximum, and minimum ride lengths to summarize overall ride duration. The summary() function provides a consolidated view of these metrics.

2.Comparison by User Type:
Ride durations were analyzed separately for casual riders and members using aggregate functions (mean, median, max, min).

3.Day-Specific Analysis:
Explored average ride durations for each user type across weekdays. Days were reordered logically from Sunday to Saturday for accurate representation.

4.Ridership Patterns by Weekday:
Created a new weekday field using the ride start time and grouped data by user type and weekday. Metrics such as the total number of rides and average ride duration were calculated.

Share

Data Visualizations are your best friend. I was able to create the:
a.Number of Rides: Visualized the total rides by weekday and user type using a grouped bar chart.
b.Average Duration: Developed a similar grouped bar chart to depict average ride durations for casual riders and members across weekdays. This chart revealed how ride durations vary between user groups and across different days, offering insights into the behavioral differences between casual and annual riders.

Act

1. Final Conclusion Based on Analysis

My analysis highlights significant differences in usage patterns between casual riders and annual members. Key findings include:

a.Casual riders take longer rides on average, especially on weekends, while members have shorter, more frequent rides, likely indicating commuting habits.
b.Weekday usage is higher among members, aligning with workday commuting trends, whereas casual riders favor weekends for leisure.
c.The difference in ride frequency and duration suggests targeted strategies can address casual riders' specific needs to encourage membership conversion.

2. Recommendations for Business Application

Top Three Recommendations:

a.Offer Exclusive Weekend Membership Benefits:
Provide special incentives like discounted weekend rates or exclusive ride perks for members to attract casual riders who primarily ride for leisure.

b.Launch a "Trial Membership" Program:
Create a short-term membership option (e.g., one-month trial) to let casual riders experience the benefits of annual membership, targeting their frequent weekend usage.

c.Focus Digital Campaigns on Weekend Riders:
Use ads and personalized email campaigns to highlight how membership enhances their riding experience. Showcase cost savings and convenience for frequent users.

3. Next Steps for Stakeholders

a.Implement a pilot marketing program to test these recommendations and measure their effectiveness in converting casual riders to members.

b.Conduct follow-up analysis on the program’s success, leveraging user feedback and updated ride data.
Explore partnerships with local businesses to offer discounts or promotions for members.

4. Additional Data for Expansion

Collect survey data from casual riders to understand barriers to membership and preferred incentives.
Analyze seasonal patterns to adjust marketing efforts during peak riding months.