As part of my capstone journey in data analytics, I also worked on the Cyclistic bike-share case study. Cyclistic is a fictional bike-share company in Chicago with over 5,800 bikes and hundreds of stations across the city. The business challenge:
How do casual riders and annual members use Cyclistic bikes differently — and how can we turn casual riders into loyal annual members?
What I Did
I followed the data analysis process step by step:
- Ask → Define the goal: identify usage differences between casual and annual riders.
- Prepare → Gathered 12 months of bike trip data (see dataset here).
- Process → Cleaned the data, created new fields (ride length, day of week).
- Analyze → Compared average ride times, number of rides by weekday, and seasonal usage patterns.
- Share → Built visualizations to highlight key differences.
- Act → Developed recommendations to support marketing strategy.
Key Insights
- Casual Riders: Take longer rides, especially on weekends.
- Annual Members: Ride more frequently and often during weekdays, indicating commuter behavior.
- Profitability: Annual members bring more stable, long-term revenue.
Recommendations for Cyclistic
- Weekend Promotions – Offer membership discounts during peak casual use (weekends).
- Targeted Digital Campaigns – Use social media/app notifications to show cost savings of memberships.
- Commuter Messaging – Position annual plans as the best option for daily commuters.
Why It Matters
This project showed how data storytelling can directly inform business growth strategies. For Cyclistic, it’s about converting casual riders into committed members. For me, it was hands-on experience in cleaning, analyzing, and visualizing large datasets.
Full Analysis & R Code on Kaggle:
Check out my Kaggle Notebook here
