In this project, I analysed a fictional bike share company’s usage data using R and Tableau to identify trends and strategies for converting casual riders into annual members.
The findings showed that casual riders tend to take longer rides, especially during the summer. Based on the insights, I proposed targeted marketing campaigns and membership offers to improve user retention and engagement.
Cyclistic is a fictional bike-share company based in Chicago with:
- 5,800+ bicycles and 600+ docking stations
- Primarily traditional bikes, with 8% electric-assisted usage
- Users who mostly ride for leisure, with 30% commuting regularly
The marketing team's goal is to understand usage patterns between casual riders (single-ride or day-pass) and annual members, in order to grow memberships.
How do annual members and casual riders use Cyclistic bikes differently?
Identify trends that can help convert casual riders into annual members.
- Public Divvy trip data (used under Cyclistic's fictional context)
- 📂 Data source link
- R (R Studio) for data cleaning, transformation, and analysis
- Tableau for visual storytelling
- Cleaned and merged multiple monthly datasets in R
- Conducted exploratory data analysis (EDA) on:
- Ride duration
- Seasonality and peak hours
- Weekday vs weekend usage
- Bike and station preferences
- Exported clean dataset to Tableau Public
- Built a slide deck-style visual story to present business context, methodology, key findings, insights, and recommendations for stakeholders
- Casual riders take longer rides, especially during weekends and summer months.
- Members have more consistent usage year-round, peaking during weekday commute times (8 AM, 12 PM, 5 PM).
- Summer months see the highest ridership, especially among casual riders.
- July (casual) and August (members) were peak months.
- Top stations are similar across segments but ranked differently.
- Classic bikes are most used; docked bikes are only used by casual riders.
- Casual riders follow leisure/tourist patterns, while members align with commuter behavior.
- A subset of casual riders mirrors member behaviour — these are prime targets for conversion.
- Weekends and summer afternoons offer major opportunities for seasonal campaigns.
- Market cost savings of memberships vs. day passes
- Email personalised usage stats showing how much they could save
- Incentivise referrals and promote during weekday peaks (8 AM, 12 PM, 5 PM)
- Create summer-only membership options or weekend passes
- Use on-site ads at popular stations/tourist spots
- Introduce points-based reward systems for long rides
- Only one year of data analysed (April 2022–March 2023)
- No demographic data (age, gender, income)
- Station name inconsistencies affected mapping and distance calculations
In a future version, I plan to include:
- Station Analysis: Peak usage times and logistics planning
- Bike Type Analysis: Usage trends for classic vs. electric
- Route/Distance Analysis: Identify commuting corridors and tourist loops
cyclistic_analysis_notebook.ipynb: Jupyter Notebook version of the project (downloaded from Kaggle)README.md: This project overview- Tableau visual slide deck: View here
📌 This case study was completed as part of the Google Data Analytics Capstone.
All brand names used are fictional.