Cyclistic Bike-Share Analysis
Analyzing rider data to develop a targeted marketing strategy for converting casual riders into paying members.
The Business Challenge
Cyclistic, a popular bike-share company, has two types of customers: "casual" riders who pay per ride, and "member" riders who pay for an annual subscription. While both are valuable, annual members are the financial backbone of the company, providing a steady stream of revenue.
The company's leadership believes there's a huge opportunity to convert more casual riders into members, but they don't know where to start. My mission was to analyze a year's worth of ride data to understand the key differences between these two groups and recommend a data-driven marketing strategy.
My Analytical Process
Data Aggregation & Cleaning
I started by collecting 12 months of ride data. This massive dataset needed to be carefully cleaned and formatted to remove errors and ensure every trip was accurately recorded, creating a reliable foundation for analysis.
Feature Engineering
From the raw data, I created new, more insightful metrics. This included calculating the duration of each ride, identifying the day of the week, and determining the month, which helped uncover deeper behavioral patterns.
Comparative Analysis
This was the core of the project. I compared casual and member riders across various dimensions: How long do they ride on average? Which days of the week are most popular for each group? Do they use different types of bikes?
Insight Synthesis & Visualization
Finally, I summarized my findings into clear, actionable insights. I created compelling visualizations to present the story in the data, making it easy for stakeholders to understand the "why" behind my recommendations.
Key Information
- Type: Marketing Analytics
- Tools: R, Tableau
- Dataset: 12 Months of Ride Data
Technology Stack
- R (Tidyverse)
- Tableau
- SQL
- Microsoft Excel
Key Features
- Business Focus: Delivers a clear marketing strategy to boost annual memberships.
- Business Focus: Identifies distinct user personas for targeted campaigns.
- Technical: Involves large-scale data cleaning and manipulation of over 5 million rows.
- Technical: Features compelling data visualizations to communicate complex findings simply.
Actionable Insights for Growth
The analysis revealed clear, distinct patterns between casual riders and annual members, paving the way for a targeted conversion strategy. The key is to understand their different motivations for riding.
Weekend Explorers vs. Weekday Commuters
Casual riders dominate the weekends for longer, leisurely trips. Members, on the other hand, ride more frequently during the week for shorter, purposeful commutes.
Ride Duration Tells a Story
The average ride for a casual user is significantly longer than for a member, suggesting different use cases (e.g., tourism vs. daily transport).
Recommendation: A "Commuter Pass" Campaign
Target casual riders who show weekday usage with a campaign focused on the cost savings of an annual membership for daily commutes.