Google Data Analytics Project 2: Bike Share Case Study Project
Transforming Data into Strategy: How I Converted Cyclistic's Casual Riders into Members.
Published on: 18th October, 2025
Tags: #DataAnalytics #CaseStudy #RStudio #Tableau #Cyclistic #MarketingStrategy
Introduction: The Business Problem
As a Data Analyst for the fictional bike-share company Cyclistic, my core mission was simple: How do annual members and casual riders use Cyclistic bikes differently, and how can we use that insight to convert casual riders into annual members?
This project moved through the entire data analysis process—from Ask to Act—culminating in clear, data-backed marketing recommendations for the Director of Marketing.
1. Ask & Prepare: Defining the Challenge
The business objective was to drive membership growth. We started with 12 months of Cyclistic trip data (publicly available historical data) and prepared it for analysis.
Below are the datasets that are made available for this project:
Initial Challenge: The data was inconsistent, with key columns like
ride_idhaving different data types across different quarters.Key Action: We standardized column names and data types (e.g., ensuring all
ride_idcolumns were character strings) and merged the data into a single, cohesive dataset using R.
2. Process & Clean: Ensuring Data Quality
The raw data contained outliers that would skew our averages, particularly in the calculated field, ride_length (the duration of the trip).
Outlier Identification: We identified trips with a duration of 0 minutes (errors) and trips longer than 24 hours (1,440 minutes), which were considered extreme outliers likely representing lost or stolen bikes.
Key Action: We filtered out all trips shorter than 1 minute and longer than 1,440 minutes to ensure our final metrics were based on legitimate user behavior.
3. Analyze: Uncovering the Core Behavioral Gap
Our analysis focused on key metrics: Average Ride Duration, Total Volume, and Temporal Trends (Day of Week). This is where the story truly began to take shape.
Rider Type | Average Duration (Minutes) | Insight |
|---|---|---|
Annual Member | 11.47 minutes | Confirms short, utility-focused trips (commuters). |
Casual Rider | 38.48 minutes | Confirms long, leisure-focused trips (recreation). |
Visual Evidence 1: Duration Proves Purpose
The difference is stark: Casual Riders spend more than 3x the time on the bikes than members. This strongly suggests that members view the bike as a means to an end (transportation), while casual riders view it as an activity (leisure).
[
'Avg. Ride Duration by Rider Type'
]
Visual Evidence 2: Weekend Warriors
Analyzing usage by day of the week provided the crucial temporal context for when to target conversions.
Annual Members peak during the work week (Mon-Fri).
Casual Riders peak dramatically on Saturday and Sunday.
[
'Total Rides by Day of Week'
]
4. Share & Act: The Final Dashboard and Strategy
The findings were consolidated into a dynamic Tableau Dashboard to present the recommendations to stakeholder
Here is the link to the tableau dashboard and the embed image after it.
Link: Bike Share Dashboard for Google Data Analytics Project
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]
The dashboard highlights the two most important KPIs: 11.47 min (Member Average) and 38.48 min (Casual Average). This visual disparity drives the final strategy.
Stakeholder Presentation also captured here for your review.
Below is the link for the stakeholder presentation:
Link: Stakeholder Presentation Slides
Based on the evidence that Casual Riders are primarily leisure-focused, weekend users who overpay for long trips, we proposed three actionable recommendations:
Top 3 Actionable Recommendations
Introduce the "Weekend Pass" or "Leisure Membership"
Goal: Offer a discounted, auto-renewing subscription covering Friday through Sunday use. This validates their existing behavior while providing a clear cost-saving alternative to expensive single-ride passes.
Implement Real-Time Cost-Saving Communication
Goal: Target Casual Riders immediately after a long, expensive weekend trip via the app or email. The message must clearly state: "You just spent $X. An Annual Membership would save you $Y after only [Z] more similar rides!"
Geo-Target Promotions at Leisure Hubs
Goal: Focus advertising (digital and physical signage) at stations near parks, lakefronts, and tourist destinations—the specific places where casual usage volume is highest.
Conclusion
By shifting our focus from demanding that casual riders become commuters to simply making their existing leisure habit more affordable, Cyclistic can unlock a significant new stream of annual membership revenue. This project demonstrates the power of using data to understand user behavior and design a strategy that aligns with their needs, rather than trying to change them.
What are your thoughts on this strategy? Do you have any questions about the data process? Let me know in the comments!
Email: matthewbeeun@gmail.com
Phone: +2348115231834
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