🚲 Cyclistic Bike-Share Case Study (Dec 2023 – Jan 2025)

Author: Nikki Carlson
Tools: R (tidyverse, lubridate, janitor), Tableau, RMarkdown
Status: Completed project

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📌 Project Overview

This case study analyzes 14 months of Cyclistic bike-share trip data (Dec 2023 – Jan 2025) containing over 6.2 million ride records.

The goal is to identify behavioral differences between casual riders and annual members in order to support data-driven marketing strategies that increase membership conversions.

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🔗 Project Links

📊 Interactive Tableau Dashboard
https://public.tableau.com/app/profile/nikki.carlson2355/viz/CyclisticUsageInsights/CyclisticUserBehaviorAnalysis2024

📄 Full RMarkdown Report (RPubs)
https://rpubs.com/Nikki0686/1334937

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📊 Dashboard Preview

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🔍 Business Questions

This analysis focuses on three key questions:

• How do casual and member riders differ in their riding patterns?
• When do peak usage times occur by hour, day, and season?
• What behavioral trends can support targeted marketing and rider conversion?

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🪚 Data Cleaning

The dataset required several preparation steps before analysis:

• Combined 14 monthly CSV files (Dec 2023 – Jan 2025)
• Standardized inconsistent timestamp formats
• Filtered rides under 3 minutes or over 24 hours
• Removed invalid or missing station coordinate records

Engineered Features

• ride_length (minutes)
• hour_of_day
• day_of_week
• season
• week_start

The final dataset contains approximately 5.99 million valid ride records.

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📊 Visual Analysis

The Tableau dashboard highlights key usage patterns including:

• Peak ride hours by rider type
• Ride duration comparisons (member vs casual)
• Day-of-week riding behavior
• Seasonal ride volume trends
• Monthly ridership patterns

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🧠 Key Findings

Insight	Business Relevance
Casual riders peak on weekends and midday	Suggests recreational usage patterns
Members ride consistently during weekday commuting hours	Indicates commuter-focused behavior
Casual rides tend to last longer	Recreational riders may benefit from flexible membership offers
Summer shows the highest casual ridership	Seasonal promotions may improve membership conversion

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🗺️ Strategic Recommendations

• Launch weekend promotions targeting recreational riders
• Offer seasonal or short-term membership plans
• Use post-ride conversion offers for frequent casual riders
• Highlight commuter savings in in-app membership messaging

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🔄 Data Pipeline

Data Ingestion

Load 14 monthly CSV files from the Divvy dataset.

Cleaning & Transformation

• Timestamp standardization
• Ride length filtering
• Feature engineering

Export

• Create Tableau-ready summary datasets
• Export cleaned dataset for reproducibility

Visualization

Build an interactive dashboard in Tableau.

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📂 Repository Structure

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⚠️ Raw Divvy trip data (~6M records) is not included due to repository size limits.

Download the dataset here:
https://divvy-tripdata.s3.amazonaws.com/index.html