Cyclistic Bike-Share Analysis

Project Overview

This project analyzes Cyclistic's bike-share usage in Chicago to understand how annual members and casual riders use the service differently. The goal is to identify behavioral patterns that can help Cyclistic develop more effective strategies for converting casual riders into annual members.

The analysis examined 84,776 rides, including 61,148 member rides (72.13%) and 23,628 casual rides (27.87%). Using spreadsheet-based data cleaning, analysis, pivot tables, and visualizations, I compared the two rider groups across ride volume, trip duration, day of the week, time of day, weekday versus weekend usage, and starting stations.

Key Findings

The analysis identified several important differences between members and casual riders:

• Members account for the majority of rides: 72.13% of all rides were taken by annual members, compared with 27.87% by casual riders.

• Casual riders take substantially longer trips: The average casual ride was 29 minutes 33 seconds, compared with 15 minutes 14 seconds for members.

• Casual riders show stronger weekend usage: Approximately 44.6% of casual rides occurred on weekends, compared with 33.1% of member rides.

• Members have stronger weekday usage: Approximately 66.9% of member rides occurred on weekdays, suggesting more consistent use throughout the workweek.

• Rider groups have different peak times: Member usage peaked around 5 PM, while casual rider usage peaked around 2 PM.

• Station-level behavior provides additional targeting opportunities: Clark St & Elm St was the most popular starting station identified in the analysis.

Recommendations

Based on these findings, the analysis recommends a behavior-based marketing strategy rather than treating all Cyclistic riders as one customer group.

1. Target Casual Riders on Weekends

Develop membership campaigns aimed at casual riders during weekends, when their relative usage is highest. Messaging could emphasize convenience, value, and the benefits of annual membership for regular recreational riders.

2. Focus on Casual Riders Taking Longer Trips

Casual riders spend nearly twice as long on each ride as members. Cyclistic could identify casual riders with repeated or longer trips and communicate the potential value and convenience of annual membership to this group.

3. Use Timing and Location for Targeted Marketing

Marketing campaigns could be delivered during periods when casual riders are most active, particularly around the 2 PM peak, and at stations with high casual-rider activity. Potential tactics include digital promotions, QR codes, station signage, and targeted membership offers.

Tools and Skills

This project demonstrates practical data analytics skills using Google Sheets/spreadsheet software, including:

• Data cleaning and transformation

• Spreadsheet formulas and functions

• Descriptive statistics

• Pivot tables

• Data aggregation

• Behavioral comparison

• Data visualization

• Business recommendations based on data

The project follows the Ask → Prepare → Process → Analyze → Share → Act data analysis framework.

Limitations and Next Steps

The analysis identifies differences in riding behavior but cannot determine exactly why individual casual riders choose short-term passes instead of annual memberships. The dataset does not include information such as age, income, occupation, trip purpose, or individual customer conversion behavior.

Future analysis could incorporate additional information such as weather, seasonality, customer demographics in aggregated form, trip frequency, marketing exposure, and membership conversion rates. A/B testing could then be used to determine which marketing strategies are most effective at converting casual riders into annual members.

Project Deliverables

This project includes a detailed research report documenting the data preparation, analysis, findings, recommendations, and limitations, as well as a presentation highlighting the most important insights and visualizations.

Explore the full project: