Welcome to the Cyclistic bike-share analysis case study! In this project, we analyze the bike-share data from Cyclistic, a fictional bike-share company in Chicago. As a junior data analyst on the marketing analytics team, our goal is to understand how annual members and casual riders use Cyclistic bikes differently. This analysis will inform our marketing strategy to convert casual riders into annual members.
Cyclistic is a bike-share program in Chicago offering over 5,800 bicycles and 600 docking stations. The company provides traditional bikes as well as reclining bikes, hand tricycles, and cargo bikes, making bike-share more inclusive to people with disabilities and riders who can't use a standard two-wheeled bike. Our users primarily ride for leisure, but about 30% use the bikes to commute to work each day.
Lily Moreno: Director of Marketing and our manager, responsible for developing campaigns and initiatives to promote the bike-share program. Cyclistic Marketing Analytics Team: A team of data analysts responsible for collecting, analyzing, and reporting data to guide Cyclistic's marketing strategy. Cyclistic Executive Team: The executive team that will decide whether to approve the recommended marketing program. About the Company Cyclistic launched its bike-share offering in 2016, growing to a fleet of 5,824 bicycles across 692 stations in Chicago. The bikes can be unlocked from one station and returned to any other station in the system anytime. Cyclistic's pricing plans include single-ride passes, full-day passes, and annual memberships. Annual members are much more profitable than casual riders, making them a key focus for future growth.
Our task is to answer the following questions:
- How do annual members and casual riders use Cyclistic bikes differently?
- Why would casual riders buy Cyclistic annual memberships?
- How can Cyclistic use digital media to influence casual riders to become members?
- We'll start by analyzing how annual members and casual riders use Cyclistic bikes differently, aiming to produce a report with clear insights and recommendations based on our analysis.
- Clear Statement of Business Task: Identifying the key question we aim to answer.
- Data Sources: Description of the data used for analysis.
- Data Cleaning and Manipulation: Documentation of any cleaning or manipulation of data performed.
- Analysis Summary: A summary of our analysis findings.
- Supporting Visualizations and Key Findings: Visualizations and key insights derived from our analysis.
- Recommendations: Our top three recommendations based on the analysis.
Attached to this repository is an Exploratory Data Analysis (EDA) document that provides an in-depth explanation of all the analysis conducted on the dataset. The EDA includes detailed insights, visualizations, and interpretations of the data, offering a comprehensive overview of the dataset.
Please note that I do not own the dataset and all credit for the data goes to its respective owners:Motivate International.
If you have any questions or feedback regarding this analysis, feel free to reach out. Your insights and suggestions are valuable for refining our approach and improving our results.
Notes datasets can be acessed from Divvy_Trips_2019_Q1.zip and Divvy_Trips_2020_Q1.zip