Cyclistic bike-share Data Analysis

Background on this project and the company

This project was part of my Google Data Analysis course and I worked as a junior data analyst on the marketing analyst team at Cyclistic, a fictional bike-share company in Chicago. The company is looking to understand how they can maximize the number of annual memberships. To do that, I needed to analyze how casual riders and riders with annual memberships are using the Cyclistic bikes differently.

In 2016, Cyclistic launched a successful bike-share offering. Since then, the program has grown to a fleet of 5,824 bicycles that are geotracked and locked into a network of 692 stations across Chicago. The bikes can be unlocked from one station and returned to any other station in the system anytime.

This use case presented a unique opportunity for me to analyze different types of data and further hone some of my newest skills in data analysis and the R programming language. Work on this project followed my typical data analysis process which includes the following steps: Ask, Prepare, Process, Analyze, Share, Act, and Reflect. You can see more about my approach on analysis here. Continue reading to see the details of what I learned in each step of the process during this project and the skills and thought process I deployed.

Ask

• Three questions are asked of the team for this project:
  1. How do annual members and casual riders use Cyclistic bikes differently?
  2. Why would casual riders buy Cyclistic annual memberships?
  3. How can Cyclistic use digital media to influence casual riders to become members?
• I have a great team available to help me in this project. In addition to the market analyst team, Moreno, the director of marketing has given great clarity and support on what is needed to achieve success.
• The Business Task: I will be focusing on the first question and will be looking at how different categories of riders use the bikes differently. Understanding this data will help inform the rest of the team on the additional questions.

Prepare

• The timeline for this project is pretty quick and should be able to be accomplished in just a few days.
• The data necessary for this work is available in a public dataset which can be found here.
• I’ll begin by downloading the data for the last 12 months and prepare it for analysis.
• The initial analysis will include:
  1. How is the data organized?
  2. Sort and filter the data
  3. Determine the credibility of the data
• Data Sources Used: All data sources used for this project were provided by the Cyclystic team and is publicly available for download through the link above.

Process

• Now that I’ve downloaded the data and done the initial preparatory analysis, I need to process the data to get from raw to clean.
• I will be using R Studio for the primary work on this project, with Excel for some of the initial analysis and data verification.
• My entire cleaning, filtering, transforming, and bias checking of the data will be done within R so that I can maintain a record of the work, document any cleaning of the data, as well as to make the process repeatable.
• Data Cleaning and Manipulation: See the R Markdown file for the specifics and the code used, but there were four primary steps:
  1. Normalize the fields in the 2019 and 2020 datasets so that the data could be aggregated.
  2. Calculate the length of time for each ride segment.
  3. Calculate the date parts of the date fields for easier summary.
  4. Remove data with negative ride times and quality control records.

Analyze

• We’ve asked questions, and also prepared and processed the data, now we need to analyze the data to be able to make recommendations.
• Summary of Analysis:
  • Casual riders take longer rides on average than members
  • Casual riders take fewer rides on average than members
  • Casual riders take 50% more long rides than members

Share

• Let’s take a look at a few of the key findings

• A visual of the average lengths of rides for casual riders compared to member riders

• Casual riders take longer rides on average

• A visual of the number of rides for casual riders compared to member riders

• Casual riders take fewer rides on average

• A visual of the number of rides that are longer than one day, per rider type

• Casual riders take 50% more long rides than members

Act

• Recommendations
  1. On average, Casual Riders ride longer. Since casual riders pay by the length of the ride, if we market to the casual riders that a membership will let them ride more frequently at a better price per ride, it could attract more members.
  2. Casual Riders take fewer rides. If we market to casual riders that the membership option pays for itself with as few as x number of rides, it could interest them in a membership rather than paying for individual rides.
  3. Casual Riders take more rides that are longer than one day than members, paying for multiple day passes for a single ride. If we position membership prices the right way to casual riders, they’ll see the benefit of membership over paying for the multiple day passes for a single ride.

Reflect

• With the project complete, we’re now at the phase of reflection.
• What have I learned? What can I take away from this project to use in the future?
• This was my first case study to complete as part of my Data Analysis course. I was able to synthesize a lot information and use the skills I learned for this compact project.
• Using my Data Analysis Checklist from the previous course helped me to align a plan for what steps needed to happen.
• I can repeat this process, with a template now and continue to grow my skill set!
• I also learned a bit about the limitations of free online tools like RStudio with regards to how much storage can be used.
• Additionally, I was able to learn more about using R programming to accomplish data visualization.
• Lastly, I was reminded the importance of managing time and preparing for the unplanned so that when tools don’t work as expected, there is time remaining to complete the work.