Cyclistic Case Study
I was presented with the opportunity to complete a case study. The company surrounding the case study is called Cyclistic. The company wants to take a look into bike ridership and try and convert casual riders into annual memberships. The question I was tasked to answer was: how do casual riders differ from membership riders?
First, I would like to start off by identifying the key stakeholders of this project. Those include: the Cyclistic executive team; Lily Moreno (the director of marketing and manager); Cyclistic analyst team.
I created a folder entitled Divvy Data was stored on my local hard drive and it consists of 12 files of divvy ridership information for each month of 2022
The next part of the process involved gathering data. The data provided came from Divvy, a Chicago based bike sharing company. The files consisted of ridership data from January 2022 through December 2022.
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To start the cleaning process, I wanted to figure out the following measures:
1.) The average ride length between members and casual riders per day
and
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2.) The total numbers of rides per season between members and casual riders.
The next thing for me to do is pick which tools I need to further process and analyze the data. Those tools included Google Sheets, Big Query for SQL, and R Studio. I initially wanted to use SQL to process and clean data, however it was a lot of data to download into Big Query. I instead opted to use R.
Most data manipulation took place in R and I was able to follow along with 2 separate R notebooks: Notebook 1 and Notebook 2.
Here you can find the complete script that I put together to process the gathered data. It combines aspects of both Notebook 1 and Notebook 2. Along the way there were some challenges that I encountered that pushed me to use two notebooks instead of the one that was provided within the case study instructions.
After completing the analysis process, I was able to create the following visuals.
This visual shows total ridership between membership riders and casual riders by season. We can see here that during the Winter and Fall months members take more rides. Where as casual ridership is higher during the Spring and Summer months.
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This visual demonstrates the average ride time on each day between casual riders and members in 2022 and the total number of rides per season between members and casual riders
We can see that casual riders take, on average, at least 2x longer rides than members. Members also take more rides throughout the year compared to Casual riders. Casual ridership during the winter months is extremely low at 3% ridership.
This particular visual was created in Tableau. Here you can find the interactive dashboard that was developed to demonstrate this alongside the daily average ride time between members and casual riders also shown below.
Final Thoughts
After gathering, processing, analyzing, and visualizing Cyclistic's data and getting some insights, here are a couple of suggestions that I would like to share.
1.) Present casual riders the opportunity to participate in a survey after they complete their ride. This can allow to further investigate and guide us to figure out what would convert them to become members.
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2.) Another suggestion that I can give would be to convert casual riders into members by providing them with a limited time offer(s) of some sort for casual riders. For example: they can take the cost of the last ride they took and apply it towards a membership