Case Study: How Does a Bike-Share Navigate Speedy Success?
Case Study: How Does a Bike-Share Navigate Speedy Success?
Hi there, a pleasure to welcome you as I would like to walk you through the process of how I completed my Case Study as part of my Google Data analytics course on Coursera as a Business Task, The case study is for Divvy, I Cylistic company in Chicago and to solve this business task I am going to follow the six phrases of the data analysis process (Ask, Prepare, Process, Analyze, Share, and Act).
Here is the scenario…
Case Study Scenario
I am a junior data analyst working in the marketing analyst team at Cyclistic, a bike-share company in Chicago. The director
of marketing believes the company’s future success depends on maximizing the number of annual memberships. Therefore,
my team wants to understand how casual riders and annual members use Cyclistic bikes differently. From these insights,
my team will design a new marketing strategy to convert casual riders into annual members. But first, Cyclistic executives
must approve your recommendations, so they must be backed up with compelling data insights and professional data
visualizations.
Characters and teams
● Cyclistic: A bike-share program with more than 5,800 bicycles and 600 docking stations. Cyclistic sets itself apart by offering 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. The majority of riders opt for traditional bikes; about 8% of riders use assistive options. Cyclistic users are more likely to ride for leisure, but about 30% use them to commute to work each day.
● Lily Moreno: The director of marketing and my manager. Moreno is responsible for the development of campaigns and initiatives to promote the bike-share program. These may include email, social media, and other channels.
● Cyclistic marketing analytics team: A team of data analysts who are responsible for collecting, analyzing, and reporting data that helps guide Cyclistic marketing strategy. I joined this team six months ago and have been busy learning about Cyclistic’s mission and business goals — as well as how I, as a junior data analyst, can help Cyclistic achieve them.
● Cyclistic executive team: The notoriously detail-oriented executive team will decide whether to approve the recommended marketing program.
About the company
In 2016, Cyclistic launched a successful bike-share offering. Since then, the program has grown to a fleet of 5,824 bicycles that are tracked 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 at any time. Until now, Cyclistic’s marketing strategy relied on building general awareness and appealing to broad consumer segments.
One approach that helped make these things possible was the flexibility of its pricing plans: single-ride passes, full-day passes, and annual memberships. Customers who purchase single-ride or full-day passes are referred to as casual riders. Customers who purchase annual memberships are Cyclistic members. Cyclistic’s finance analysts have concluded that annual members are much more profitable than casual riders. Although the pricing flexibility helps Cyclistic attract more customers, Moreno believes that maximizing the number of annual members will
be key to future growth. Rather than creating a marketing campaign that targets all-new customers, Moreno believes there is a very good chance to convert casual riders into members. She notes that casual riders already know the Cyclistic program and have chosen Cyclistic for their mobility needs. Moreno has set a clear goal: Design marketing strategies to convert casual riders into annual members. In order to do that, however, “the marketing analyst team needs to better understand how annual members and casual riders differ, why casual riders would buy a membership, and how digital media could affect their marketing tactics”. Moreno and her team are interested in analyzing the Cyclistic historical bike trip data to identify trends.
Cyclistic future marketing program, Task goal by Morelo:
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?
Moreno the director of Cyclist Marketing has assigned to me the first question to answer: How do annual members and casual riders use Cyclistic bikes differently? And I am expected to produce a report with the following deliverables:
1. A clear statement of the business task
2. A description of all data sources used
3. Documentation of any cleaning or manipulation of data
4. A summary of your analysis
5. Supporting visualizations and key findings
6. Your top three recommendations based on your analysis
To solve this CaseStudy Business Task as a Data Analyst I am going to follow the six phrases of the data analysis process (Ask, Prepare, Process, Analyze, Share, and Act)just like I stated above.
Ask
Here, my task is to identify the business task which is how to convert casual riders to annual members, to do this I would have to identify user ride trends.
Also, I will need to identify the key stakeholders which are the executive Team who decide the approval of my analysis report and recommendation.
Prepare
In this Phrase, my task here is to Download data and store it appropriately, Identify how it’s organized, Sort and filter the data, and determine the credibility of the data. I use Spreadsheet (Excel) to do this task and then save them appropriately for the next phase (process) in R Studio.
I will use Cyclistic’s historical trip data to analyze and identify trends. Since I have to download the previous 12 months of Cyclistic trip data to analyze annual findings. I downloaded data from April 2022 to March 2023.
Data source: The Dataset for this Case Study is actually public data and it has been made available by Motivate International Inc. under this license
Process
The key task here is Checking the data for errors, transforming the data so I can work with it effectively, then documenting the cleaning process.
I’m going to use R for preparing, Process, and analyzing then export the result for visualization in Excel, tableau, and Rstudio and use PowerPoint for Presentation.
NB: Visit my profile page on GitHub or Kaggle to read my R scripts to understand how I run them in R
Analyze
The key task here is to aggregate data so it’s useful and accessible, Organize and format data, Perform calculations, and Identify trends and relationships. I use the R console in R studio to conduct and run scripts for this analysis. I calculated the mean, max, median, and mode to analyze and showcase the trend between the ride users, and how they use divvy bikes differently. after I aggregate and am done with my analysis calculation, I exported the result in a CVS file, tho I can visualize with R but I’d always like to choose my chart for data visualization on Excel or Tableau.
NB: Please Visit my profile page on GitHub or Kaggle to read my R scripts of how I run them in R console.
I would like to share visualizations and insight to understand my conclusion insight.
Below are the charts of my Analysis using an Excel pivot table and chart…
Kindly Please use this link to view my presentation on PowerPoint link
Analysis:
- Casual riders use Cyclistic bikes to travel the same average distance as the member users, casual riders engage in longer rides, and on the max, mode, and mean ride_length analysis insight, casual riders ride more times than the annual member
- Casual riders are more likely to be converted to annual members if a direct advert can be made to them with the benefit of the annual membership they are missing for being casual riders.
- Membership riders are more active on a weekday and use Cyclistic bikes mostly unlike casual users most of the time to ride.
- Casual riders use the service more often over weekends. It leads me to conclude that membership riders use this service for their commute while casual riders use it for fun.
My conclusion is that Casual users ride mostly o the weekends while annual members mostly commute during the weekdays.
My Top 3 Recommendations:
- Run an Advertisement on Mobile App directly to Casual riders only, Ads should include the benefits of the annual membership and the advantages.
2. Utilize casual rider-generated data from the app to digitally reach them with an advertisement to become an annual member
3. Cyclistic could use digital media to influence casual riders to become members with a pop-up phone on the app, email marketing, and bulk SMS campaign.
Thanks for the time to read, I would appreciate your comment, what’s your insight feedback, and question. Please feel free to comment, share and Like.
Gratitude
Scorefield
