Google-Capstone-Project

Case Study: How Does a Bike-Share Navigate Speedy Success?

Introduction:

Cyclistic, it is a bike-share company in Chicago. 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.

Case Study:

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.

The director of marketing believes the company’s future success depends on maximizing the number of annual memberships. Therefore, your team wants to understand how casual riders and annual members use Cyclistic bikes differently. From these insights, your 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.

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, Lily Moreno: The director of marketing and your manager, 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 are already aware of the Cyclistic program and have chosen Cyclistic for their mobility needs.

Problem Statement:

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?

Case Study Roadmap:

Ask

● What is your final conclusion based on your analysis?
● How could your team and business apply your insights?
● What next steps would you or your stakeholders take based on your findings?
● Is there additional data you could use to expand on your findings?

Prepare

● Where is your data located?
● How is the data organized?
● Are there issues with bias or credibility in this data? Does your data ROCCC?
● How are you addressing licensing, privacy, security, and accessibility?
● How did you verify the data’s integrity?
● How does it help you answer your question?
● Are there any problems with the data?

Process

● What tools are you choosing and why?
● Have you ensured your data’s integrity?
● What steps have you taken to ensure that your data is clean?
● How can you verify that your data is clean and ready to analyze?
● Have you documented your cleaning process so you can review and share those results?

Analyze

● How should you organize your data to perform analysis on it?
● Has your data been properly formatted?
● What surprises did you discover in the data?
● What trends or relationships did you find in the data?
● How will these insights help answer your business questions?

Share

● Were you able to answer the question of how annual members and casual riders use Cyclistic bikes differently?
● What story does your data tell? ● How do your findings relate to your original question?
● Who is your audience? What is the best way to communicate with them?
● Can data visualization help you share your findings?
● Is your presentation accessible to your audience?

Act

● What is your final conclusion based on your analysis?
● How could your team and business apply your insights?
● What next steps would you or your stakeholders take based on your findings?
● Is there additional data you could use to expand on your findings?

Stakeholders:

● Cyclists
● Lily Moreno: The director of marketing
● Cyclistic marketing analytics team
● Cyclistic executive team
● Cyclists Riders and Customers

Dataset:

Cyclists user data is available from the month of January 2020 to June 2022. The dataset in consideration here is of the entire year of 2021. Each month dataset is compressed in a zip file and is avaialable in .csv format.The link to the dataset is available click here. This data has been made publicly available via license by Motivate International Inc. and the city of Chicago. All user’s personal data has been scrubbed for privacy.

Outcome:

● The warmer months of Chicago (Jun-Aug), experience an spike in the Casual users. The number of casual user also exceed the yearly members.click here.
● The days of the week also further shows that causal riders prefer to use the service during the weekends as their usage peaked then. The long term members conversly utilised the service more-so throughout the typical work week i.e (Monday- friday)click here.
● Moreover, the Casual demographic spent on average a lot longer time per ride than their counter part of year members. click here.
● Long term riders tended to stick more so to classic bikes as opposed to the docked or electric bikes. click here.

Inputs:

● Introducing plans thats may be more appealing to casuals for the summer months. This marketing should be done during the winter months in preperation.
● The casual users might be more interested in a memebrship option that allows for per-use balance card. Alternatively, the existing payment structure may be altered in order to make single-use more costly to the casual riders as well as lowering the long-term membership rate.
● Membership rates specifically for the warmer months as well as for those who only ride on the weekends would assist in targeting the casual riders more specifically.

Missing Data to Improve Analysis:

● Age and gender: This would add a dynamic to whether or not customers are being targeted across demograpic lines. Is the existing marketing effective? Is there potential for more inclusive targeting?
● Pricing structure: THe actual pricing plans data was not provided and would give further insight to which plans are the most popular and by (how much) when comparing them. It would also be effective to understanding the spending behaviour of casual user.
● Household income data: Pinpointing the average income of the long-term memebrs as compared to the casual counter-parts would allow for further analysis of what is the typical economic standing of each type of member, as well as providing the ability to analysis overall price sensitivity between the two different membership types.

Thank You!