Cyclistic Bike-Share Case Study

 

CASE STUDY

Cyclistic Bike Share 

 

- Scenario

Cyclistic is a fictional bike share company from Chicago. Lily Moreno is the Director of marketing who believes that the company's future success resides on the company's abilities to maximize the number of annual memberships. Your team of Data analysts want to understand how casual riders and annual riders 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.

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. 

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.

 

-APPROACH-

1- Ask

Moreno has assigned you the first question to answer: How do annual members and casual riders use Cyclistic bikes differently?

At this point we know what management and stakeholders need. What the data analyst should do is deliver a clear statement of the business task. 

 

2- Prepare

The dataset provided for this task will downloaded from https://divvy-tripdata.s3.amazonaws.com/index.html

This is a public dataset offered by a motivated international, Inc. The issues of privacy, security, and licensing are addressed ahead of the study. The dataset is securely downloaded and stored safely. The excel files are filtered and cleared. The Reliability, Originality, comprehensiveness, time stamp and the citation is well established. 

 

3- Process

All .CSV files are uploaded to the Excel and the data is ready for cleaning and transformation. The tool of choice in this case is Microsoft excel for data manipulation. First, we unzip all the files. Next step is to check for errors and nulls. Remove rows we don't need such as: ride_id. The columns started _at and ended_at are duplicated in order to separate the time and the date. In both rows, the date is formatted as HH:MM:SS and date is formatted as MM/DD/YYYY. New column is created with the title of "trip_duration_minutes" using the formula

= Table.RenameColumns(#"Removed Columns",{{"tip_abs", "trip_duration_minutes"}, {"ended_hour", "end_your"}, {"ended_date", "end_date"}, {"started_hour", "start_hour"}, {"started_date", "start_date"}})

 

Created a column called “day_of_week,” and calculated the day of the week that each ride started using the “WEEKDAY” command (for example, =WEEKDAY(C2,1)) in each file. Format as General or as a number with no decimals, noting that 1 = Sunday and 7 = Saturday.

 

4- Analyze

In order to understand the data, some calculations and new columns are needed. 

Column mean of "ride_length" = AVG (total _ride length/total ride). Max ride_length= longest  ride. The mode day of the week is the commonly reoccurring value in a dataset.

During the year period, we notice that the MAX ride length by a casual member is 2,924,156 minutes compared to 3,145,444 minutes of use by an annual member. During that same period, the MIN ride length for a casual member is 65,033; compared to 26,439 minutes by an annual member.  

 

This graph shows the numbers and length of rides by both casual and annual members.

Users with annual membership to more rides that those who have a casual membership.  

 

 

Overall the data show also that both annual members and casual members preferred the electric bike over the classic bike. Furthermore, it is very important to take into consideration the number of docked bikes. More bikes on the dock represent a significant loss in revenue for the company.

 

The Third and Fourth quarter were the busiest amongst both types of riders. 

Nevertheless, annual members still had the highest number of rides regardless of quarter.

 

 

5- Share

Considering our previous analysis, the data shows that the annual members took more trips and for a longer duration than casual members. However, casual members took more trips than annual members on the weekends. We also notice the preference in the electric bike compared to the classic bikes. Nevertheless, we have seen the staggering amount of docked bike at any given time of the year.

 

Recommendations

We have seen how casual members and annual members of Cyclistic user bike differently. Because the first quarter of the year is very slow in service for both casual and annual members, Cyclistic could offer promotional discounts and coupons in order to encourage casual members to upgrade to annual members.

The number of docked bikes is a loss of revenue to Cyclistic. A collection of data must be done to identify those starting points that are most used and increase the number of bikes there. Furthermore, studying the routes of those users will be very beneficial to Cyclistic in the future. 

 

 

 

CASE STUDY

by Sinclair Allen

11/04/2022