Scenario

You are a junior data analyst working in the marketing analyst team at Divvy, 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, your team wants to understand how casual riders and annual members use Divvy 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.

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

The Data

The data I used for this analysis was from the 12 months from May 2022 to April 2023, publicly available at https://divvy-tripdata.s3.amazonaws.com/index.html under the license https://ride.divvybikes.com/data-license-agreement.

Methods

I used an R script in R Studio to compare all column headings and make sure they matched, to remove any trips that showed up as zero or negative in duration, and to add columns to calculate the trip duration and the day of the week each trip started on. I then imported my cleaned data into Tableau to create a dashboard.

My Dashboard

This dashboard I created in Tableau shows the differences in average ride length between members and casual users, both overall and by day of the week. 

It is also available on my Tableau Public profile page here.