Provide invenstors with data visualizaions, using Tableau, that proves that a bicycle ride sharing business in Des Moines, Iowa is a solid business proposal. Existing data from a similar business, the Citi Bike program in New York City, will be utilized to understand how the bike-sharing business actually works. In particular, this analysis will focus on how bike trips (quanity and length) relate to other factors.

Click here to see a live view of the Dashboard.

Below are figures of the visualizations created, followed by a description of what information can be gleaned from each visualizaion.

Figure 1 (Demographics Dashboard)

• Customer Breakdown
  • Over 75% of the bike trips are taken by subscribers instead of single pay customers.
• Gender Breakdown
  • The majority of the bike trip participants are male.

Figure 2 (Trip Duration by Age)

• Average Trip Duration
  • As individuals get younger they consistently take longer trips.

Figure 3 (Checkout Times Dashboard)

• Checkout Times for Users
  • Most bike trips last less than an hour.
  • The most popular length for bike trips is between 5 and 10 minutes.
• Checkout Times by Gender
  • There is little to no difference in how long each gender likes to ride a bike.

Figure 4 (Trips by Weekday per Hour Dashboard)

• Trips by Weekday per Hour
  • During the work week, the most popular times to ride a bike are in the morning (6AM-9AM) and in the afternoon (4PM-8PM)
  • Otuside of the work week, bike rides are active throughout all daylight hours of the day (7AM-8PM) with Saturday being more popular than Sunday.
• Trips by Gender (Weekday per Hour)
  • The distribution of bike rides between male and female is very similar, indicating that there is no preference difference between when either gender rides a bike.

Figure 5 (User Trips by Gender by Weekday)

• User Trips by Gender by Weekday
  • There is little to no preference for single pay customers on when a bike ride is taken or what gender takes a bike ride.
  • For subscribers, Thursday and Friday are the most popular days to ride a bike.

Based on the data anlyzed the following recommendations can be made:

• The target audience should largely be young male riders.
• The business model should focus on growing subscribers as they ride more often.
• Logistics (availability of bikes, repair frequency, etc.) should revolve around bikes being ridden for less than an hour.
• The business should capitalize on demand outside of typical work hours during the workweek and throughout daylight hours outside of the work week.

In order to make a better informed decision, the following visualizations are suggested for future analysis:

• Map visualization showing where different genders or user types (shown by color) tend to ride bikes and for how long (shown by marker size).
• Line or area visualization showing number of bike rides by birthyear.