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

Introduction

Cyclistic is a bike-sharing program that features more than 5,800 bicycles and 600 docking stations. Cyclistic sets itself apart by also 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 the assistive options. Cyclistic users are more likely to ride for leisure, but about 30% use them to commute to work each day. As part of an analysis project, I have obtained a data set of all Cyclistic trips taken from April 2019 to March 2020.

The goal of this case study is to explore the data and create visualizations that help us understand patterns and trends of the rider type both member and casual in the use of the service. The result from this will help to determine ways on how to encourage casual riders to become annual subscribers.

Data Sources and Methods

The dataset we are using includes information about each Cyclistic trip, including the start and end time, the starting and ending stations, and the user type (member or casual). We will use R and various packages (tidyverse, lubridate, ggplot2, etc.) to manipulate and visualize the data.

Data Exploration

To start, we will load the data set and examine its summary statistics: Descriptive analysis on ride_length (all figures in seconds)

load(file = "R markdown bikeshare.RData")

kable(total_number_of_rides) %>%
  kable_styling("striped", full_width = F) %>%
  scroll_box(width = "90%", height = "150px")

We can see that there were over 3.8 Million Cyclistic trips taken from April 2019 to March 2020.

all_trips_v2 %>% 
  mutate(weekday = wday(started_at, label = TRUE)) %>% 
  group_by(member_casual, weekday) %>% 
  summarise(number_of_rides = n()
            ,average_duration = mean(ride_length)) %>% 
  arrange(member_casual, weekday)  %>% 
  ggplot(aes(x = weekday, y = average_duration, fill = member_casual)) +
  geom_col(position = "dodge") +
  scale_fill_manual(values = c("#f9c642", "#02bfe7"))

all_trips_v2 %>% 
  mutate(weekday = wday(started_at, label = TRUE)) %>% 
  group_by(member_casual, weekday) %>% 
  summarise(number_of_rides = n()
            ,average_duration = mean(ride_length)) %>% 
  arrange(member_casual, weekday)  %>% 
  ggplot(aes(x = weekday, y = number_of_rides, fill = member_casual)) +
  geom_col(position = "dodge") +
  scale_fill_manual(values = c("#f9c642", "#02bfe7"))

This chart shows that there are more Cyclistic trips taken on weekdays than on weekends, and that member users take more trips than casual users.

kable(counts_number_of_rides) %>%
  kable_styling("striped", full_width = F) %>%
  scroll_box(width = "90%", height = "250px")

Moreover, the average ride length in seconds for casual bike riders is 3,579.64 seconds (approx 1 hour), while the average ride length for annual subscribers is 861.44 seconds (approx. 14 minutes)

Looking at the averages by day, we can see that casual riders have longer ride lengths on all days of the week compared to annual subscribers. Saturday has the longest average ride length for both casual riders and annual subscribers, at 3,331.91 seconds and 968.93 seconds, respectively.

Overall, the data suggests that casual riders tend to take longer rides compared to annual subscribers. This could be due to a variety of factors, such as casual riders being more likely to use the bikes for leisurely rides or sightseeing, while annual subscribers may be more likely to use the bikes for commuting or shorter trips while going to work. This information could be useful for Cyclistic to better understand the usage patterns of different customer segments and tailor their services and marketing efforts accordingly.

Conclusion

In this case study, we explored Cyclistic trip data and created visualizations to understand usage patterns and trends. Further analysis could be done to investigate factors that influence trip duration, station usage, and user behavior.

There are several strategies you can use to encourage casual bike riders to become annual subscribers:

1. Offer Discounts, Rewards or Referral fee: One way to encourage casual riders to subscribe annually is to offer them discounts or incentives. You could offer a reduced annual fee, or perhaps offer a free month or other bonus for signing up for a year. Or a reward system e.g. ride 5 free 1 ride.

2. Highlight the Benefits: Emphasize the benefits of an annual subscription, such as unlimited rides, priority access to bikes, and the ability to reserve bikes in advance. Explain how an annual subscription can save them money and make it more convenient to use the service.

3. Provide accessible Sign-Up: Make it easy for casual riders to sign up for an annual subscription by offering a streamlined sign-up process. This could include a mobile app or online portal that makes it easy to sign up and manage their account.

4. Increase Visibility: Increase the visibility of the annual subscription option by promoting it through social media.

5. Provide Great Service: Finally, the best way to encourage casual riders to become annual subscribers is to provide a great service. Building trust and credibility through service is also a marketing strategy through word of mouth. Offer responsive customer service and resolve any issues quickly. When riders have a positive experience with your service, they’ll be more likely to commit to an annual subscription.