Abstract:

The city of Chicago it’s provided with a bicycle sharing service. I use public data to determine differences in the behavior of two types of users: annual members and casual riders.The results and graphs show that, even though annual members make more daily and monthly bicycle trips, casual riders take longer trips each day and there’s a difference in the type of use as well. Therefore, there are opportunities to increase profits if the marketing strategy focuses on attracting casual users to join an annual membership. Other relevant insights show that the most popular types of bikes are “classics”. Further analysis will be seen throughout the document.

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Methodology:

Using RStudio, the databases were cleaned, analyzed, and reported. The process involved storing the bike-sharing usage databases for the city of Chicago from July 2023 to June 2024. Subsequently, the databases were merged into a single dataset to address the question: How do regular and annual members of the bike-sharing service differ? After a thorough data cleaning process, graphs were created to reveal any trends worth noting. Finally, this results report was created using R Markdown.

Results:

To answer how annual members and casual riders are different in the use of the Bike Sharing Service, I used three approaches. First, using a one year sample from July 2023 to June 2024, I compared the number of rides of the two types of users:

The line graph shows similar behavior for both annual and casual members over the course of a year. The highest number of trips occurs between July and August 2023. From this point, there’s a significant decrease until it reaches the lowest number of trips during January 2024. Finally, there’s an upward trend through the rest of that year. The graph also shows that annual members use the bike sharing service significantly more than casual riders and this difference remains steady throughout our sample year.

The observed behavior could be explained by taking into account the climate in the City of Chicago. It is possible that warmer months encourage a greater use of transportation systems such as bicycles, while during colder months citizens prefer other types of transportation. The graph bellow was taken from U.S. Climate Data and shows how, on average, July uses to be the hottest month, which corresponds to the peak of shared bike uses in our line graph.

U.S. Climate Data: Chicago Climate Graph
U.S. Climate Data: Chicago Climate Graph

Now that we have a general idea of users behavior during a year, it is also useful to get the total number of rides by type of user during the week:

The bar graph shows the average use of the Bicycle Sharing Service by day of the week using the sample of one year from July 2023, to June 2024. As we determined with the monthly line graph, annual members use the service more than casual riders do and we see this trend for each day of the week. However, when we examine this last graph, we notice an important insight. Annual members use the bike-sharing service more during weekdays, while casual members use the service more during the weekend . In fact, the highest usage of bicycles by annual members occurs on Wednesdays, with an average just below 9,000 rides, while casual members use the service most on Saturdays, with an average of around 6,000 rides.

An evaluation of this data suggests that the difference in the number of rides by type of user could correspond to the type of use given to the Bicycle Sharing Service. This means that, while it is likely that annual members use the bike-sharing service as a daily means of transportation, for example to their workplaces, casual users probably use it for recreational purposes. This would explain why usage differs depending on the day of the week.

Another important aspect to characterize the two types of users is the average time they use the bike-sharing service according to the day of the week:

## `summarise()` has grouped output by 'member_casual'. You can override using the
## `.groups` argument.

The bar chart shows a revealing result: casual riders use, on average, the bike-sharing service for longer each day, compared to annual members. On one hand, annual members have a similar average usage time for each day of the week, ranging from 10 to 15 minutes per ride. On the other hand, travel time for casual riders depends on the day of the week, but it ranges between 20 and 30 minutes.

This result suggests that there are significant opportunities to increase the company’s revenue by encouraging casual users to join the annual plan.

Lastly, the data provided allows us to characterize the types of bicycles being used. This is aimed at determining the best way to invest resources according to user preferences:

## `summarise()` has grouped output by 'member_casual'. You can override using the
## `.groups` argument.

The bar chart allows us to state that the most used type of bicycles are the classic ones by both casual and annual members. The second most used category of bicycles is electric bikes, although they are used almost half as often as classic ones. Finally, there are no records of docked bikes being used by annual members during the sample year (2023-2024), and their usage by casual members is very low.

Although these figures might relate to the availability of each type of bicycle in the City and not just due to user preferences, it is important to highlight this characteristic to make informed decisions when acquiring new bicycles. For example, more classic bikes can be added if they are more economical compared to electric ones.

Conclusions and suggestions:

My conclusions include answers to three questions. First: How do annual members and casual cyclists differ in their use of the bike-sharing service? The graphs presented in the results section allowed us to conclude that annual members take more rides than casual users, both on a monthly and daily basis. Additionally, a behavior difference was found: while annual users used the service more on weekdays (Monday to Friday), casual users used the service more on weekends, which may reflect the type of use of the bike-sharing service: as a means of transportation or for recreational purposes. Another difference lies in the average ride time according to user type. Casual users have a higher average ride time each day compared to annual members, although this time varies by day of the week for casual users, while it remains almost the same every day for annual members. Finally, both annual members and casual users use classic bikes more than other types of bikes.

Second: Why would casual cyclists purchase annual memberships for the bike-sharing service?

Casual cyclists might purchase annual memberships by changing the way they use the service. While it is not possible to alter the monthly usage patterns—likely influenced by weather conditions, it is possible to encourage casual cyclists to use the service for daily transportation rather than just on weekends.Therefore, if casual users know they will use the service much more, they will prefer to save money and become annual members. We know that, on average, casual users take longer rides, so encouraging them to switch to an annual membership could lead to better future results for the company.

Third: How can the bike-sharing service use digital media to influence casual cyclists to become members?

The company could employ a marketing strategy targeted at casual users that encourages the use of bikes as a means of transportation. An environmental focus could be effective. Of course, digital media will play a significant role in this effort, with promotional content and explanatory videos that highlight the benefits of the service to these users. However, it is also necessary for bikes to be available at high-traffic work centers to ensure easy access. Final recommendations include gathering more information on the behavior and preferences for different types of bikes to better understand if the preference for classic bikes should guide and improve the distribution of available units.