I like to use public bikes in my life. Whenever I arrive in a new city, public bikes are always my first choice to travel around and get to know the city. It brings me to the places unreachable by public transport, allowing me to enjoy my adventure freely.

Naturally, how bike-sharing companies make profits and how data analysis could accelerate their growth elicited my interest.

With the rising of awareness of climate change and importance of physical health, more people prefer riding bikes instead of using private cars or public transportation. This creates an opportunity for bike-sharing companies to emerge and grow. However, these companies rely heavily on their subscribers to achieve sustainable profits. As a result, it is crucial for the companies’ growth to convert casual riders to annual members. A fictional company, Speedybike, is one of them.

Speedybike is a bike-share company in Chicago, USA. The marketing director, Lilian White, believes the company’s future success depends on maximizing the number of annual memberships. As Speedybike has tremendous users, she wants to convert casual riders to members to increase the profits rather than targeting new customers.

Even though Speedybike was founded in 2018 and has vast users now, the past marketing strategies focused on attracting new customers and thus the company had little understanding on how existing casual riders differ from annual members. Understanding their difference is the key to design a new marketing strategy to augment membership subscribers.

The open data accessed in the case study was collected by Motivate International and shared by Divvy in Chicago, USA under this license. This case study was based on historical trip data from 2021-03 to 2022-02.

Data was processed via Excel and BigQuery. Aggregated data from BigQuery was imported into Excel, which created pivot charts for visualization in this article. In addition, an interactive dashboard was also generated via Tableau.

Approximately 5.6 million records in datasets were processed. Outliers and incomplete data were identified and cleaned via the functions in Excel and Bigquery.

To be able to analyze the difference in bike usage between members and casual riders, we need to know their motivations of using the bike-sharing service. This can be divided by two purposes, either for commuting or for leisure. Theoretically, people ride bikes for commuting follow the same pattern on every workday (assuming people work on weekdays here) while people ride for leisure might have irregular records on weekdays but showing high rental frequency on holidays (typically on weekends). Therefore, their riding dates and length are crucial for this analysis.

Customers' biking frequency in seasons can also provide further insights. It's known that summer is a peak season for tourists while winter is the least favorable season. However, the need for commuting should not depend much on seasons. By analyzing the total rides of both groups of users across four seasons, there was a huge surge of biking record from casual riders in the summer of 2021, which even surpassed members' records. . However, number of rides from casual members dramatically dropped with winter approaching in the same year (Figure 1). Meanwhile, members' total rides dropped slower than casual riders. This shows seasons affect casual riders' frequency of using bikes much more than members.

Figure 1 Seasonal rides of customer

Figure 2 Seasonal average riding length of customers

Figure 3 Average weekday/weekend riding length of customers

• Casual riders used bikes for leisure while members used them for commuting.

• Casual riders rode much more often in summer than in winter.

• Member riders mostly used bikes for short-distance commuting.

To convert casual riders to members, I would like to provide the following three recommendations for Speedybike:

1. Target casual riders to promote annual memberships in summer.

2. Deploy more bike stations in where most casual riders live.

3. Advertise the benefits of riding bikes compared other transportations.