This analysis is a capstone project assisgned at the completion of the Google Professional Certification in Data Analytics. The data used in this case analysis is available for manipulation and study under a provided license. For the analysis, I used SQL and R programming language in local R Studio.

Scenario:

I am a junior data analyst on the marketing analytics team at Cyclistic, a bike-share company in New York. The Marketing Director 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 Cyclistic 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 need to be supported with compelling data insights and professional data visualizations.

Analysis Summary

Between casual riders and annual members of the bike share program, there were a a fews measurable differences that were observed. By focusing on the total number of rides, ride length, and bike type for both groups of patrons, we were assume a difference in rider behavior.

Overall, members rode the bikes more frequently than casual riders, but casual riders rode the bikes for longer daily and monthly trip lengths. The weekends and the earlier months of the year were more active biking periods for casual riders, however, annual members were more actively using the bikes during the weekdays and displayed a consistent level of riding throughout the year. Across bike types, classic, docked, and electric bikes, the docked bike was the only bike type exclusively used by casual members; the other two types were used equally between casual riders and members.

We can make an educated conclusion that casual riders used the bikes recreationally and annual members used bikes for transportation to or from work in the city. With more total rides, a higher concentration of rides during the week, and a consistent rate of usage and ride length across the year, including the winter months, this could be the general behavior of a work commuter. On the other hand, casual riders partake recreationally as suggested by their lack of consistent usage during the week and colder months, and higher usage in opportune times like the weekends and warmer weather months of the year.

Recommendations for the Marketing Team:

1. Offer discounted memberships during the peak months so casual riders have an incentive to stay during the winter months.

2. Offer weekend-only memberships, or semi-annual memberships so casual riders can become members without changing their behavior.

3. Create marketing campaigns targeting bike usage during the winter at dock stations, social media, and email advertising.