Considering the business task and the question I have been assigned to answer, there are three main takeaway points from this analysis:
Casual riders are more in weekends than weekdays while members are more in weekdays than weekends.
Casual riders use bike-share for longer durations than members.
Casual riders show inconsistency in their ride duration, where as members tend to be consistent all day throughout the week
When we consider the different bike types (classic, docked or electric) in our analysis a few more insights are achieved:
Only casual riders use docked bikes. They use them for very long duration than other two bikes.
Casual riders use both classic and electric bikes almost equally while members preffer classic bikes more than electric bikes.
For any bike type casual riders take longer duration than members on any given day.
The inconsistency in ride duration of casual riders is probably due to the inconsistency in using docked bikes.
Docked bikes are probably used for long didtance rides since they have the longest ride duration. Hence members seems to go for short comutes only.
If more data was made available (like ride location, working or student, etc) a broader study coul have been done. For example, ride location data could help us understand at what location should we concentrate our marketing strategies on and working or student data could help us to whom to approch more.
With the insights gained from this analysis, i have reached certain conclusions or recomendations which could help Cylistic attract more casual riders for their annual membership.
Annual membership promotions and registrations should be made available during weekends since that is when casual riders show up the most.
Offers or promotions for long rides could attract more casual riders towards annual memberships since they mostly ride longer than current members.
A survey should be conducted to understand the rider's perspective of the program. Like what do they miss, what is their most liked feature, what should improve, etc. It is always good to consider the users input as well.
It is a good option to study docked bikes since they are used the longest and only by casual riders but at the same time they have very less rides. Insights from this study could help attract more casual riders since they are the only ones who use it.
Any feedback regarding the project is gladly appreciated. Every suggetions helps me develop my analytic skills and get higher in my data science environment. Feel free to email me with subject 'Capstone' for any feedback or suggessions or new project ideas. Thank you for your valuable time.
Email: jithin.george14796@gmail.com