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https://github.com/zoeyportfolio/cyclistic-bike-share-project

I conducted an analysis for Cyclistic, a Chicago-based bike-share company, to support their goal of increasing annual memberships. My role involved analyzing the different usage patterns between casual riders and annual members and designing targeted marketing strategies based on the findings.
https://github.com/zoeyportfolio/cyclistic-bike-share-project

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I conducted an analysis for Cyclistic, a Chicago-based bike-share company, to support their goal of increasing annual memberships. My role involved analyzing the different usage patterns between casual riders and annual members and designing targeted marketing strategies based on the findings.

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# Cyclistic-Bike-Share-Project

I conducted an analysis for Cyclistic, a Chicago-based bike-share company, to support their goal of increasing annual memberships. My role involved analyzing the different usage patterns between casual riders and annual members and designing targeted marketing strategies based on the findings.



The dataset, provided by Lyft Bikes and Scooters, LLC ("Bikeshare") through the Divvy system, and owned by the City of Chicago, was extensive. 



### I utilized Microsoft SQL to clean and transform the data, performing tasks such as:

1. converting DateTime values to character months

2. extracting and rounding time portions

3. converting times to numerical representations

4. calculating the length of rides

5. combining monthly datasets to ensure a comprehensive analysis.

   

### Using Tableau, I visualized the data and uncovered key insights:

1. Bike Preferences: Members prefer classic bikes, while casual users favor electric bikes.

2. Seasonal Usage: Both groups spend the most time riding in May, with casual users riding more frequently than members during this month.

3. Weekly Trends: Members ride most during mid-week, whereas casual users prefer weekends.

4. Peak Times: The ride length for both groups peaks at 5 PM and decreases sharply overnight. Members show a secondary peak at 8 AM, while casual users' ride length steadily increases until 5 PM. Members' activity starts at 4 AM, an hour earlier than casual users.

5. January Abnormality: Casual users exhibit unique patterns in January, with more weekday activity and a slight increase in ride length between 1 and 2 AM.



Tableau Public link: https://public.tableau.com/views/Cyclistic_1to5/Dashboard1?:language=en-US&:sid=&:redirect=auth&:display_count=n&:origin=viz_share_link

   

### Based on these insights, I developed several marketing strategies:

1. Targeted Promotions: Target casual users with personalized emails, ads, and app notifications that emphasize membership benefits during the one-hour window before and after peak times, specifically around 5 PM on weekends in February, March, April, and May. Shift the budget focus to weekdays in January instead.

2. Weekend Membership Plan: Introduce a weekend membership plan with discounts applicable between 5 AM and 5 PM from February to May. This plan targets casual users who prefer weekend rides but could benefit from the flexibility and savings of a membership.

3. Exclusive Member Benefits: Promote exclusive benefits for members, such as unlimited electric bike rides, priority access to electric bikes, and discounts on extended rides.