Context

This is one of the case studies in the Capstone project of the Google Data Analytics Certificate.

Case Study 1: How does a bike-share navigate speedy success?

Scenario

A bike-share company, Cyclistic, is trying to increase the profits in the coming years and looking for realistic business strategies. The director of marketing believes maximizing the number of annual memberships would be the most efficient way. Therefore, the analyst team would like to understand how casual riders and annual members use Cyclistic bikes differently so that they can design a new marketing strategy to convert casual riders into annual members.

About the company

Cyclistic launched a successful bike-share offering in 2016. After five years of growing, Cyclistic now has more than 5000 bicycles that are geotracked and locked into a network of 692 stations across Chicago. The bikes can be unlocked from one station and returned to any other station in the system anytime. Apart from classic bicycles, Cyclistic also offer bikes for disabilities and electrical bikes. Most of users use the shared bikes for leisure, but 30% use them for commute.

Until now, Cyclistic’s marketing strategy relied on building general awareness and appealing to broad consumer segments. One approach that helped make these things possible was the flexibility of its pricing plans: single-ride passes, full-day passes, and annual memberships. Customers who purchase single-ride or full-day passes are referred to as casual riders. Customers who purchase annual memberships are Cyclistic members. Cyclistic’s finance analysts have concluded that annual members are much more profitable than casual riders.

Business tasks

1. Maximizing the number of members by converting casual riders into annual memberships
2. Make recommendations on how digital media could affect their marketing tactics

Content

The data are Cyclistic's historical trip data in the past 12 months (202004 to 202103). The data has been made available by Motivate International Inc. under this license (https://www.divvybikes.com/data-license-agreement). The original data was divided into several csv files based on month, and tables shown in this notebook has been joined in BigQuery before uploaded.

The union_tables includes 'ride_id', 'rideable_type', 'started_at', 'ended_at', 'start_station_name', and 'end_station_name'; the union_tables_geo includes 'start_station_name', 'end_station_name', 'start_lat', 'start_lng', 'end_lat', and 'end_lng'.

Tools for data preparation, process and cleaning

Google Sheet, BigQuery, Python (Colab link: https://colab.research.google.com/drive/1_G0bh_Anbl-i41HnssK9qDeANhFAiaON#scrollTo=lz2H9aCSyzdV)

Data Visualization

Please check up the dashboard here: https://public.tableau.com/profile/jing.ai#!/vizhome/shared_bike_202105/Dashboard1

Conclusions

Based on the trip record data from the past 12 months, 1. The number of casual users increased remarkably in the summer in a year, in the afternoon in a day, and on the weened in a week; 2. The busiest stations have more casual users; 3. The casual users tend to bike a longer time than annual members; 4. Classic bikes were replacing the docked bikes.

Recommendations

1. Make promotions on digital media in the early summer, particularly in the afternoon and on weekends to let more people know the Cythe Cyclistic bikes;
2. Mention reasonable discounts in the promotion for users who upgrade to the membership;
3. Highlight that classic bikes have been replacing the docked bikes, which may bring more flexibility and convenience for users.

Other thoughts

If time and budget are allowed, a survey would probably be good to understand what stops casual users upgrade to annual members. Price? Bike quality? Do not use bikes often? etc. Additionally, the bike record data based on each user could also be helpful to understand what annual members or casual users are in common. Then, we could do think about how to improve the business strategies based on the above analysis.

Let me know how do you think about my capstone project? (It is literally my first data analysis project.) I would be very much appreciated if any comments. Thanks! :)