Google Data Analytics Capstone 1

Case Study 1: Cyclistic Bike-Share

This is my capstone project for the Google Data Analytics Certificate program in Coursera. This scenario is about a fictional bike-share company.

Scenario: You are a junior data analyst working in the marketing analyst team at Cyclistic, a bike-share company in Chicago. The director of marketing 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 they must be backed up with compelling data insights and professional data visualizations.

Ask

Lily Moreno, the director of marketing, set a clear goal: Design marketing strategies aimed at converting casual riders into annual members. In order to do that, the marketing team needed to understand how annual members and casual riders differ. This study aims to analyze the historical bike trip data to identify trends which will guide how the marketing strategy will be developed.

Prepare

Data were downloaded from here . The data used were from Dec 2021 until Nov 2022. It was a public dataset provided by Motivate International Inc.

Process

The csv files were converted to Excel files to make the cleaning process easier. Starting and ending station ID columns were removed. Filters were applied to check if there were existing starting and ending latitude and longitude to indicate if there were starting and ending stations. If none, rows were removed. Also, trip durations with less than 3 seconds (2 and below) were removed. Pivot tables were created for initial analysis. The files were then exported to R Studio for further processing and analysis.

Analysis

In R Studio, the 12 files were combined to a single dataframe. An additional column ride_length was also created to calculate the trip duration. The average duration and number of rides were computed. A summary about the trip duration was visualized using ggplot2. The modified R code used for analysis and the computed summary in csv format can be found in Github.

Here were the graphs extracted from the analysis stage.

Act

Key findings:

Casual riders took longer trips than annual members.
Casual riders took longer trips during weekends while annual members' trip were consistent during the week.
Number of rides between casual riders and annual members were almost the same during weekends, but had a big discrepancy during the weekdays.

Recommendations:

Company can introduce weekend only and weekday only type memberships with different pricing.
Company can introduce seasonal type membership or discount. Number of riders decreased during the winter month.
Company can introduce discounts or perks to members for long rides. This may entice casual riders who take longer rides to sign up for membership.

Page updated

Report abuse