This case study is part of my Google Data Analytics certification. Using Cyclistic, a fictional bike-share company in Chicago, I explore user behaviour to identify trends and provide data-driven recommendations to support the company’s growth.
The study follows a structured six-phase data analysis process:
Business Task: Defining the problem and understanding the goal.
Data Preparation: Cleaning and transforming raw data for analysis.
Processing Data: Calculating metrics and generating results.
Analysing Data: Interpreting patterns and trends.
Sharing Results: Creating visualizations to communicate findings.
Acting on Insights: Data-Driven Recommendations.
The goal is to analyse the behaviour of bicycle usage between casual users and annual members to identify key differences. This will enable the design of marketing strategies aimed at converting casual users into annual members, driving Cyclistic's growth and profitability. Using historical bike usage data, we aim to identify behavioural patterns and key differences between casual and annual users to inform targeted marketing campaigns.
For this analysis, we will use data from Motivate International Inc., provided under the necessary licence. The dataset covers a full year of bike usage in 2023, including the start and end times of each ride, as well as the day of the week and user types. The raw data is first cleaned and transformed to ensure consistency, remove duplicates, and address any missing values. Further details about the cleaning process will be provided in the next phase – Processing Data.
This stage is crucial to ensure the data is accurate and structured appropriately for the analysis, enabling us to extract meaningful insights and trends that will inform Cyclistic’s marketing strategy.