This notebook analyzes user behavior patterns for Cyclistic, a bike-sharing company based in Chicago. The goal is to convert casual riders into annual members by understanding the differences in how both groups use the service.
Cyclistic has experienced rapid growth, with a diverse user base of both casual riders and annual members. The company wants to increase its number of annual members by converting casual riders. This case study focuses on uncovering insights through data analysis that can inform a data-driven marketing strategy.
- Identify key differences in usage patterns between casual riders and annual members.
- Provide actionable insights to the marketing team to support their strategy for converting casual riders into annual members.
The dataset used for this analysis includes information on bike rides, such as:
- Ride ID
- Rideable type (e.g., electric or classic bike)
- Start and end times
- Start and end locations
- Rider type (casual or annual member)
This analysis is conducted using the R programming language, with the following key libraries:
- tidyverse: For data manipulation and visualization.
- conflicted: To handle conflicts between packages.
- Data Preparation: Loading the data and cleaning it by ensuring consistent column names across datasets.
- Data Exploration: Analyzing and visualizing trends in the data to highlight differences between casual riders and annual members.
- Findings: Summarizing insights and recommendations based on the analysis.
- Clone the repository or download the notebook.
- Open the notebook in an environment that supports R (e.g., Kaggle or RStudio).
- Ensure that the necessary libraries are installed.
- Execute the notebook cells to reproduce the analysis and visualizations.
The notebook provides visual and statistical insights that help differentiate the behavior of casual riders from that of annual members. The findings can guide Cyclistic's marketing team in their efforts to grow the number of annual members.