Instacart, an established online grocery store operating through an app, aims to delve deeper into its sales patterns. The primary focus of this project is to perform a thorough initial data and exploratory analysis to uncover valuable insights about customer behaviors and purchasing patterns. This analysis will aid in developing more effective customer segmentation and targeted marketing strategies.
Our task is to analyze Instacart's customer data to:
- Identify unique purchasing behaviors.
- Suggest strategies for improved customer segmentation.
- Aid in the creation of a targeted marketing strategy.
Understanding the diversity of Instacart's customer base and their buying habits is key to this project.
We were provided with comprehensive datasets encompassing all aspects of customer interactions and purchases:
- Orders: Detailed customer order history.
- Orders_Products_Prior: Prior purchasing data linked with orders.
- Products: Information on products sold.
- Customers: Customer demographic and behavior data.
- Departments: Department-wise classification of products.
The project is organized into several folders for efficient navigation and understanding:
- Project Management: Contains the project brief and other administrative documents.
- Data: This is divided into:
- Original Data: Raw datasets as provided.
- Prepared Data: Cleaned and processed data ready for analysis.
- Scripts: Includes all Python scripts used throughout the analysis process.
- Analysis: Visualizations and intermediate analytical findings.
- Sent to client: The final report compiled in an Excel format, summarizing our findings and recommendations.
Due to file size constraints, some data folders (specifically the Original Data and the Prepared Data) are not included in this repository. However, the scripts and methodologies are fully documented, allowing for reproducibility of the analysis with the original data.