Instacart Customer Analysis

• View Github Repo
• View Tableau Viz

Objective

Gain insights into the purchasing patterns of Instacart users, including frequency and items bought.

Dataset Summary

• Aisles and Departments : There are 134 unique aisles and 21 unique departments, categorizing the products.
• Orders and Users : A total of 3.4M orders have been placed by ~200K users, indicating substantial user engagement.
• Products: The dataset includes ~50k products categorized by aisle and department.
• User Information: Additional user data includes order day, order hour, days since last order, and reorder flags.

Process

The process included uploading multiple files into Python, forming a dictionary of dataframes, merging them into a unified dataset. These dataframes were then imported into a SQL server for querying and exploring insights. Extracted valuable information was exported and loaded into Tableau for visualization purposes.

Key Insights

• Procude and Dairy eggs are clear winner in terms of orders and reorder ration within departments.
• Fresh fruits and vegetables have very high reorders than the packaged ones.
• Saturday afternoons and Sunday mornings are prime time for orders.
• Reordering Pattern: Most of the reorders happen on weekly or monthly basis i.e. 7th, 14th, 21st and 30th day of order. We can clearly see spikes in reorders on these days.
• Coke vs Pepsi : In the battle of Coke vs Pepsi, Coke is the clear winner.