Customer Shopping Behavior

Customer Shopping Behavior Analysis using Python and Power BI to Optimize Revenue, Customer Segmentation, and Inventory Planning

Project Overview

Step-by-step sales data transformation—from raw CSV files to interactive Power BI dashboards

📄

CSV sales files

🧹

Data cleaning using Power Query

🔄

Create meaningful and insightful columns

📥

Load data into Power BI and create DAX measures for insightful visuals

📊

Showcasing KPIs and insights aligned with business needs

Power BI

Python

CSV

PostgreSQL

SQL Alchemy

Matplotlib

Pandas

Strong overall performance:Total sales reached $1.20M, indicating healthy demand across outlets.
Supermarket Type 1 dominates:It contributes the highest sales (~$787K) and item volume, making it the most profitable outlet type.
Top-performing categories:Fruits & Vegetables and Snack Foods are the highest revenue–generating item types (≈ $0.18M each).
Medium-sized outlets lead sales:Medium outlets generate the highest revenue (~$508K), outperforming small and large formats.
Tier 3 locations perform best:Tier 3 outlets record the highest sales (472K), showing strong demand beyond metro areas.
Consistent customer satisfaction:Average rating remains stable at 4.0 across all outlet types.
Low-fat products outperform:Low-fat items contribute more sales than regular items, reflecting a shift toward healthier choices.

$498528

60.1

★★★⯪☆

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