Customer Behavior & Sales Analysis

End-to-End Retail Analytics using Python, SQL, and Power BI

A comprehensive retail analytics project analyzing 3,900+ customer transactions to understand shopping behavior, customer segmentation, product performance, and revenue drivers using a modern analytics stack.

Python PostgreSQL Power BI ETL SQL Customer Analytics
Sales Dashboard
Sales Dashboard An overview of key sales metrics and performance indicators.

Problem Statement

Retail businesses often struggle to understand how customer demographics, discounts, subscriptions, and product categories impact purchasing behavior. Without proper analysis, companies miss opportunities to improve customer retention, optimize discounts, and maximize revenue. This project solves that challenge through data-driven customer and sales analytics.

Approach & Methodology

  • Cleaned and preprocessed raw transactional data using Python
  • Handled missing values and performed feature engineering
  • Created customer age groups and purchase frequency metrics
  • Loaded transformed data into PostgreSQL
  • Wrote analytical SQL queries for business insights
  • Built an interactive Power BI dashboard for visualization

Concepts & Skills Applied

Exploratory Data Analysis
ETL Pipeline Development
Feature Engineering
SQL Analytics
Customer Segmentation
Data Visualization
Business Intelligence
Dashboard Development

Key Insights

  • Female customers generated higher overall revenue
  • Subscribers spent significantly more than non-subscribers
  • Express shipping correlated with higher purchase amounts
  • A few product categories drove the majority of sales
  • Loyal customers contributed the highest repeat revenue

Business Recommendations & Impact

  • Focus retention strategies on loyal customers
  • Expand subscription offerings to increase customer lifetime value
  • Optimize discount campaigns for high-value segments
  • Promote top-performing product categories
  • Encourage express shipping for premium customer segments

Advanced Features

  • End-to-end ETL workflow
  • Automated PostgreSQL integration using SQLAlchemy
  • Advanced SQL using CTEs and window functions
  • Interactive Power BI dashboard
  • Customer segmentation framework
  • Business-ready reporting

Project Workflow

  1. Data Cleaning & Preprocessing (Python)
  2. Feature Engineering
  3. Database Integration (PostgreSQL)
  4. Business Analysis using SQL
  5. Dashboard Development in Power BI

Tech Stack

Python, Pandas, NumPy, SQLAlchemy, PostgreSQL, Power BI, DAX

Business Applications

  • Customer Segmentation
  • Revenue Analysis
  • Product Performance Monitoring
  • Subscription Optimization
  • Discount Strategy Evaluation
  • Sales Forecasting Support
GITHUB REPOSITORY ← BACK TO ALL PROJECTS