📊 Customer Shopping Behavior – Data Analytics Project

📌 Overview

This project presents a complete end-to-end data analytics workflow, transforming raw customer shopping data into meaningful business insights. It demonstrates practical skills across Python-based analysis, SQL querying, business intelligence dashboarding, and executive-level reporting.

The objective is to understand customer behavior, purchasing patterns, and revenue drivers to support data-driven decision-making.

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📂 Dataset

• Source: Business / retail customer shopping dataset
• Format: CSV
• Records: ~3,900 transactions
• Features: 18 columns

Key Attributes

• Customer demographics (age, gender, location, subscription status)
• Purchase details (product, category, amount, season)
• Behavioral factors (discounts, promo codes, shipping type, review ratings, repeat purchases)

Data Quality Notes

• Minor missing values in review ratings
• Redundant discount-related columns handled during preprocessing

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🛠️ Tools & Technologies

• Python: pandas, NumPy, Matplotlib
• SQL: PostgreSQL / MySQL / SQL Server
• Business Intelligence: Power BI
• Environment: Jupyter Notebook, VS Code

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🔄 Project Workflow

1. Data Loading & Exploratory Data Analysis (Python)

• Loaded dataset using pandas
• Reviewed schema and distributions using .info() and .describe()
• Performed EDA to identify trends, outliers, and inconsistencies

2. Data Cleaning & Feature Engineering

• Handled missing values and duplicates

• Standardized column names and data formats

• Created derived features such as:

  • Age groups
  • Purchase frequency indicators

• Removed redundant columns to optimize analysis

3. SQL-Based Analysis

• Imported cleaned data into a relational database

• Wrote optimized SQL queries to:

  • Segment customers (new, returning, loyal)
  • Compare subscriber vs non-subscriber behavior
  • Identify top-performing products and categories
  • Analyze discount and shipping impact on revenue

4. Dashboard Development (Power BI)

• Designed an interactive Power BI dashboard featuring:

  • Key KPIs (total customers, revenue, average purchase value
  • Category-wise and segment-wise performance
  • Trend analysis and comparative insights
  • Built with a stakeholder-friendly, business-first design

5. Reporting & Presentation

• Compiled findings into a structured analytical report
• Focused on insights, impact, and actionable recommendations

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📈 Dashboard

The Power BI dashboard provides an interactive view of:

• Revenue and sales performance
• Customer segmentation and behavior
• Product and category insights
• Subscription and discount analysis

Designed to support quick exploration and strategic decision-making.

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💡 Key Results & Insights

• Loyal and repeat customers contribute the majority of revenue
• Subscribers show more consistent purchasing behavior
• Certain products are highly discount-driven, impacting margins
• Specific age groups and categories generate higher average spend

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📌 Business Recommendations

• Expand subscription programs to convert repeat buyers
• Optimize discount strategies for margin-sensitive products
• Focus marketing on high-value customer segments
• Promote top-rated and high-performing products

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▶️ How to Run

1. Clone the repository

   git clone https://github.com/your-username/customer-shopping-analysis.git

2. Install required Python libraries

   pip install pandas numpy matplotlib

3. Run the Jupyter Notebook for EDA and data cleaning

4. Load the cleaned dataset into PostgreSQL/MySQL/SQL Server

5. Execute SQL queries from the provided .sql file

6. Open the Power BI file to explore the dashboard

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