📊 Customer Behavior Data Analytics Project

🔎 Overview

This project focuses on analyzing customer behavior using Python, SQL, and Power BI. The goal is to extract meaningful insights from raw data, identify trends, and present business-friendly results through dashboards and reports.

The project demonstrates end-to-end data analytics skills including data cleaning, exploration, SQL querying, visualization, and reporting.

📁 Dataset

The dataset contains customer-related information such as:

Customer ID

Age / Age Group

Previous Purchases

Purchase Amount

Subscription Status

Discount Applied

Product Category

The data is used to analyze customer segmentation, revenue contribution, repeat buying behavior, and subscription patterns.

🛠 Tools & Technologies Used

Python (Pandas, NumPy, Matplotlib, Seaborn)

SQL (PostgreSQL / MySQL / SQL Server)

Power BI (Dashboard creation & visualization)

Gamma (Presentation creation)

Git & GitHub (Version control)

🔄 Project Steps

1️⃣ Data Loading (Python)

Imported dataset using Pandas

Inspected structure and data types

2️⃣ Data Cleaning

Handled missing values

Removed duplicates

Converted data types

Created calculated columns (e.g., customer segments)

3️⃣ Exploratory Data Analysis (EDA)

Analyzed customer purchase patterns

Identified revenue distribution by age group

Checked subscription trends

Visualized key insights using charts

4️⃣ SQL Analysis

Wrote aggregation queries

Used GROUP BY, CASE, CTEs

Calculated revenue contribution

Segmented customers (New, Returning, Loyal)

Analyzed repeat buyer behavior

5️⃣ Power BI Dashboard

Connected SQL Server database

Built interactive dashboard

Created KPIs and summary visuals

Designed business-friendly reports

6️⃣ Reporting & Presentation

Created structured analytics report

Built presentation slides using Gamma

Highlighted business recommendations

📊 Dashboard Highlights

The Power BI dashboard includes:

Total Revenue KPI

Revenue by Age Group

Customer Segmentation Distribution

Subscription vs Non-Subscription Analysis

Repeat Buyer Insights

📈 Key Results & Insights

Identified top revenue-generating age group

Segmented customers into New, Returning, and Loyal

Analyzed discount impact on purchases

Observed trends in subscription behavior

Provided data-driven recommendations for customer retention

▶️ How to Run the Project

🔹 Python Analysis

Install required libraries:

pip install pandas numpy matplotlib seaborn

Run the Jupyter Notebook or Python script.

🔹 SQL Queries

Import dataset into PostgreSQL / MySQL / SQL Server.

Run the provided SQL scripts in your database environment.

🔹 Power BI

Open Power BI Desktop.

Connect to SQL Server database.

Load tables and refresh visuals.

🎯 Project Objective

To demonstrate practical data analytics skills by working on a real-world customer behavior dataset and presenting insights in a professional format suitable for business decision-making.

👩‍💻 Author

Khushnuma Ansari Aspiring Data Analyst | SQL | Python | Power BI

I# Customer_Behavior_Analysis Data analytics project showcasing customer behavior analysis using python, sql and power BI