📊 Customer Behavior Analysis

🔍 Overview

In this project, I analyzed customer shopping behavior using data from around 3,900 transactions across different product categories. The main goal was to understand how customers shop — what they buy, how much they spend, and how factors like subscriptions or discounts affect their behavior.

The insights from this analysis can help businesses make better, data-driven decisions.

📁 Dataset Details

The dataset contains 18 features that cover different aspects of customer behavior, including:

Customer details: Age, Gender, Location, Subscription Status Purchase information: Product, Category, Purchase Amount, Season, Size, Color Behavioral data: Discount usage, Purchase frequency, Ratings, Shipping type

There were a few missing values in the Review Rating column, which I handled using the median rating for each product category.

🧹 Data Preparation (Python)

I used Python (Pandas) to clean and prepare the data before analysis:

Cleaned and structured the dataset Standardized column names for better readability Handled missing values Created new useful features: Age groups for better segmentation Purchase frequency (in days) Removed unnecessary or duplicate columns Loaded the cleaned data into a SQL database for further analysis

🗄️ Data Analysis (SQL)

Using SQL, I explored the data to answer important business questions, such as:

How revenue differs between male and female customers Which customers spend more even when using discounts Which products have the highest ratings Differences between standard and express shipping Behavior of subscribers vs non-subscribers Customer segmentation (New, Returning, Loyal) Revenue contribution by different age groups Products that rely heavily on discounts

📊 Data Visualization (Power BI)

I created an interactive Power BI dashboard to present the insights in a simple and visual way.

The dashboard includes:

Customer segmentation Revenue trends Product performance Subscription insights

💡 Key Insights

Customers with subscriptions tend to contribute more to revenue Loyal customers purchase more frequently and spend more Some products rely heavily on discounts to drive sales Age group and shipping preferences influence buying decisions

🚀 Recommendations

Based on the analysis:

Promote subscription plans with added benefits Introduce loyalty programs to retain customers Optimize discount strategies to maintain profit Focus marketing on high-value customers and top products

🛠️ Tech Stack

Python (Pandas) – Data cleaning and preparation SQL (MySQL/PostgreSQL) – Data analysis Power BI – Data visualization

📌 Conclusion

This project shows how tools like Python, SQL, and Power BI can be used together to turn raw data into useful insights, helping businesses better understand their customers and improve decision-making.