🛒 Customer Behaviour Analysis

This is an end-to-end data analytics project that analyzes customer purchasing behaviour to uncover insights about revenue trends, customer segments, and buying patterns.

📊 Dashboard Preview

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💡 Key Business Insights

• Revenue Drivers: Male customers contribute significantly to the total revenue, with specific interest in the Clothing and Accessories categories.
• Seasonal Trends: Significant peaks in purchasing frequency were observed during the Fall season, suggesting a need for increased inventory during this period.
• Customer Loyalty: Approximately 15% of the customer base is categorized as "Loyal" (over 10 previous purchases), yet they account for a disproportionate amount of total sales.
• Discount Impact: Use of promo codes resulted in a higher average transaction value compared to non-promo transactions.

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🛠️ Tools & Technologies

Tool	Purpose
Python	Data loading, Cleaning, and EDA (Pandas, Seaborn)
PostgreSQL	Structured data storage and complex business queries
Power BI	Building interactive visual reports and KPIs
Jupyter	Development environment for data exploration

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🗄️ SQL Analysis Highlights

I used PostgreSQL to answer specific business questions. Here is an example of the segmentation logic used:

-- Customer segmentation by purchase history
SELECT customer_segment, COUNT(*) AS total_customers
FROM (
    SELECT customer_id,
    CASE 
        WHEN previous_purchases = 1 THEN 'New'
        WHEN previous_purchases BETWEEN 2 AND 10 THEN 'Returning'
        ELSE 'Loyal'
    END AS customer_segment
    FROM customer
) t
GROUP BY customer_segment;

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🚀 How to Run

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1. Clone the repository:

   git clone [https://github.com/bsanghvi78/Customer-Behaviour-Analysis.git](https://github.com/bsanghvi78/Customer-Behaviour-Analysis.git)

2. Install Dependencies: Make sure you have Python installed, then run: Bash pip install pandas matplotlib seaborn
3. Run the Analysis: Open Customer_shopping_behaviour.ipynb in Jupyter Notebook or VS Code to view the analysis.

📁 Repository Structure

• Customer_shopping_behaviour.ipynb: The main Python notebook containing data cleaning and EDA.

• customer_shopping_behavior.csv: The raw dataset used for the analysis.

• SQL-Queries.pdf: Documentation of SQL scripts used for database analysis.

• Customer-Shopping-Behavior-Analysis.pptx: Final presentation of business findings.

• Customer Shopping Behavior Analysis.pdf: Comprehensive written report.