🛒 E-Commerce Sales & Customer Behavior Analysis (SQL Project)

This project explores an E-Commerce dataset using advanced SQL queries to extract insights about customer behavior, sales trends, time patterns, and funnel conversion performance. All analysis was performed using MySQL Workbench.

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📂 Dataset

• Source: Kaggle - E-Commerce Behavior Data
• File: 2019-Oct.csv
• Size: 2M+ rows, includes timestamp, user_id, product, category, and event type

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📊 Key Analysis Areas

1. 📁 Exploratory Data Analysis (EDA)

• Verified schema and missing values
• Removed corrupted timestamps (year 2106)
• Checked min/max dates, event types

2. 🧑‍💻 Customer Behavior

• Total unique users, purchasers, and repeat buyers
• Engagement rate per session
• Insights into user types and behavior patterns

3. 💰 Sales & Product Analysis

• Top-selling products by purchase count
• Revenue by brand and category
• Average Order Value (AOV)
• Top spending users

4. 📅 Time-Based Analysis

• Sales trends by hour, weekday, and month
• Peak sales periods identified

5. 🔁 Funnel Analysis

• Views: ~1,016,814
• Cart: ~14,762 (1.45% of views)
• Purchase: ~17,000 (1.67% of views)
• Insight: Most users purchase directly without using the cart — likely due to mobile behavior

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📎 Project Structure

ecommerce-sql-project/ ├── queries/ │ ├── 1.INITIAL DATA EXPLORATION (EDA).sql │ ├── 2.CUSTOMER BEHAVIOR ANALYSIS.sql │ ├── 3.SALES AND PRODUCT ANALYSIS.sql │ ├── 4.TIME ANALYSIS.sql │ └── 5.CONVERSION FUNNEL ANALYSIS.sql ├── data/ ├── README.md ├── LICENSE └── .gitignore

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🔍 Key Analysis Performed

📊 1. Initial Data Exploration (EDA)

• Checked date ranges, event types, user IDs, and data quality
• Identified the presence of future-dated rows (e.g., year 2106)
• Determined active time range in dataset

👥 2. Customer Behavior Analysis

• Tracked repeat users and single-visit users
• Identified purchase patterns across users
• Segmented behavior by event types (view, cart, purchase)

💸 3. Sales and Product Analysis

• Found top-selling products
• Analyzed sales by category and product type
• Identified products with high cart abandonment

⏰ 4. Time-Based Analysis

• Analyzed sales and activity by hour, day, and month
• Found peak user activity and conversion hours

🔁 5. Funnel Conversion Analysis

• Calculated view-to-cart, cart-to-purchase, and view-to-purchase rates
• Visualized drop-offs in the sales funnel

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📈 Insights

• 🔍 Over 1 million views, but only ~17K purchases
• 📉 View-to-purchase conversion rate: ~1.67%
• 🛍️ Majority of purchases bypassed the cart step
• 🕖 Most purchases occurred during evening hours
• 🛒 High cart abandonment rate for certain product types

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🔧 Tools Used

• SQL (MySQL Workbench)
• Git & GitHub for version control
• Excel (for initial data checks)

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📁 Dataset Info

• Format: .csv

• Rows: ~1.1 million

• Fields: event_time, event_type, product_id, category_id, user_id, etc.

• Source: Sample E-Commerce behavior data

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📜 License

This project is licensed under the MIT License.

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🙌 Acknowledgements

Thanks to online communities, mentors, and open datasets that support hands-on learning in data analytics.

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💼 About Me

I'm an aspiring Data Analyst passionate about turning data into actionable insights.
Connect with me on LinkedIn for collaboration, feedback, or opportunities.

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