{"id":25921849,"url":"https://github.com/pradip-data/ecommerce-sales-optimization","last_synced_at":"2026-05-09T06:04:58.529Z","repository":{"id":280106374,"uuid":"941003702","full_name":"pradip-data/Ecommerce-Sales-Optimization","owner":"pradip-data","description":"This project examines cart abandonment trends at MagicMade e- commerce , identifying revenue loss, customer behavior, and optimization strategies. 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Customers often add items to their carts but leave without completing the purchase, leading to revenue loss and reduced customer retention. This project leverages **Python, Power BI, and SQL** to analyze cart abandonment trends, identify key reasons, and suggest actionable strategies to improve conversions.\n\n## 🛒 Problem Statement\n\nMagicMade, an online retail platform, has been experiencing a high shopping cart abandonment rate. This issue affects revenue generation and impacts customer experience. The challenge is multifaceted, involving factors such as:\n\n### **1️⃣ Revenue Impact**\nEach abandoned cart represents a **missed sales opportunity**, directly reducing potential earnings. Understanding why users abandon their carts helps recover lost revenue.\n\n### **2️⃣ Customer Experience Issues**\nA high abandonment rate may indicate usability problems, such as a complex checkout process, hidden fees, or slow website performance. Improving these factors can enhance user experience and encourage successful purchases.\n\n### **3️⃣ Marketing and Conversion Optimization**\nAnalyzing abandonment patterns allows MagicMade to refine marketing strategies. **Retargeting, email reminders, and checkout process improvements** can help convert abandoned carts into successful transactions.\n\n\n## **🛠 Tech Stack Used**\n\n- **Python** 🐍 (Data cleaning, analysis, and visualization using  Numpy, Pandas, Matplotlib, and Seaborn)\n- **SQL** 🗄️ (Data extraction, transformation, and aggregation)\n- **Power BI** 📊 (Interactive dashboards for reporting and insights)\n\n  \n### **Data Attributes:**\n- `User_ID` - Unique identifier for users\n- `User_Location` - Geographical location of users\n- `Gender` - Male or Female\n- `Cart_Contents` - Items in the shopping cart\n- `Cart_Value` - Value of items in the cart\n- `Session_Date` - Date of session activity\n- `Session_Duration` - Total time spent in a session\n- `Abandonment_Reason` - Stated reason for not completing the purchase\n- `Purchase_Category` - Category of products\n- `Referral_Medium` - Source of website traffic (Social Media, Search Engine, Email, etc.)\n- `Device_Type` - Desktop, Mobile, or Tablet\n- `Cart_Status` - Paid or Abandoned\n\n\n### **Note :**\nOriginally this Dataset Row Count is 7212 Then Use of Python Code \u0026 AI we can generate more 500000  Row Dataset according to previous patteren of Dataset\n\n## **🎯 Key Objectives**\n\n1. **Analyze Cart Abandonment Trends** 📊\n   - Identify the most common reasons for cart abandonment.\n   - Examine patterns in user behavior leading to abandonment.\n\n2. **Understand Revenue Impact** 💰\n   - Calculate total revenue loss due to abandoned carts.\n   - Analyze cart values of abandoned vs. completed purchases.\n\n3. **Optimize Customer Experience** 🔍\n   - Determine session duration trends and their correlation with abandonment.\n   - Identify user demographics contributing to high abandonment rates.\n\n4. **Improve Marketing \u0026 Retargeting Strategies** 🎯\n   - Analyze the effectiveness of different referral mediums.\n   - Determine the best-performing and worst-performing product categories.\n\n  \n\n   ---\n## **📥 Installation \u0026 Usage Guide**\n\n### **1️⃣ Clone the Repository**\n```bash\ngit clone https://github.com/your-username/shopping-cart-abandonment.git\ncd shopping-cart-abandonment\n```\n\n### **2️⃣ Install Dependencies**\n```bash\npip install pandas numpy matplotlib seaborn powerbi-python-sdk\n```\n\n### **3️⃣ Run the Python Analysis**\n```bash\npython analysis.py\n```\n\n### **4️⃣ Load SQL Queries in Database**\nRun the provided SQL queries in your **database management system**.\n\n### **5️⃣ Open Power BI Dashboard**\nImport the Power BI `.pbix` file to explore interactive insights.\n\n---\n  \n   \n## **📊 Data Analysis \u0026 Visualization (Python + Power BI)**\n\n### **1️⃣ Python Data Analysis \u0026 Insights**\nThe dataset is analyzed using Python to uncover hidden patterns and trends:\n\n📌 **Python-Generated Visualizations:**  \n\n#### **1.Cart Status Distribution**  \n\u003cimg src=\"Python%20Visulization%20Images/cart%20status%20distribution.png\" width=\"400\"\u003e\n\n#### **2.Most Common Cart Abandonment Reason**  \n\u003cimg src=\"Python%20Visulization%20Images/most%20common%20abandonment%20reason.png\" width=\"400\"\u003e\n\n#### **3.Top Referral Medium**  \n\u003cimg src=\"Python%20Visulization%20Images/top%20refferal%20medium.png\" width=\"400\"\u003e\n\n#### **4.Month Wise Cart Abandonment Rate**  \n\u003cimg src=\"Python%20Visulization%20Images/month%20wise%20cart%20abandonment%20rate.png\" width=\"400\"\u003e\n\n#### **5.Abandoned Users by Session Range**  \n\u003cimg src=\"Python%20Visulization%20Images/abandoned%20users%20by%20session%20range.png\" width=\"400\"\u003e\n\n#### **6.Total Cart Value: Paid vs Abandonment**  \n\u003cimg src=\"Python%20Visulization%20Images/total%20cart%20value%20paid%20vs%20abandonment.png\" width=\"400\"\u003e\n\n---\n\n### **2️⃣ Power BI Interactive Dashboard**\nThe **Power BI dashboard** provides a real-time, interactive analysis of cart abandonment patterns.\n\n#### **Cart Abandonment Analysis Dashboard**  \n\u003cimg src=\"Python%20Visulization%20Images/Cart%20Abandonment%20Analysis%20Overview-1.png\" width=\"600\"\u003e\n\n\u003cimg src=\"Python%20Visulization%20Images/Cart%20Abandonment%20Analysis%20Dashboard-2.png\" width=\"600\"\u003e\n\n\n\n\n---\n## **📌 SQL Queries for Data Extraction \u0026 Transformation**\nSQL queries were used to extract and transform data before visualization:\n\n🔹 **Total Cart Abandonment \u0026 Paid Transactions:**\n```sql\nSELECT Cart_Status, COUNT(*) AS Total_Count\nFROM shopping_cart_data\nGROUP BY Cart_Status;\n```\n\n🔹 **Abandonment Reasons Breakdown:**\n```sql\nSELECT Abandonment_Reason, COUNT(*) AS Abandonment_Count\nFROM shopping_cart_data\nWHERE Cart_Status = 'Abandoned'\nGROUP BY Abandonment_Reason\nORDER BY Abandonment_Count DESC;\n```\n\n🔹 **Average Cart Value of Abandoned vs. Paid Transactions:**\n```sql\nSELECT Cart_Status, AVG(Cart_Value) AS Avg_Cart_Value\nFROM shopping_cart_data\nGROUP BY Cart_Status;\n```\n\n## 🔍 Key Insights from Data Analysis\n\n1. **Total Users:** 507,211\n2. **State with Highest Customers:** Virginia (85,681 customers)\n3. **Highest Cart Abandonment Rate by State:** Virginia (42,765 abandoned carts)\n4. **Most Common Abandonment Reasons:**\n   - **Complex Checkout:** 63,748 cases\n   - **No Guest Checkout Option:** 63,746 cases\n5. **Devices and Checkout Complaints:**\n   - **Desktop Users:** Most complaints about **complex checkout**\n   - **Mobile Users:** Most complaints about **complex checkout**\n   - **Tablet Users:** Most complaints about **No Guest Checkout Option**\n6. **Average Abandoned Cart Value:** $260\n7. **Cart Value Range with Highest Abandonment:** $100-$200\n8. **Session Duration Analysis:**\n   - **Longer session times (81-120 mins) saw the highest abandonment rates**\n   - **Shorter session times (5-20 mins) had the lowest abandonment rates**\n9. **Purchase Categories with Highest Abandonment:**\n   - **Candle Holders, Wedges, Puzzles, Slippers, Games**\n10. **Potential Revenue Loss Due to Abandonment:** **50.05% of total revenue**\n\n\n## ✅ Recommendations \u0026 Final Solution\n\n### **🛠️ 1. Simplify the Checkout Process**\n- Reduce the number of checkout steps\n- Enable **one-click checkout** for returning customers\n- Provide a clear **progress bar** to guide users\n\n### **👥 2. Introduce Guest Checkout**\n- Avoid forcing users to **create an account before purchasing**\n- Offer an **express checkout** option\n\n### **📢 3. Optimize Marketing \u0026 Retargeting**\n- **Email Reminders**: Send abandoned cart reminders with discounts\n- **Retargeting Ads**: Display relevant ads to users who abandoned their carts\n- **Personalized Offers**: Provide discount codes for hesitant customers\n\n### **📱 4. Improve Mobile Experience**\n- Optimize checkout for **mobile users**, as they have the highest abandonment rate\n- Ensure a **fast, responsive** mobile site\n\n### **💳 5. Transparent Pricing \u0026 Payment Options**\n- Display all fees upfront (shipping, taxes)\n- Offer **multiple payment options** (credit cards, PayPal, Buy Now Pay Later)\n\n### **📊 6. A/B Testing \u0026 Analytics**\n- Conduct A/B testing on different checkout designs\n- Monitor abandonment trends with **real-time analytics**\n\n## 🎯 Conclusion\nShopping cart abandonment is a major issue for **MagicMade**, leading to **lost revenue and decreased customer retention**. Our data-driven approach has helped identify key reasons behind cart abandonment, allowing us to propose actionable solutions. \n\nBy implementing **checkout optimizations, guest checkout, retargeting, mobile-friendly experiences, and better pricing transparency**, MagicMade can significantly **reduce abandonment rates** and **increase conversions**. With a combination of **Python (for analytics), Power BI (for visualization), and SQL (for data querying),** this project provides a comprehensive solution for improving e-commerce performance.\n\n### **📌 Author: Mangroliya Pradip**  \n### **📎 Contact: pradipias2023@gmail.com**  \n### **🌐 GitHub: https://github.com/pradip-data/Ecommerce-Sales-Optimization**  \n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpradip-data%2Fecommerce-sales-optimization","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpradip-data%2Fecommerce-sales-optimization","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpradip-data%2Fecommerce-sales-optimization/lists"}