Strategies to Combat Shopping Cart Abandonment & Boost Sales at MagicMade e-commerce

📌 Project Overview

Shopping cart abandonment is a significant challenge for e-commerce businesses, including MagicMade. 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.

🛒 Problem Statement

MagicMade, 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:

1️⃣ Revenue Impact

Each abandoned cart represents a missed sales opportunity, directly reducing potential earnings. Understanding why users abandon their carts helps recover lost revenue.

2️⃣ Customer Experience Issues

A 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.

3️⃣ Marketing and Conversion Optimization

Analyzing abandonment patterns allows MagicMade to refine marketing strategies. Retargeting, email reminders, and checkout process improvements can help convert abandoned carts into successful transactions.

🛠 Tech Stack Used

• Python 🐍 (Data cleaning, analysis, and visualization using Numpy, Pandas, Matplotlib, and Seaborn)
• SQL 🗄️ (Data extraction, transformation, and aggregation)
• Power BI 📊 (Interactive dashboards for reporting and insights)

Data Attributes:

• User_ID - Unique identifier for users
• User_Location - Geographical location of users
• Gender - Male or Female
• Cart_Contents - Items in the shopping cart
• Cart_Value - Value of items in the cart
• Session_Date - Date of session activity
• Session_Duration - Total time spent in a session
• Abandonment_Reason - Stated reason for not completing the purchase
• Purchase_Category - Category of products
• Referral_Medium - Source of website traffic (Social Media, Search Engine, Email, etc.)
• Device_Type - Desktop, Mobile, or Tablet
• Cart_Status - Paid or Abandoned

Note :

Originally this Dataset Row Count is 7212 Then Use of Python Code & AI we can generate more 500000 Row Dataset according to previous patteren of Dataset

🎯 Key Objectives

1. Analyze Cart Abandonment Trends 📊

   • Identify the most common reasons for cart abandonment.
   • Examine patterns in user behavior leading to abandonment.

2. Understand Revenue Impact 💰

   • Calculate total revenue loss due to abandoned carts.
   • Analyze cart values of abandoned vs. completed purchases.

3. Optimize Customer Experience 🔍

   • Determine session duration trends and their correlation with abandonment.
   • Identify user demographics contributing to high abandonment rates.

4. Improve Marketing & Retargeting Strategies 🎯

   • Analyze the effectiveness of different referral mediums.
   • Determine the best-performing and worst-performing product categories.

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📥 Installation & Usage Guide

1️⃣ Clone the Repository

git clone https://github.com/your-username/shopping-cart-abandonment.git
cd shopping-cart-abandonment

2️⃣ Install Dependencies

pip install pandas numpy matplotlib seaborn powerbi-python-sdk

3️⃣ Run the Python Analysis

python analysis.py

4️⃣ Load SQL Queries in Database

Run the provided SQL queries in your database management system.

5️⃣ Open Power BI Dashboard

Import the Power BI .pbix file to explore interactive insights.

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📊 Data Analysis & Visualization (Python + Power BI)

1️⃣ Python Data Analysis & Insights

The dataset is analyzed using Python to uncover hidden patterns and trends:

📌 Python-Generated Visualizations:

1.Cart Status Distribution

2.Most Common Cart Abandonment Reason

3.Top Referral Medium

4.Month Wise Cart Abandonment Rate

5.Abandoned Users by Session Range

6.Total Cart Value: Paid vs Abandonment

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2️⃣ Power BI Interactive Dashboard

The Power BI dashboard provides a real-time, interactive analysis of cart abandonment patterns.

Cart Abandonment Analysis Dashboard

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📌 SQL Queries for Data Extraction & Transformation

SQL queries were used to extract and transform data before visualization:

🔹 Total Cart Abandonment & Paid Transactions:

SELECT Cart_Status, COUNT(*) AS Total_Count
FROM shopping_cart_data
GROUP BY Cart_Status;

🔹 Abandonment Reasons Breakdown:

SELECT Abandonment_Reason, COUNT(*) AS Abandonment_Count
FROM shopping_cart_data
WHERE Cart_Status = 'Abandoned'
GROUP BY Abandonment_Reason
ORDER BY Abandonment_Count DESC;

🔹 Average Cart Value of Abandoned vs. Paid Transactions:

SELECT Cart_Status, AVG(Cart_Value) AS Avg_Cart_Value
FROM shopping_cart_data
GROUP BY Cart_Status;

🔍 Key Insights from Data Analysis

1. Total Users: 507,211
2. State with Highest Customers: Virginia (85,681 customers)
3. Highest Cart Abandonment Rate by State: Virginia (42,765 abandoned carts)
4. Most Common Abandonment Reasons:
   • Complex Checkout: 63,748 cases
   • No Guest Checkout Option: 63,746 cases
5. Devices and Checkout Complaints:
   • Desktop Users: Most complaints about complex checkout
   • Mobile Users: Most complaints about complex checkout
   • Tablet Users: Most complaints about No Guest Checkout Option
6. Average Abandoned Cart Value: $260
7. Cart Value Range with Highest Abandonment: $100-$200
8. Session Duration Analysis:
   • Longer session times (81-120 mins) saw the highest abandonment rates
   • Shorter session times (5-20 mins) had the lowest abandonment rates
9. Purchase Categories with Highest Abandonment:
   • Candle Holders, Wedges, Puzzles, Slippers, Games
10. Potential Revenue Loss Due to Abandonment: 50.05% of total revenue

✅ Recommendations & Final Solution

🛠️ 1. Simplify the Checkout Process

• Reduce the number of checkout steps
• Enable one-click checkout for returning customers
• Provide a clear progress bar to guide users

👥 2. Introduce Guest Checkout

• Avoid forcing users to create an account before purchasing
• Offer an express checkout option

📢 3. Optimize Marketing & Retargeting

• Email Reminders: Send abandoned cart reminders with discounts
• Retargeting Ads: Display relevant ads to users who abandoned their carts
• Personalized Offers: Provide discount codes for hesitant customers

📱 4. Improve Mobile Experience

• Optimize checkout for mobile users, as they have the highest abandonment rate
• Ensure a fast, responsive mobile site

💳 5. Transparent Pricing & Payment Options

• Display all fees upfront (shipping, taxes)
• Offer multiple payment options (credit cards, PayPal, Buy Now Pay Later)

📊 6. A/B Testing & Analytics

• Conduct A/B testing on different checkout designs
• Monitor abandonment trends with real-time analytics

🎯 Conclusion

Shopping 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.

By 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.

📌 Author: Mangroliya Pradip

📎 Contact: pradipias2023@gmail.com

🌐 GitHub: https://github.com/pradip-data/Ecommerce-Sales-Optimization