🛒 Customer Conversion Analysis Using Clickstream Data

📌 Project Overview

This project analyzes clickstream data from an e-commerce platform to predict customer conversions, estimate potential revenue, and segment users for personalized marketing strategies. By leveraging machine learning techniques, the project enhances decision-making for businesses seeking to optimize user engagement and sales.

🎯 Objectives

1. ✅ Predict Customer Conversion (Classification)
   Determine whether a customer will complete a purchase or not based on browsing behavior.

2. 💰 Estimate Potential Revenue (Regression)
   Forecast expected revenue per user based on historical data (Generating the Revenue).

3. 🧠 Segment Customers (Clustering)
   Identify distinct customer groups based on behavioral patterns to enable targeted marketing.

💼 Business Use Cases

🎯 Marketing Optimization: Improve ad targeting and promotions by identifying high-conversion customers.

📈 Revenue Forecasting: Predict customer spending patterns to assist in pricing strategies.

👤 Personalization & Customer Retention: Group customers into behavioral segments for personalized recommendations.

🚪 Churn Prevention: Identify potential drop-offs and re-engage users with tailored interventions.

🔍 Approach

1. 🧹 Data Preprocessing:

   • Cleaned and handled missing values.
   • Encoded categorical features (e.g., country, product category).
   • Scaled numerical features using standardization.

2. 📊 Exploratory Data Analysis (EDA):

   • Analyzed browsing patterns, session lengths, and product interactions.
   • Visualized customer engagement trends using bar charts and histograms.

3. 🏗️ Feature Engineering:

   • Extracted behavioral metrics (e.g., browsing depth, time spent per category).
   • Created session-based features to capture customer intent.

4. 🧠 Model Selection:
   🔎 Supervised Learning:

   • Classification: Logistic Regression, Decision Trees, Random Forest, and XGBoost to predict purchase likelihood.
   • Regression: Linear Regression, Ridge, Lasso, and Gradient Boosting Regressors to estimate revenue.

🧩 Unsupervised Learning:
- Clustering: K-Means, DBSCAN, and Hierarchical Clustering to categorize customers into meaningful segments.

5. 📏 Model Evaluation:

   • Classification Metrics: Accuracy, Precision, Recall, F1-Score, ROC-AUC.
   • Regression Metrics: RMSE, MAE, R² Score.
   • Clustering Metrics: Silhouette Score, Davies-Bouldin Index, Within-Cluster Sum of Squares.

6. 🌐 Streamlit Application Development:

   • Built an interactive web app for:
     • 📁 CSV file uploads or manual input.
     • ⚡ Real-time purchase prediction.
     • 💸 Revenue estimation.
     • 📊 Customer segmentation visualization.

🧠 Results & Insights

• ✅ Achieved high accuracy in predicting customer conversions.
• 💵 Provided reliable revenue estimations using regression models.
• 👥 Generated distinct customer clusters for targeted marketing strategies.
• 🖥️ Developed a user-friendly Streamlit application for data-driven decision-making.

📦 Project Deliverables

• 📊 Data Analysis & Insights - Summary of findings from the dataset.
• 🔦 Streamlit Web Application - Interactive tool for business decision-making.
• 📈 Visualizations & Reports - Data exploration and clustering insights.
• 📝 Documentation - Detailed methodology, results, and interpretations.

🚀 Future Improvements

• 🤖 Incorporate Deep Learning Models: Enhance classification and regression performance with neural networks.
• 📡 Real-time Data Processing: Implement streaming analytics for real-time customer insights.
• 🔗 Integration with Business Systems: Connect predictive models with CRM and marketing platforms.

🛠️ Technical Stack

• Programming: Python
• Data Processing: Pandas, NumPy
• Machine Learning: Scikit-learn, XGBoost, Random Forest, Classification, Regression, Clustering
• Visualization: Matplotlib, Seaborn, Plotly
• Web Application: Streamlit app

📚 Dataset Reference

• UCI Machine Learning Repository: 🔗 Clickstream Data for Online Shopping