Optimizing Revenue through Predictive Analytics: An SVM Approach to User Behavior Classification

Project Overview

The goal of this project was to predict revenue generation on a website based on user behavior. By leveraging data science techniques such as feature engineering, dimensionality reduction, and machine learning, the objective was to build a model that can classify user sessions as revenue-generating or non-revenue-generating. The final output of this project provides actionable insights for businesses to optimize user engagement, tailor marketing strategies, and increase revenue.

Business Case

With the growing importance of data-driven decision-making, businesses are keen to optimize their website to increase revenue generation. By understanding how user behavior impacts revenue, companies can refine their marketing strategies, website design, and content personalization. This project seeks to provide insights into user engagement, which can be used to boost conversion rates and enhance the overall customer experience.

Key Steps Taken

1. Data Preparation and Feature Engineering

• Feature Selection: The dataset included various features such as user activity metrics (e.g., BounceRates, ExitRates, PageValues) and user demographics (OperatingSystems, Browser). The target variable, V_Revenue, was separated to predict whether a session generated revenue or not.
• Preprocessing:
  • Numerical Features: Features such as BounceRates, ExitRates, and PageValues were standardized using StandardScaler to ensure they are on the same scale.
  • Categorical Features: Categorical features like Month, Region, and VisitorType were one-hot encoded using OneHotEncoder to handle categorical data properly.
• Dimensionality Reduction: Principal Component Analysis (PCA) was performed to reduce the dimensionality of the dataset and retain the most significant components for model training. This helped in improving model efficiency by eliminating irrelevant features.

2. Model Construction

• Support Vector Machine (SVM) models were used to predict revenue generation:
  • Linear SVM: A basic SVM model using a linear kernel.
  • RBF SVM: SVM model with a Radial Basis Function (RBF) kernel for capturing non-linear relationships.
• The models were trained using the training dataset and evaluated using the test set to assess their performance.

3. Model Evaluation

• Cross-validation: Both models were evaluated using 5-fold cross-validation, showing high performance for both linear and RBF kernels with accuracy scores around 99%.
• Classification Metrics:
  • Precision, Recall, and F1-score were calculated for each class (revenue-generating and non-revenue-generating).
• The Linear SVM model, with an accuracy of 98.7%, was selected as the preferred model due to its stability and efficiency in prediction.

4. Model Interpretation

• Comparison of predicted values with actual values revealed that both models performed well in classifying the revenue status of sessions.
• The Linear SVM model was found to be robust and efficient, producing accurate revenue predictions with minimal computational complexity.

5. Future Recommendations

• Feature Enhancement: More sophisticated features, such as real-time session data, could be added to improve prediction accuracy.
• Advanced Models: Exploring models like XGBoost, Random Forest, or Neural Networks might enhance performance further, especially in more complex datasets.
• Real-Time Data Integration: Incorporating real-time data could provide dynamic insights, helping businesses make quick adjustments to strategies.

Business Recommendations

• Targeted Marketing: Use the insights from the clustering of user behaviors to create targeted marketing campaigns aimed at high-potential users who are likely to generate revenue.
• Personalized User Experience: Personalize website content based on the behavior and preferences of users to increase engagement and conversion rates.
• Optimizing Website Design: Adjust website design elements (e.g., bounce rate reduction, content engagement) based on the analysis to enhance user interaction and revenue generation.

Conclusion

This project successfully demonstrated how user behavior data can be leveraged to predict revenue generation on a website. The application of SVM models helped achieve high accuracy, while feature engineering and PCA facilitated improved model performance. By understanding user behavior, businesses can refine strategies and maximize revenue potential.

Author

Ansuman Patnaik
MS in Data Science & Analytics, Yeshiva University
Email: ansu1p89k@gmail.com