Marketing Campaign Performance and Conversion Analytics

Authors: Chintaparthi Harsha Vardhan Balaji Rao , Achrla Purushotham , Durgam Ganesh, Juruku Govardhan , Bommala Venkatamani Chandra , Moulika Grandhi

DOI Link: https://doi.org/10.22214/ijraset.2026.79124

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Abstract

This paper presents a deep learning-based disaster zone classification and analysis system that automatically identifies natural disasters such as cyclones, floods, landslides, wildfires, and volcanic eruptions from satellite and aerial images. Traditional manual image analysis is slow and error-prone, making it unsuitable for emergency response. To address this, the proposed system employs advanced Convolutional Neural Network (CNN) architectures, including DenseNet, EfficientNet, and ResNet, to accurately classify disaster images. Targeted data augmentation techniques, such as rotation, color variation, and perspective transformation, improve the recognition of visually similar disaster types and enhance model generalization. Grad-CAM visualization is integrated to highlight affected regions, increasing model transparency and supporting disaster assessment. The workflow includes dataset collection, preprocessing, exploratory data analysis, model training, evaluation, and visualization. The system is designed for cloud deployment using frameworks like Flask or FastAPI, enabling real-time disaster monitoring through web or mobile applications. Experimental results show that EfficientNet-B0 achieved the highest accuracy (82.15%), outperforming DenseNet-121 (78.64%) and ResNet-34 (80.72%), while maintaining efficient processing times of 1.5–3 seconds per image. Overall, the proposed framework provides an accurate, scalable, and interpretable solution for automated disaster detection, supporting faster emergency response and disaster management.

Introduction

This paper presents a machine learning-based marketing campaign prediction system that analyzes customer behavior and predicts campaign response to improve digital marketing effectiveness. Traditional metrics such as clicks and impressions often fail to reflect actual customer engagement and conversion, creating the need for intelligent predictive analytics. The proposed system applies data preprocessing, feature engineering, class balancing (SMOTE), and machine learning classification to identify customers likely to respond to marketing campaigns. An interactive dashboard enables users to input campaign parameters and visualize prediction probabilities, return on investment (ROI), conversion rates, and campaign analytics in real time. The methodology includes data cleaning, feature creation, model training, evaluation using accuracy, precision, recall, F1-score, and confusion matrix, followed by deployment through an API and user-friendly dashboard. Experimental results show that the proposed Random Forest model outperformed other algorithms, achieving 75% accuracy, 79% precision, 75% recall, and a 77% F1-score, demonstrating balanced and reliable performance. Overall, the system provides interpretable predictions, real-time decision support, and actionable insights that help businesses optimize marketing strategies, improve customer targeting, increase conversion rates, and maximize return on investment (ROI).

Conclusion

The proposed system successfully demonstrates the application of machine learning techniques in predicting customer response and analyzing marketing campaign performance. By incorporating data preprocessing, feature engineering, and classification methods, the system is able to generate meaningful and reliable predictions. The use of advanced techniques such as SMOTE and proper model evaluation ensures balanced performance and improved handling of class imbalance. The integration of an interactive dashboard enhances the usability of the system by allowing users to input campaign parameters and visualize results in real time. Key metrics such as prediction probability, ROI, and conversion analytics provide valuable insights into campaign effectiveness. Features like trend analysis and what-if simulation further support users in understanding the impact of different strategies and optimizing decision-making. Overall, the system highlights the importance of data-driven approaches in modern marketing. It provides a scalable and flexible solution that can be extended with additional features, advanced models, and real-time data integration. The project demonstrates how machine learning can significantly improve marketing efficiency, customer targeting, and overall business outcomes.

References

[1] Scikit-learn Documentation. Available: https://scikit-learn.org?? [2] Pandas Documentation. Available: https://pandas.pydata.org?? [3] NumPy Documentation. Available: https://numpy.org?? [4] Matplotlib Documentation. Available: https://matplotlib.org?? [5] Streamlit Documentation. Available: https://streamlit.io?? [6] FastAPI Documentation. Available: https://fastapi.tiangolo.com?? [7] Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras& TensorFlow. [8] Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques. [9] Kotler, P., & Keller, K. (2016). Marketing Management. [10] Research papers on marketing analytics and customer behavior prediction from IEEE and Springer.

Copyright

Copyright © 2026 Chintaparthi Harsha Vardhan Balaji Rao , Achrla Purushotham , Durgam Ganesh, Juruku Govardhan , Bommala Venkatamani Chandra , Moulika Grandhi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Paper Id : IJRASET79124

Publish Date : 2026-03-31

ISSN : 2321-9653

Publisher Name : IJRASET

DOI Link : Click Here