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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM65169

SmartFunnel-Lead Management System using Predictive Modelling for Lead Conversion

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM65169

Volume:

10 |

6 |

Jun 2026

Pages:

1-5

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM65169

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

Devesh Mudaliar

Devesh Mudaliar

Computer Engineering

Met’s Institute of Engineering

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Siddhant Junnare

Siddhant Junnare

Computer Engineering

Met’s Institute of Engineering

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Khurram Khan

Khurram Khan

Computer Engineering

Met’s Institute of Engineering

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Chanchal Deore

Chanchal Deore

Computer Engineering

Met’s Institute of Engineering

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Kanchan Pekhale

Kanchan Pekhale

Computer Engineering

Met’s Institute of Engineering

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Abstract

The increasing complexity of digital marketing cam-paigns demands intelligent tools that can efficiently manage leads, prioritize high-potential prospects, and automate outreach. Traditional solutions focusing only on automation or analytics often fail to optimize campaign performance. SmartFunnel is an AI-powered marketing automation and lead management platform that integrates predictive analytics and automated engagement into a unified, decoupled local system. Utilizing a modern React frontend and FastAPI backend, it collects and manages leads seamlessly. SmartFunnel employs an XGBoost ma-chine learning engine to assign predictive scores to leads, helping marketing teams prioritize prospects with a higher likelihood of conversion. Real-time data is synchronized using Google Firebase Firestore, bypassing traditional relational database bottlenecks. Furthermore, automated outreach via WhatsApp and Email is executed through n8n webhooks, reducing manual follow-ups and improving engagement. A custom dark-themed dashboard provides actionable insights into campaign performance, lead metrics, and ROI. With its modern tech stack and data-driven approach based on a synthetic training dataset, SmartFunnel provides a structured solution for improving lead prioritization, response rates, and overall marketing efficiency.

KEYWORDS

SmartFunnel Predictive Analytics Lead Man-agement Marketing Automation Machine Learning XGBoost React FastAPI Firebase n8n

Cite This Article

Devesh Mudaliar, Siddhant Junnare, Khurram Khan, Chanchal Deore, Kanchan Pekhale, "SmartFunnel-Lead Management System using Predictive Modelling for Lead Conversion," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 6, pp. 1-5, Jun 2026. https://doi.org/10.55041/IJSREM65169

Full Article/Conclusion

The SmartFunnel system effectively modernizes digital mar-keting lead management by combining advanced predictive an-alytics with a fast, decoupled web architecture. By integrating a React frontend, Firebase Firestore, FastAPI machine learning backend, and n8n workflow automation, the platform provides accurate real-time lead scoring and automated engagement. Experimental validation confirms the XGBoost model’s strong predictive capability. Future cloud deployment and organic data integration will further strengthen scalability and enter-prise readiness.

References

[1]     Zhao, L., Chen, W., and Tan, R., “Causal Predictive Optimization and Generation (CPOG) for Business AI,” Journal of Business Artificial Intelligence, vol. 12, no. 1, pp. 33–49, 2025. [2]     Andrew, M., “Enhancing B2B Sales Funnels with AI-Driven Lead Scoring Models,” International Journal of Sales Analytics and Predictive Modeling, vol. 9, no. 2, pp. 78–94, 2024. [3]     Jena, S., Yang, J., and Tan, F., “Unlocking Sales Growth: Account Prioritization Engine with Explainable AI,” Journal of Explainable Artificial Intelligence in Business, vol. 7, no. 3, pp. 120–134, 2023. [4]     Sharma, P., and Gupta, N., “Machine Learning-Based Lead Management Systems for CRM Optimization,” Journal of Artificial Intelligence and Marketing, vol. 8, no. 2, pp. 110–124, 2021. [5]     Chen, L., and Zhang, Y., “Automated Campaign Optimization using Predictive Models in Digital Advertising,” in Proceedings of the IEEE International Conference on Big Data, pp. 1458–1466, 2020. [6]     Kumar, A., and Mehta, R., “Predictive Analytics in Digital Marketing: Enhancing Lead Scoring and Campaign Performance,” International Journal of Data Science and Business Analytics, vol. 10, no. 3, pp. 45–56, 2022. [7]     Patel, S., and Desai, V., “Cloud-Based Customer Analytics Framework for Lead Management Systems,” International Journal of Cloud Appli-cations, vol. 14, no. 4, pp. 78–89, 2021. [8]     T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” in Proc. 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD), San Francisco, CA, USA, pp. 785–794, 2016. [9]     R. Mishra, P. Kumar, and N. Rao, “Explainable AI Techniques for Transparent Marketing Decisions,” IEEE Access, vol. 10, pp. 121540–121552, 2022. [10]    A. Molnar, Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, 2nd ed., Lulu Press, 2023.

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Publication Date

6/19/2026

Peer Review Type

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Review Rounds: 2

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