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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM65169
Volume:
10 |
6 |
Jun 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
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.

Computer Engineering
Met’s Institute of Engineering

Computer Engineering
Met’s Institute of Engineering

Computer Engineering
Met’s Institute of Engineering

Computer Engineering
Met’s Institute of Engineering

Computer Engineering
Met’s Institute of Engineering
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
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.
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Publication Date
6/19/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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