Forecasting Lead Conversion in Healthcare Campaigns Using Machine Learning-Enhanced CRM Pipelines

Authors

  • Ololade Shukrah Abass  Independent Researcher, Lagos, Nigeria
  • Oluwatosin Balogun  Independent Researcher, Lagos, Nigeria
  • Paul Uche Didi  Independent Researcher, Lagos, Nigeria

Keywords:

Lead Conversion, Healthcare Campaigns, CRM, Machine Learning, Predictive Analytics, Healthcare Marketing, Patient Acquisition, Decision Support, Campaign Optimization, Performance Dashboards.

Abstract

Effective lead conversion in healthcare campaigns is critical for improving patient acquisition, optimizing marketing ROI, and enhancing overall service delivery. This study introduces a machine learning-enhanced Customer Relationship Management (CRM) pipeline designed to forecast lead conversion in healthcare campaigns with greater precision and actionable insights. Traditional CRM systems often rely on static rules and limited segmentation strategies, which can overlook complex behavioral patterns and contextual factors influencing lead conversion. By integrating advanced machine learning models including decision trees, gradient boosting, and logistic regression into the CRM workflow, this framework dynamically analyzes historical campaign data, engagement metrics, demographic profiles, and behavioral signals to predict the likelihood of lead conversion in real time. The proposed solution incorporates a modular architecture that connects data ingestion, feature engineering, model training, and lead scoring seamlessly within the CRM environment. It enables healthcare marketers to prioritize leads based on conversion probability, personalize outreach strategies, and allocate resources more efficiently. Additionally, the framework supports continuous learning by retraining models with new campaign data, ensuring adaptability to evolving trends and audience behaviors. Key performance indicators (KPIs) such as lead scoring accuracy, campaign response rates, and conversion uplift are monitored through integrated performance dashboards for real-time decision-making and campaign optimization. The framework was validated using data from multi-channel healthcare campaigns, including email, web, call center, and social media leads. Results demonstrate a significant improvement in conversion forecasting accuracy and lead prioritization effectiveness compared to traditional CRM heuristics. Moreover, the integration of machine learning within the CRM workflow enhances cross-functional alignment between marketing, sales, and patient engagement teams. This research contributes to the growing body of knowledge in predictive marketing analytics for the healthcare sector and provides a scalable model that can be adapted across various health services, from preventive care to specialist referrals. Future extensions may explore natural language insights from unstructured patient inquiries, A/B testing automation, and federated learning for privacy-preserving model training.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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2022-10-25

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Volume 8, Issue 5, September-October-2022

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Research Articles

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Ololade Shukrah Abass, Oluwatosin Balogun, Paul Uche Didi, " Forecasting Lead Conversion in Healthcare Campaigns Using Machine Learning-Enhanced CRM Pipelines" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 5, pp.537-565, September-October-2022.