Authors : Linda Aluso; Joy Onma Enyejo

Volume/Issue : Volume 10 - 2025, Issue 11 - November

Google Scholar : https://tinyurl.com/3waadtuy

Scribd : https://tinyurl.com/nhjsx8b6

DOI : https://doi.org/10.38124/ijisrt/25nov949

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Abstract : Customer Relationship Management (CRM) systems have evolved into data-driven platforms that support strategic decision-making in sales and marketing operations. However, traditional CRM analytics often rely on single-model predictive algorithms that fail to capture the complex, nonlinear patterns influencing lead conversion and pipeline efficiency. This review explores the integration of multi-model ensemble learning specifically XGBoost, Random Forest, and Gradient Boosting frameworks within HubSpot environments to enhance predictive accuracy and optimize sales pipeline performance. By aggregating multiple weak learners, these ensemble models improve generalization and reduce variance, thereby offering a more robust mechanism for forecasting lead conversion probabilities, prioritizing high-value prospects, and identifying bottlenecks across sales stages. The paper examines comparative performance metrics, feature-importance interpretability, and deployment strategies that integrate HubSpot’s native APIs with advanced machine learning workflows. It also evaluates the role of data preprocessing, real-time automation triggers, and dashboard visualizations in supporting sales decision intelligence. Through a synthesis of current literature, case studies, and empirical analyses, the review highlights how hybrid ensemble systems can transform CRM analytics from reactive reporting tools into proactive, prescriptive engines that drive higher sales efficiency and long-term customer value creation. Future directions emphasize model explainability, ethical AI practices in lead scoring, and scalable integration of ensemble pipelines across multi-tenant CRM architectures.

Keywords : Ensemble Learning; CRM Optimization; Lead Conversion Prediction; HubSpot Analytics; XGBoost and Random Forest Models.

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