Engineering and Technology
| Open Access |
DOI: AI-Powered Lead Scoring and Opportunity Management in Salesforce CRM
Geetha Krishna Sangam , Irving, TX, USAAbstract
Lead scoring and opportunity management are critical functions in customer acquisition and revenue generation for modern enterprises. Traditional rule-based approaches often rely on static criteria, manual judgment, and historical heuristics, resulting in inaccurate prioritization, delayed conversions, and suboptimal sales outcomes. The emergence of Artificial Intelligence (AI) within Customer Relationship Management (CRM) platforms has fundamentally transformed how organizations identify, prioritize, and convert potential customers. This paper presents an in-depth study of AI-powered lead scoring and opportunity management within Salesforce CRM, focusing on machine learning–driven predictive intelligence, real-time data orchestration, and automated sales workflows. The proposed architecture demonstrates how AI enhances pipeline visibility, improves conversion accuracy, and enables data-driven decision-making while maintaining scalability, governance, and explainability.
Keywords
AI-Driven Lead Scoring, Opportunity Management, Salesforce CRM, Machine Learning, Predictive Analytics, Sales Automation, Customer Intelligence
References
IEEE, Artificial Intelligence in Customer Relationship Management, IEEE Xplore.
Chen et al., “Predictive Analytics for Sales Forecasting,” IEEE Transactions on Knowledge and Data Engineering.
Salesforce, AI and Predictive Intelligence in CRM Platforms.
ISO/IEC 27001, Information Security Management Systems.
Gartner, AI Adoption in Sales and CRM Systems.
Fig 1.1:
https://www.peeklogic.com/article/salesforce-einstein-lead-scoring/
https://salespanel.io/blog/product/lead-scoring-for-salesforce/
Download and View Statistics
Copyright License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain the copyright of their manuscripts, and all Open Access articles are disseminated under the terms of the Creative Commons Attribution License 4.0 (CC-BY), which licenses unrestricted use, distribution, and reproduction in any medium, provided that the original work is appropriately cited. The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations.