ParaBANT: An Adaptive Paraconsistent Model for Bias-Resistant B2B Lead Qualification and Segmentation

Effective lead qualification is essential for maintaining a consistent pipeline of qualified opportunities in sales. Traditional frameworks like BANT (Budget, Authority, Need, and Timing) rely on subjective expert assessments, often affected by inconsistent and incomplete information—particularly common in early sales stages—leading to unreliable decisions and missed opportunities. Machine learning-based automated models often lack transparency, which can hinder their adoption in real-world decision-making. This study integrates Paraconsistent Annotated Evidential Logic Eτ (Logic Eτ) into the BANT framework by proposing the ParaBANT model. The goal is to reduce subjectivity and bias while enhancing decision performance and accuracy in a deterministic and transparent manner—even under conflicting or insufficient data. Findings show that the proposed model improves qualification consistency and supports lead segmentation through an innovative Logic Eτ-based clustering approach, optimizing sales pipeline efficiency with transparency and clarity. The study contributes to academic research by introducing a formal and explainable alternative to both subjective frameworks and opaque AI systems. It expands the use of paraconsistent logic in business intelligence and delivers a low-cost, interpretable solution particularly suitable for small and medium-sized enterprises. More broadly, it aligns with the United Nations’ Sustainable Development Goals—specifically SDG 8, SDG 9, and SDG 12—by promoting innovation, improving resource allocation, and encouraging more ethical, transparent, and accountable decision-making practices.