A Novel Approach for Commercial Opportunities Qualification Using the BANT Methodology Under the Fuzzy Set Theory Framework

EasyChair Preprint 13680

Abstract

Demand generation is crucial for organizations, supplying sales teams with well-qualified commercial opportunities. Despite the wide variety of existing opportunity qualification methodologies, the subjective nature of the experts’ final evaluation remains an obstacle to efficiency and productivity in the business process. This research investigated how Fuzzy Set Theory and Fuzzy Logic could be applied to the BANT methodology of qualification of commercial opportunities to replace these deliberative evaluations by experts to increase the sales cycle performance. A fuzzy inference system was developed, emulating the assessments of the experts. The rating analysis obtained after processing a sample of commercial opportunities from 2022 and 2023 confirmed the system’s effectiveness in aligning with expert perceptions. While the study showed room for refinements in the model, the findings underscore the potential to streamline the qualification of opportunities and improve sales cycle performance

Keyphrases: BANT Methodology, Behavioral Biases, Fuzzy Inference Systems, Fuzzy Logic, Lead Qualification, Sales Forecasting, Sales Optimization, bant criteria, better sales cycle performance, commercial opportunity qualification bant methodology, decision making, expert assessments, fuzzy inference, fuzzy set, fuzzy set theory, fuzzy set theory and fuzzy logic, improve sales cycle performance, machine learning, marketing and sales teams, opportunity qualification methodologies, qualification of commercial opportunities, qualifying business opportunities, rating scores, sample of commercial opportunities

@booklet{EasyChair:13680,
  author    = {Marcus V. Leite and Jair M. Abe and Marcos L. H. Souza},
  title     = {A Novel Approach for Commercial Opportunities Qualification Using the BANT Methodology Under the Fuzzy Set Theory Framework},
  howpublished = {EasyChair Preprint 13680},
  year      = {EasyChair, 2024}}