This thesis develops a quality-driven decision-support framework for B2B pricing and customer segmentation in distribution contexts. In such environments, pricing performance is not only a matter of analytical sophistication but of decision reliability, governance and data quality. The objective of this work is to design and operationalize a structured system that connects analytical outputs to managerial execution while ensuring control, transparency and scalability. The proposed framework is based on a two-dimensional customer segmentation model that combines current economic relevance and future potential through a segmentation matrix. On this analytical backbone, additional performance indicators are computed to produce standardized pricing flags that support managerial evaluation without automating final pricing decisions.