The article analyzes existing methodologies for assessing the effectiveness of incentive tax benefits in regions and identifies their advantages and disadvantages. It is substantiated that the assessment of the effectiveness of incentive tax benefits is aimed at determining the correlation between losses in budget revenues and the economic benefits arising for the state and taxpayers, as well as making decisions to extend, adjust, or cancel the benefit. It is demonstrated that the assessment of tax benefit effectiveness should be carried out based on budgetary, economic, and social criteria, and the assessment process should be conducted stage-by-stage. Based on the research results, an assessment methodology based on an integral coefficient and covering the main efficiency criteria is proposed. This allows for a comprehensive assessment of the budgetary, economic, and social consequences of tax benefits at the local level, as well as making well-grounded management decisions.

Nowadays, B2B data pricing models are dominated by the seller side, ignoring buyers’ perspectives. Traditional model results in reduced market efficiency and suboptimal financial outcomes for all stakeholders in the long run. This research analyzes components such as value levers (i.e., functions for buyers and sellers), a dynamic algorithm using LightGBM machine learning, and an API for real-time pricing.