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.
| Mualliflar | Xaitov , Bekzod, Хаитов , Бекзод, Xaitov , Bekzod |
|---|---|
| Jurnal | Иқтисодий тараққиёт ва таҳлил |
| Nashr sanasi | 2025-11-28 |
| Jild | 3 |
| Son | 11 |
| Betlar | 258-268 |
| Til | O‘zbek |
| DOI | 10.60078/2992-877x-2025-vol3-iss11-pp258-268 |
DOI: 10.60078/2992-877x-2025-vol3-iss11-pp258-268 · Maqolaning asl sahifasi
bilateral optimization, B2B data pricing, dynamic pricing model, Data monetization, value-based pricing, machine learning pricing, SHAP value analysis, двусторонняя оптимизация, ценообразование данных в сегменте B2B, модель динамического ценообразования, монетизация данных, ценообразование на основе ценности, ценообразование с использованием машинного обучения, анализ значений SHAP, ikki tomonlama optimallashtirish, B2B maʼlumotlar narxlash, dinamik narxlash modeli, maʼlumotlarni monetizatsiya qilish, qiymatga asoslangan narxlash, mashinani oʻrganish narxlas, SHAP qiymatini tahlil qilis
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