Economic efficiency of dynamic pricing algorithms in the secondary car market under conditions of full digitalization
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| Format: | Recurso digital |
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Zenodo
2026
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| _version_ | 1866901964177014784 |
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| author | Ishchenko, Vladyslav |
| author_facet | Ishchenko, Vladyslav |
| contents | <p><em>The study’s relevance stems from the rapid digital transformation of the market and the growing role of algorithmic pricing in modern business models. The purpose of the article is to highlight the theoretical and methodological foundations and assess the economic efficiency of dynamic pricing algorithms in the secondary car market.</em></p> <p><em>The study uses the following methods: analysis of the scientific literature to examine the current state of development in the subject; generalization and systematization to present the study’s results.</em></p> <p><em>It is shown that dynamic pricing is a mechanism that integrates analytics of historical and current data, behavioral signals, and market factors into real-time pricing decisions on digital platforms. It is established that the secondary car market, due to its heterogeneity and information asymmetry, creates conditions in which algorithmic pricing is more adaptive than traditional approaches. It is noted that modern digital platforms actively integrate rule-based, econometric, and machine-learning algorithms, enabling continuous price adjustments in response to demand fluctuations and competitive dynamics. It is determined that the use of dynamic pricing contributes to increased pricing accuracy, revenue optimization and reduced vehicle dwell time. Compared to static pricing, dynamic approaches yield higher profitability and better inventory turnover, though they require additional investment in technological infrastructure and maintenance. It is noted that integrating big data and artificial intelligence enhances the predictive capabilities of pricing systems and supports adaptive decision-making.</em></p> <p><em>In summary, the cost-effectiveness of dynamic pricing algorithms is achieved through a synergistic combination of the system’s technological excellence, data quality, and alignment with market conditions in the secondary car market, ensuring sustainable value creation and improved performance for market participants in a fully digital environment.</em></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19142901 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Economic efficiency of dynamic pricing algorithms in the secondary car market under conditions of full digitalization Ishchenko, Vladyslav <p><em>The study’s relevance stems from the rapid digital transformation of the market and the growing role of algorithmic pricing in modern business models. The purpose of the article is to highlight the theoretical and methodological foundations and assess the economic efficiency of dynamic pricing algorithms in the secondary car market.</em></p> <p><em>The study uses the following methods: analysis of the scientific literature to examine the current state of development in the subject; generalization and systematization to present the study’s results.</em></p> <p><em>It is shown that dynamic pricing is a mechanism that integrates analytics of historical and current data, behavioral signals, and market factors into real-time pricing decisions on digital platforms. It is established that the secondary car market, due to its heterogeneity and information asymmetry, creates conditions in which algorithmic pricing is more adaptive than traditional approaches. It is noted that modern digital platforms actively integrate rule-based, econometric, and machine-learning algorithms, enabling continuous price adjustments in response to demand fluctuations and competitive dynamics. It is determined that the use of dynamic pricing contributes to increased pricing accuracy, revenue optimization and reduced vehicle dwell time. Compared to static pricing, dynamic approaches yield higher profitability and better inventory turnover, though they require additional investment in technological infrastructure and maintenance. It is noted that integrating big data and artificial intelligence enhances the predictive capabilities of pricing systems and supports adaptive decision-making.</em></p> <p><em>In summary, the cost-effectiveness of dynamic pricing algorithms is achieved through a synergistic combination of the system’s technological excellence, data quality, and alignment with market conditions in the secondary car market, ensuring sustainable value creation and improved performance for market participants in a fully digital environment.</em></p> |
| title | Economic efficiency of dynamic pricing algorithms in the secondary car market under conditions of full digitalization |
| url | https://doi.org/10.5281/zenodo.19142901 |