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The research aims to analyze the effectiveness of dynamic pricing algorithms and their impact on consumer responses in digital marketplaces.

April 22, 2026Iconic Research and Engineering Journals

Dynamic Pricing Strategies in Digital Markets: Real-Time Algorithm Effectiveness and Consumer Response

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The research aims to analyze the effectiveness of dynamic pricing algorithms and their impact on consumer responses in digital marketplaces.
Utilized a comprehensive dataset from e-commerce platforms between 2020-2023.
Evaluated performance of machine learning algorithms such as Gradient Boosting Machines, Random Forest, and Neural Networks.
Analyzed consumer response considering price individualization levels and privacy concerns.
Gradient Boosting Machines achieved the highest accuracy with an R-squared score of 0.92 and Mean Squared Error of 0.012.
Businesses using advanced dynamic pricing report a 6-9% increase in revenue.
Consumer perceptions on fairness pose challenges for dynamic pricing effectiveness.

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Evaluates dynamic pricing algorithms' effectiveness and consumer reactions, suggesting revenue impacts for businesses.

Nikhar et al. (2025) studied this question.

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