Home / Current Issue / Paper 1716695
Dynamic Pricing Strategies in Digital Markets: Real-Time Algorithm Effectiveness and Consumer Response
Subject area: Management and Commerce · Area of research: Dynamic Pricing Strategies
DOI: https://doi.org/10.64388/IREV8I10-1716695
Abstract
Dynamic pricing has emerged as a critical revenue optimization strategy in digital markets, enabling businesses to adjust prices in real-time based on various market factors. This study examines the effectiveness of dynamic pricing algorithms and analyzes consumer responses in digital marketplaces. Using a comprehensive dataset from e-commerce platforms spanning 2020-2023, we evaluate the performance of machine learning algorithms including Gradient Boosting Machines (GBM), Random Forest, and Neural Networks. Results indicate that GBM achieves the highest accuracy with an R-squared score of 0.92 and Mean Squared Error of 0.012. Consumer response analysis reveals significant variations based on price individualization levels and privacy concerns. Businesses implementing sophisticated dynamic pricing systems report 6-9% revenue increases, though consumer fairness perceptions remain a critical challenge. This research provides empirical evidence for algorithm selection and consumer strategy considerations in dynamic pricing implementations.
Keywords
Dynamic Pricing, Machine Learning, Consumer Behavior, E-Commerce, Algorithmic Pricing, Revenue Optimization
How to cite this paper
@article{1716695,
author = {Dr. Pooja Piyush Nikhar, Shruti Ashok Das, Dr. Deepti Prashant Lele},
title = {Dynamic Pricing Strategies in Digital Markets: Real-Time Algorithm Effectiveness and Consumer Response},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {10},
pages = {1855-1860},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1716695.pdf},
abstract = {Dynamic pricing has emerged as a critical revenue optimization strategy in digital markets, enabling businesses to adjust prices in real-time based on various market factors. This study examines the effectiveness of dynamic pricing algorithms and analyzes consumer responses in digital marketplaces. Using a comprehensive dataset from e-commerce platforms spanning 2020-2023, we evaluate the performance of machine learning algorithms including Gradient Boosting Machines (GBM), Random Forest, and Neural Networks. Results indicate that GBM achieves the highest accuracy with an R-squared score of 0.92 and Mean Squared Error of 0.012. Consumer response analysis reveals significant variations based on price individualization levels and privacy concerns. Businesses implementing sophisticated dynamic pricing systems report 6-9% revenue increases, though consumer fairness perceptions remain a critical challenge. This research provides empirical evidence for algorithm selection and consumer strategy considerations in dynamic pricing implementations.},
keywords = {Dynamic Pricing, Machine Learning, Consumer Behavior, E-Commerce, Algorithmic Pricing, Revenue Optimization},
month = {April},
doi = {https://doi.org/10.64388/IREV8I10-1716695}
}