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Journal code(s)
- E-ISSN Number: 2277-7695
- P-ISSN Number: 2349-8242
- CODEN Code: PIHNBQ
- ZDB-Number: 2663038-2
- IC Journal ID: 7725
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Vol. 8, Special Issue 2 (2019)
Deep reinforcement learning in dynamic pricing strategies: A review of applications in sales prediction
Author(s):
VK Pallaw, Manish Singh and Sudershan Singh
VK Pallaw, Manish Singh and Sudershan Singh
Abstract:
Dynamic pricing strategies have gained prominence in today's highly competitive business landscape, and leveraging advanced technologies such as Deep Reinforcement Learning (DRL) has emerged as a promising avenue to optimize these strategies. This review paper systematically examines the applications of DRL in dynamic pricing, focusing specifically on its role in sales prediction. With an increasing volume of data generated by online transactions, businesses are presented with the opportunity to enhance decision-making processes through the integration of DRL algorithms.
Dynamic pricing strategies have gained prominence in today's highly competitive business landscape, and leveraging advanced technologies such as Deep Reinforcement Learning (DRL) has emerged as a promising avenue to optimize these strategies. This review paper systematically examines the applications of DRL in dynamic pricing, focusing specifically on its role in sales prediction. With an increasing volume of data generated by online transactions, businesses are presented with the opportunity to enhance decision-making processes through the integration of DRL algorithms.
The paper begins by providing an overview of dynamic pricing, emphasizing its significance in adapting to market fluctuations and maximizing revenue. Subsequently, it delves into the foundational principles of Deep Reinforcement Learning, elucidating how this subset of machine learning facilitates decision-making in complex, uncertain environments. The intersection of dynamic pricing and DRL is explored through a comprehensive analysis of existing literature, highlighting the diverse applications and their impact on sales prediction.
The review identifies key challenges in traditional pricing strategies, including the limitations in adapting to real-time market dynamics and predicting consumer behavior accurately. DRL, with its ability to learn from data and adjust strategies iteratively, offers a solution to these challenges. The paper examines various DRL models employed in dynamic pricing, such as Q-learning and policy gradient methods, assessing their effectiveness in capturing the intricate patterns of consumer behavior.
Moreover, the review assesses the practical implications of implementing DRL in sales prediction, discussing case studies across industries to showcase successful applications and elucidate potential areas for improvement. It explores how DRL can contribute to personalized pricing strategies, tailoring offers to individual customers based on their preferences and historical interactions.
Pages: 10-14 | 720 Views 487 Downloads
How to cite this article:
VK Pallaw, Manish Singh and Sudershan Singh. Deep reinforcement learning in dynamic pricing strategies: A review of applications in sales prediction. The Pharma Innovation Journal. 2019; 8(2S): 10-14. DOI: 10.22271/tpi.2019.v8.i2Sa.25242
Journal code(s)
- E-ISSN Number: 2277-7695
- P-ISSN Number: 2349-8242
- CODEN Code: PIHNBQ
- ZDB-Number: 2663038-2
- IC Journal ID: 7725
Main Menu
Special Issue
Copyright Form
Identifier
The Pharma Innovation Journal
