A Data-Driven Method to Dynamic Pricing
Unravelling Inventory and Competitor Contests With Ai in E-Commerce
Bibliographic Data
| ID | 22198136 |
|---|---|
| Authors | Shikha Vashishtha (Chaudhary Charan Singh University), Manoj Garg (0000-0002-3492-2957, Chaudhary Charan Singh University), Mani Garg, Megha Vimal (SCMS Group of Educational Institutions) |
| Year | 2024 |
| Volume | 5 |
| Issue | 6 |
| Publication date | 2024-06-30 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ShodhKosh: Journal of Visual and Performing Arts (JOURNAL) |
| Journal identifiers | ISSN: 2582-7472 • E-ISSN: 2582-7472 |
| Publisher | Granthaalayah Publications and Printers (PUBLISHER • IN) |
| DOI | 10.29121/shodhkosh.v5.i6.2024.3392 |
| OpenAlex | W4405963552 |
| Language | EN |
| References cited | 7 |
Dynamic pricing is a critical e-commerce approach that allows firms to modify prices in real time depending on demand, competition activity, and inventory levels. However, successfully adopting such tactics necessitates overcoming obstacles such as accurate demand forecasts, rival pricing monitoring, and inventory turnover optimization. This study presents a data-driven framework that integrates artificial intelligence (AI) techniques, Bayesian optimization, and rule-based systems to provide efficient, flexible pricing strategies. The system use Bayesian Optimization to dynamically balance goals like as revenue maximization, inventory management, and competitiveness, while also including rule-based procedures to assure compliance with business limitations and regulatory norms. Long Short- Term Memory (LSTM) networks are used to estimate demand by modeling temporal trends in sales data, while rival price data is watched and analyzed using web scraping and Natural Language Processing (NLP). Experimental validation of synthetic and real-world e-commerce data shows considerable gains, such as a 22% increase in revenue, a 30% decrease in inventory costs, and improved reaction to competition price. By integrating powerful optimization algorithms with realistic business principles, this framework offers a scalable, efficient, and transparent solution for dynamic pricing in competitive marketplaces. Future research will focus on tailored pricing and explainable AI (XAI) to improve consumer trust and transparency in decision-making
Business · Data science · Dynamic Pricing · E-commerce · Economics · Industrial organization · Microeconomics · Operations research · World Wide Web · Auction Theory and Applications · Computer Science · Consumer Market Behavior and Pricing · Engineering · Supply Chain and Inventory Management · Marketing
| Highly cited | No |
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