Data Mining Techniques for Dynamic Pricing Strategies in Different Business Models with Fairness and Business Ethics

Lineshah, Lakshmi

Abstract

SPARC 2022 Poster Number 10

Dynamic pricing is the determination of selling prices for manufactured goods or services whose supply is fixed or infinite and where the demand is variable. The paper focuses on developing a dynamic pricing model that works under uncertainty of demand and market. The paper presents a comprehensive survey of different types of dynamic pricing problems, the business areas that overlap within these problems and the different algorithms that are used in these areas. The different problem types are dynamic pricing problems involving infinite inventory, dynamic pricing problems with limited supply, dynamic pricing problems with competition and demand prediction, dynamic pricing with a finite time horizon and dynamic pricing for perishable products. One major challenge identified is that of developing models that work well under uncertain and varying demand conditions. The specific approach proposed is based on the Multi-Armed Bandit (MAB) framework. This research will develop a dynamic pricing model for problems where demand is uncertain using multi-armed bandit algorithm which also consider fairness and business ethics. The study explores the use of MABs algorithm for different types of dynamic problem types and identify which is the best for a problem type.

• Online Publication Date: Jun 30, 2022
• Publication Date: Jun 30, 2022
• Deposit Date: Jan 27, 2025
• DOI: https://doi.org/10.17866/rd.salford.20161871.v1
• Publisher URL: https://salford.figshare.com/articles/poster/Data_Mining_Techniques_for_Dynamic_Pricing_Strategies_in_Different_Business_Models_with_Fairness_and_Business_Ethics/20161871
• Collection Date: Jun 30, 2022