Knowledge Assisted Dynamic Pricing for Large-Scale Retailers
It is very difficult for large-scale retailers to price thousands of items dynamically reflecting all constraints and policies. To solve this problem, we adopt a combined model approach that contingently selects appropriate pricing models and integrates them. The three proposed models are cost-plus, competitor-referenced, and demand-driven models. Since each model can be converted to a set of interval and point constraints, we have developed price point determination rules, which find a price point from the weighted interval and point constraints. A prototype system, Knowledge-Assisted Pricing Assistant (KAPA) is developed with this idea. According to our experiment involving 76 cases with 54 pricing experts, KAPA performed consistently, with human experts, about 89.5% accurate. This approach can be a very effective pricing scheme in the electronic marketing era.
Retail pricing models, Expert systems, Price point determination rules
Computer Sciences | Management Information Systems
Information Systems and Management
Decision Support Systems
SUNG, Nahk Hyun and LEE, Jae Kyu.
Knowledge Assisted Dynamic Pricing for Large-Scale Retailers. (2000). Decision Support Systems. 28, (4), 347-264. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1157