Knowledge assisted dynamic pricing for large-scale retailers

摘要

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.