基于在线购买历史聚合的 B2 C电子商务差异化折扣模型

摘要

Many B2C websites have established their member hierarchy mechanisms to provide price discount to buyers.However, member hierarchy model only considers buyers'transaction amount, which can not reflect com-prehensively buyers'online purchase history.Hence, member hierarchy model is unable to provide accurate dif-ferentiated discount for individual.To solve the above problem, a differentiated discount model for B2C E-commerce was proposed.This model integrated four indicators which can reflect buyers’ online purchase history, including transaction, withdrawal of order, recommend-to-buy and order show; then the four indicators were aggregated into one value.By using min-max normalization, a linear conversion method, the aggregation value was combined with the base discount of member hierarchy to generate a differentiated discount for target buyer. That means the B2C website can implement more accurate one -to -one marketing and price discrimination strategy.The results of simulation experiment set in 360buy.com showed the validity of the new model.%众多B2 C网站已建立起会员等级制度，并据此向买家提供价格折扣。但会员等级制模型仅考虑买家交易金额，无法全面反映买家在线购买历史（ online purchase history），故不能准确提供差异化折扣。针对上述问题，提出了一种面向B2 C电子商务的差异化折扣模型，该模型包含能体现买家在线购买历史的交易、退单、推荐购买、晒单等四个指标，将买家在线购买历史聚合为一个综合值，进而通过min-max标准化方法进行线性转换，将转换后的聚合值与会员等级基准折扣结合得到最终的差异化折扣，从而使得B2 C网站可向同级别会员实施更精准的一对一营销和价格歧视策略。以京东商城为背景的仿真实验结果证明了本文新模型的有效性。