基于在线购买历史聚合的B2C电子商务差异化折扣模型
发布时间：2019-01-14 12:40
【摘要】：众多B2C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B2C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B2C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。
[Abstract]:Many B-C websites have established a membership rating system and offer price discounts to buyers accordingly. However, the membership hierarchy model only considers the buyer's transaction amount, and can not fully reflect the buyer's online purchase history of (online purchase history), so it can not accurately provide differential discount. In order to solve the above problems, a differential discount model for B2C e-commerce is proposed. The model includes four indexes: transaction, repurchase, recommended purchase, and drying order, which can reflect the buyer's online purchase history. The buyer's online purchase history is aggregated into a comprehensive value, and then the linear conversion is carried out through the min-max standardization method. The final differential discount is obtained by combining the converted aggregate value with the membership grade benchmark discount. So that B2C website can implement more accurate one-to-one marketing and price discrimination strategy to members at the same level. The simulation results based on JingDong Mall show the validity of the new model.
【作者单位】： 四川师范大学计算机科学学院;匹兹堡大学Katz商学院;四川师范大学图书与档案信息中心;
【基金】：国家自然科学基金项目(71202165) 四川省哲学社会科学规划项目(SC13C019)
【分类号】：F724.6
[Abstract]:Many B-C websites have established a membership rating system and offer price discounts to buyers accordingly. However, the membership hierarchy model only considers the buyer's transaction amount, and can not fully reflect the buyer's online purchase history of (online purchase history), so it can not accurately provide differential discount. In order to solve the above problems, a differential discount model for B2C e-commerce is proposed. The model includes four indexes: transaction, repurchase, recommended purchase, and drying order, which can reflect the buyer's online purchase history. The buyer's online purchase history is aggregated into a comprehensive value, and then the linear conversion is carried out through the min-max standardization method. The final differential discount is obtained by combining the converted aggregate value with the membership grade benchmark discount. So that B2C website can implement more accurate one-to-one marketing and price discrimination strategy to members at the same level. The simulation results based on JingDong Mall show the validity of the new model.
【作者单位】： 四川师范大学计算机科学学院;匹兹堡大学Katz商学院;四川师范大学图书与档案信息中心;
【基金】：国家自然科学基金项目(71202165) 四川省哲学社会科学规划项目(SC13C019)
【分类号】：F724.6
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