公开(公告)号：US20140351011A1

公开(公告)日：2014-11-27

申请号：US13901009

申请日：2013-05-23

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Z. Maria WANG ,  Peter GAIDAREV

IPC: G06Q30/02

CPC classification number: G06Q30/0202

Abstract: A system that predicts promotional cross item (“PCI”) effects for retail items for a store receives historical sales data for the store and stores the historical sales data in a panel data format. The system then aggregates the stored sales data as a first level of aggregation that is aggregated to the store, a product and a time period. The system further aggregates the first level of aggregation aggregated data as a second level of aggregation that is based on a promotional cross effect attribute (“PCEA”) and is aggregated to the store, the time period and a PCEA level. The system derives PCI effect predictor variables from the second level of aggregation and, for each PCEA within a retail item family, forms a regression model. The system then generates estimated model parameters for one or more PCI effects for each PCEA from the regression models.

Abstract translation: 预测商店的零售商品的促销交叉项目（“PCI”）效应的系统接收商店的历史销售数据，并以面板数据格式存储历史销售数据。 然后，系统将存储的销售数据聚合为聚合到商店，产品和时间段的第一级聚合。 该系统进一步聚合第一级聚合聚合数据，作为基于促销交叉效应属性（“PCEA”）的聚合的第二级聚合，并聚合到商店，时间段和PCEA级别。 该系统从第二级聚合中得出PCI效应预测变量，并且对于零售项目族中的每个PCEA，形成回归模型。 然后，系统从回归模型中为每个PCEA生成一个或多个PCI效应的估计模型参数。

公开(公告)号：US20140200992A1

公开(公告)日：2014-07-17

申请号：US13740570

申请日：2013-01-14

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Z. Maria WANG ,  Peter GAIDAREV

IPC: G06Q30/02

CPC classification number: G06Q30/0246

Abstract: A system for predicting a lagged promotional effect in response to a promotion of a product in a store receives historical sales data for the product in the store and stores the historical sales data in a panel data format. The stored sales data is aggregated to the store, product and a time period. The system then trains, validates and tests one or more candidate regression models using the historical sales data, and selects one of the one or more candidate regression models based on the validating and testing. The system then scores the selected regression model to determine a sales volume change for the product after the promotion.

Abstract translation: 用于预测响应于商店中的产品的促销的滞后促销效果的系统接收商店中的产品的历史销售数据，并以面板数据格式存储历史销售数据。 存储的销售数据被聚合到商店，产品和时间段。 然后，该系统使用历史销售数据训练，验证和测试一个或多个候选回归模型，并且基于验证和测试来选择一个或多个候选回归模型中的一个。 然后，系统对所选择的回归模型进行评分，以确定促销后产品的销售量变化。