System and method for blending promotion effects based on statistical relevance

Patent metadata

Field	Value
Publication number	US-11037183-B2
Application number	US-201514955141-A
Country	US
Kind code	B2
Filing date	Dec 1, 2015
Priority date	Dec 1, 2015
Publication date	Jun 15, 2021
Grant date	Jun 15, 2021

Abstract

Systems, methods, and other embodiments are disclosed that are configured to characterize an effect on sales of a retail item due to a sales promotion. In one embodiment, first sales data for the retail item is retrieved from a plurality of stores that have applied the sales promotion for the retail item. Second sales data for the retail item is retrieved from a single store that has applied the sales promotion for the retail item. A combined promotion effect value is generated based on the first sales data and the second sales data. The combined promotion effect value characterizes an effect on sales of the retail item as sold by the single store due to the sales promotion.

First claim

What is claimed is:

1. A computer-implemented method performed by a computing device where the computing device includes at least a processor for executing instructions from a memory, the method comprising:
characterizing profitability of a retail item, due to a sales promotion for the retail item, by:
retrieving first sales data, by network communications, for the retail item from a plurality of stores that have applied the sales promotion for the retail item;
determining, by at least the processor, a first profitability estimate for the retail item as sold by the plurality of stores from the first sales data, wherein the first profitability estimate characterizes profitability of the retail item as sold by the plurality of stores due to the sales promotion;
retrieving second sales data, by the network communications, for the retail item from a single store that has applied the sales promotion for the retail item;
determining, by at least the processor, a second profitability estimate for the retail item as sold by the single store from the second sales data wherein the second profitability estimate characterizes profitability of the retail item as sold by the single store due to the sales promotion;
generating a combined profitability estimate, by at least the processor, at least by
(i) determining a first statistical confidence value associated with the first profitability estimate by performing a first regression analysis on the first sales data, wherein the first statistical confidence value represents a confidence in the first profitability estimate,
(ii) weighting the first profitability estimate by the first statistical confidence value,
(iii) determining a second statistical confidence value associated with the second profitability estimate by performing a second regression analysis on the second sales data, wherein the second statistical confidence value represents a confidence in the second profitability estimate,
(iv) weighting the second profitability estimate by the second statistical confidence value, and
(v) summing the weighted profitability estimates, wherein the combined profitability estimate characterizes profitability of the retail item as sold by the single store due to the sales promotion, and wherein the generation of the combined profitability estimate includes determining the combined profitability estimate by performing the weighting and summing of the first profitability estimate and the second profitability estimate based on the first statistical confidence value and the second statistical confidence value;

determining an amount of inventory of the retail item for the single store based at least in part on the combined profitability estimate;
generating an electronic order for the determined amount of inventory; and
placing the generated order.

2. The method of claim 1 , wherein the plurality of stores includes at least one physical store and at least one on-line store.

3. The method of claim 1 , wherein the single store is an on-line store.

4. The method of claim 1 , wherein the sales promotion includes at least one of a television advertisement promotion, a radio advertisement promotion, a newspaper advertisement promotion, an internet advertisement promotion, an email advertisement promotion, or an in-store advertisement promotion.

5. The method of claim 1 , wherein the plurality of stores includes the single store.

6. The method of claim 1 , wherein the first regression analysis is based on a demand forecast model for a class of multiple types of retail items.

7. The method of claim 1 , wherein the second regression analysis is based on a demand forecast model for a single retail item, and wherein the demand forecast model is expressed as the demand of the class of items within a single sales channel for a period of time is equal to the baseline demand for the class of items within the single sales channel for the period of time multiplied by (i) a constant to the power of a price elasticity of the class of items within the single sales channel times a price discount value of the class of items within the single sales channel for the period of time, and (ii) a product of all promotion effects active within the single sales channel during the period of time.

8. The method of claim 1 , wherein the first regression analysis is based on a demand forecast model for a plurality of stores.

9. The method of claim 1 , wherein the second regression analysis is based on a demand forecast model for a single store.

10. A non-transitory computer-readable medium storing computer-executable instructions that, when executed by a computer, cause the computer to perform functions, wherein the instructions comprise:
instructions for characterizing an effect on sales of a retail item, due to a sales promotion for the retail item, by:
retrieving first sales data for the retail item from a plurality of stores that have applied the sales promotion for the retail item;
determining, by at least the processor, a first profitability estimate for the retail item as sold by the plurality of stores from the first sales data, wherein the first profitability estimate characterizes profitability of the retail item as sold by the plurality of stores due to the sales promotion;
retrieving second sales data for the retail item from a single store that has applied the sales promotion for the retail item;
determining, by at least the processor, a second profitability estimate for the retail item as sold by the single store from the second sales data, wherein the second profitability estimate characterizes profitability of the retail item as sold by the single store due to the sales promotion;
generating a combined promotion effect value at least by
(i) determining a first statistical confidence value associated with the first profitability estimate by performing a first regression analysis on the first sales data, wherein the first statistical confidence value represents a confidence in the first profitability estimate,
(ii) weighting the first profitability estimate by the first statistical confidence value,
(iii) determining a second statistical confidence value associated with the second profitability estimate by performing a second regression analysis on the second sales data, wherein the second statistical confidence value represents a confidence in the second profitability estimate,
(iv) weighting and the second profitability estimate by the second statistical confidence value, and
(v) summing the weighted profitability estimates,
wherein the combined promotion effect value characterizes an effect on sales of the retail item as sold by the single store due to the sales promotion, and wherein the generation of the combined profitability estimate includes determining the combined profitability estimate by performing the weighting and summing of the first profitability estimate and the second profitability estimate based on the first statistical confidence value and the second statistical confidence value;

instructions for determining an amount of inventory of the retail item for the single store based at least in part on the combined profitability estimate;
instructions for generating an electronic order for the determined amount of inventory; and
instructions for placing the generated order.

11. The non-transitory computer-readable medium of claim 10 , wherein the instructions for generating the combined promotion effect value include instructions for performing a regression analysis based on a demand forecast model for a class of retail items and a plurality of stores, wherein the demand forecast model is expressed as the demand of the class of items across multiple

Assignees

• Oracle Int Corp

Inventors

• Popescu Catalin
• He Lin
• Xu Jianwu
• Lei Ming

Classifications

• G06Q30/0211 Primary

  Determining the effectiveness of discounts or incentives · CPC title