Computer-implemented systems and methods for cross-price analysis

• US 7,930,200 B1
• Filed: 11/02/2007
• Issued: 04/19/2011
• Est. Priority Date: 11/02/2007
• Status: Active Grant

• Current Assignee: SAS Institute Incorporated
• Original Assignee: SAS Institute Incorporated
• Inventors: McGuirk, Ann Mary, Chien, Yung-Hsin
• Primary Examiner(s): Jabr; Fadey S
• Application Number:
  US11/934,397
• Time in Patent Office: 1,264 Days
• Field of Search: 705/10, 705/1.1, 705/20, 705/7.31, 705/400
• US Class Current: 705/7.31
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0283 Price estimation or determination
• CPC Class Codes: G06Q 40/12 Accounting

Abstract

Computer-implemented systems and methods for determining demand of products. A system and method can be configured to determine a price with respect to a first attribute of a first product. This determination is based upon the price data of the products which compete with the first product and whose attributes are alike with respect to the first product'"'"'s attributes except for a first attribute. The determined single price is used in a mathematical model for determining demand for the first product.

Claims

1. A computer-implemented method for determining demand of products, comprising:

receiving, using one or more data processors, price data and attribute data corresponding to one or more products, wherein each product is associated with at least one attribute;

receiving, using the one or more data processors, a selection of a product;

receiving, using the one or more data processors, a selection of an attribute associated with the selected product;

comparing, using the one or more data processors, the selected product to each of the one or more products to determine a subset of the one or more products having the same associated attributes, except for the selected attribute;

determining, using the one or more data processors, a degree of difference between the selected attribute and an associated attribute for each product in the subset;

using, using the one or more data processors, the subset to determine a group of competing products, wherein each product in the group meets a pre-determined threshold corresponding to the degree of difference, and wherein each product in the group has an associated price;

using, using the one or more data processors, the prices associated with each product in the group to determine a price for the selected product;

generating, using the one or more data processors, a mathematical model using the determined price; and

using, using the one or more data processors, the mathematical model to forecast a demand for the selected product.

2. The method of claim 1, wherein the determined price for the selected product is a statistically significant variable.

3. The method of claim 1, further comprising:

receiving a selection of a second attribute associated with the selected product;

comparing the selected product to each of the one or more products to determine a second subset of the one or more products having the same associated attributes, except for the selected second attribute; and

using the prices associated with each product in the second subset to determine a second price for the selected product, wherein the mathematical model is generated using the determined price and the determined second price.

4. The method of claim 1, wherein the at least one attribute includes product brand, product type, and package size.

5. The method of claim 3, wherein the selected attribute is product brand and the selected second attribute is package size.

6. The method of claim 1, wherein the mathematical model is a linear regression model.

7. The method of claim 6, wherein the determined price is an input to the linear regression model.

8. The method of claim 6, further comprising:

using the forecasted demand to determine a predicted price for the product using an optimization model.

9. The method of claim 1, wherein the subset does not include products that differ from the selected product on two or more attributes.

10. A system for determining demand of products, comprising:

one or more processors;

one or more computer-readable storage mediums containing instructions configured to cause the one or more processors to perform operations including;

receiving price data and attribute data corresponding to one or more products, wherein each product is associated with at least one attribute,

receiving a selection of a product;

receiving a selection of an attribute associated with the selected product;

comparing the selected product to each of the one or more products to determine a subset of the one or more products having the same associated attributes, except for the selected attribute;

determining a degree of difference between the selected attribute and an associated attribute for each product in the subset;

using the subset to determine a group of competing products, wherein each product in the group meets a pre-determined threshold corresponding to the degree of difference, and wherein each product in the group has an associated price;

using the prices associated with each product in the group to determine a price for the selected product;

generating a mathematical model using the determined price; and

using the mathematical model to forecast a demand for the selected product.

11. A computer program product for determining demand of products, tangibly embodied in a machine-readable storage medium, including instructions configured to cause a data processing system to:

receive price data and attribute data corresponding to one or more products, wherein each product is associated with at least one attribute,

receive a selection of a product;

receive a selection of an attribute associated with the selected product;

compare the selected product to each of the one or more products to determine a subset of the one or more products having the same associated attributes, except for the selected attribute;

determine a degree of difference between the selected attribute and an associated attribute for each product in the subset;

use the subset to determine a group of competing products, wherein each product in the group meets a pre-determined threshold corresponding to the degree of difference, and wherein each product in the group has an associated price;

use the prices associated with each product in the group to determine a price for the selected product;

generate a mathematical model using the determined price; and

use the mathematical model to forecast a demand for the selected product.