Computer-implemented systems and methods for performing pricing analysis

• US 7,996,331 B1
• Filed: 08/31/2007
• Issued: 08/09/2011
• Est. Priority Date: 08/31/2007
• Status: Active Grant

• Current Assignee: SAS Institute Incorporated
• Original Assignee: SAS Institute Incorporated
• Inventors: Cheng, Hao, Wang, Yinhua, Zhong, Jie, Solanki, Rajendra Singh
• Primary Examiner(s): Hayes; John
• Assistant Examiner(s): NELSON, FREDA ANN
• Application Number:
  US11/848,636
• Time in Patent Office: 1,439 Days
• Field of Search: 705/1.1, 705/7, 705/10, 705/400, 705/7.31
• US Class Current: 705/400
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• 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

Abstract

Computer-implemented systems and methods for regular pricing optimization. A system can include decomposing a pricing situation into sub-problems. A non-linear optimization problem is solved to determine continuous optimal prices. A mixed integer linear programming problem is solved to snap prices to grid points.

Claims

1. A computer-implemented method for determining regular prices, comprising:

receiving, using one or more data processors, data about items to be priced, wherein each item has an integer pricing requirement;

using, using the one or more data processors, the received data to form one or more sub-problem item groups, wherein each sub-problem item group is formed based upon one or more pre-determined sub-problem grouping criteria, and wherein each sub-problem item group includes the received data corresponding to one or more items within that sub-problem group;

optimizing, using the one or more data processors, an objective function using a non-linear optimization program, one or more pre-selected business constraints, and the data corresponding to one or more items within a sub-problem group, wherein the non-linear optimization program ignores any of the integer pricing requirements when optimizing the objective function;

using, using the one or more data processors, the optimized objective function to attempt to generate one or more target item prices for the sub-problem item group, wherein when the one or more target item prices are not generated using the optimized objective function, successive runs of a linear optimization program are used to generate the one or more target item prices; and

determining, using the one or more data processors, one or more regular prices for the sub-problem item group using a mixed integer linear program, wherein determining includes minimizing one or more distances associated with the one or more target item prices.

2. The method of claim 1, wherein a regular price for an item is a price of the item when the item is not discounted during a promotion or clearance.

3. The method of claim 1, wherein the plurality of items includes goods available at a store.

4. The method of claim 1, wherein data is received for greater than ten thousand items.

5. The method of claim 4, wherein the items are associated with multiple stores.

6. The method of claim 1, wherein the objective function is maximized with respect to revenue or margin.

7. The method of claim 1, wherein the sub-problem grouping criteria includes grouping items that are related by cross price effects or by relationship.

8. The method of claim 7, wherein the relationship is a brand or size relationship.

9. The method of claim 1, wherein the business constraints include a lower bound for a regular price.

10. The method of claim 1, wherein the business constraints include a range for a regular price.

11. The method of claim 1, wherein the business constraints include a range for a regular price, and wherein the range is based upon a cost for an item.

12. The method of claim 1, wherein the business constraints include a range for a regular price, and wherein the range is based upon a current price for an item.

13. The method of claim 1, wherein the business constraints include one or more pre-selected end-digit rules.

14. The method of claim 13, wherein the one or more preselected end-digit rules include constraining a regular price to end in a value of nine.

15. The method of claim 1, wherein the business constraints include one or more linear equality constraints or linear inequality constraints.

16. The method of claim 1, wherein the successive runs of the linear optimization program are used when the optimized objective function is non-linear requiring excessive computing time.

17. A computer-implemented system for determining regular prices, 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 data about items to be priced, wherein each item has an integer pricing requirement;

using the received data to form one or more sub-problem item groups, wherein each sub-problem item group is formed based upon one or more pre-determined sub-problem grouping criteria, and wherein each sub-problem item group includes the received data corresponding to one or more items within that sub-problem group;

optimizing an objective function using a non-linear optimization program, one or more pre-selected business constraints, and the data corresponding to one or more items within a sub-problem group, wherein the non-linear optimization program ignores any of the integer pricing requirements when optimizing the objective function;

using the optimized objective function to attempt to generate one or more target item prices for the sub-problem item group, wherein when the one or more target item prices are not generated using the optimized objective function, successive runs of a linear optimization program are used to generate the one or more target item prices; and

determining one or more regular prices for the sub-problem item group using a mixed integer linear program, wherein determining includes minimizing one or more distances associated with the one or more target item prices.

18. A non-transitory computer-program product, tangibly embodied in a machine-readable storage medium, including instructions configured to cause a data processing apparatus to:

receive data about items to be priced, wherein each item has an integer pricing requirement;

use the received data to form one or more sub-problem item groups, wherein each sub-problem item group is formed based upon one or more pre-determined sub-problem grouping criteria, and wherein each sub-problem item group includes the received data corresponding to one or more items within that sub-problem group;

optimize an objective function using a non-linear optimization program, one or more pre-selected business constraints, and the data corresponding to one or more items within a sub-problem group, wherein the non-linear optimization program ignores any of the integer pricing requirements when optimizing the objective function;

use the optimized objective function to attempt to generate one or more target item prices for the sub-problem item group, wherein when the one or more target item prices are not generated using the optimized objective function, successive runs of a linear optimization program are used to generate the one or more target item prices; and

determine one or more regular prices for the sub-problem item group using a mixed integer linear program, wherein determining includes minimizing one or more distances associated with the one or more target item prices.