Method for Determing a Dynamic Bundle Price for a Group of Sales Products and a Computer Program Product

• US 20120203669A1
• Filed: 02/03/2011
• Published: 08/09/2012
• Est. Priority Date: 02/03/2011
• Status: Abandoned Application

• Current Assignee: prudsys AG (Fujitsu Limited)
• Original Assignee: prudsys AG (Fujitsu Limited)
• Inventors: Borsch, Alexander, Lippert, Jan
• Application Number:
  US13/020,155
• Publication Number:
  US 20120203669A1
• Time in Patent Office: Days
• US Class Current: 705/27.1
• CPC Class Codes: G06Q 30/0601 Electronic shopping [e-shopping]

Abstract

A method is provided for determining a dynamic bundle prices for a group of sales products. A computer program product and a computer system are further provided to determine the dynamic bundle price for the group of sales products by means of an application implemented on the computer system.

Claims

1. A method for determining a dynamic bundle price for a group of sales products by means of an application running on a computer system, wherein the method comprises the following steps:

using a web browser to provide electronic information pertaining to a selection of a sales product,

providing a web server to determine the group of sales products by selecting at least one additional product from a plurality of additional products and assigning it to the sales product, such that the group of sales products comprises at least one product to which a variable sales price is assigned,

having an application server of the computer system determine a price-sales function for the group of sales products,

using the computer system to determine a price elasticity for the group of sales products,

using the computer system to determine the dynamic bundle price for the group of sales products from the price-sales function and the price elasticity, and

providing electronic output information from the computer system, which displays product information pertaining to the group of sales products and displays price information pertaining to the dynamic bundle price.

2. The method according to claim 1, wherein the step of providing the web server to determine the group of sales products further comprises selecting the at least one additional product as part of a similarity selection.

3. The method according to claim 1, wherein the step of providing the web server to determine the group of sales products further comprises selecting the at least one additional product as part of a reinforcement learning selection.

4. The method according to claim 1, wherein

the step of using the computer system to determine the price elasticity further comprises determining a profit function for the group of sales products from the price-sales function, and

the step of using the computer system to determine the dynamic bundle price further comprises determining the dynamic bundle price for the group of sales products from the profit function and the price elasticity.

5. The method according to claim 1, wherein at least one of the method steps is performed as a realtime application.

6. The method according to claim 1, wherein the step of providing the electronic output information further comprises storing the electronic output information in a memory device and transmitting the electronic output information in response to a user request.

7. The method according to claim 1, wherein the dynamic bundle price for the group of sales products is updated at least once using the price-sales function and the price elasticity.

8. A method for dynamic product and price optimization by means of a computer system using an electronic database comprises the steps of:

using a web browser to provide electronic information on a first product;

providing a web server to determine whether the first product is capable of being bundled with a second product;

using the web server to determine that the first product and the second product are capable of being bundled to form a product bundle;

selecting the second product through a similarity determination made by an application server of the computer system;

using the application server to calculate a price-sales function based on a linear relationship between price and sales for the product bundle;

having the computer system determine a price elasticity for the product bundle;

using the computer system to optimize a dynamic bundle price for the product bundle based on the price-sales function and the price elasticity; and

displaying on the computer system the output of the dynamic bundle price for the product bundle.

9. The method of claim 8, wherein the step of selecting the second product further includes comparing at least one product attribute of the second product to that of the first product, the product attribute selected from the group consisting of title, category, manufacturer, and color.

10. The method of claim 9, wherein the step of selecting the second product includes using a plurality of self-learning algorithms based on reinforcement learning.

11. The method of claim 10, wherein the plurality of self-learning algorithms determines a utility function based on approximation.

12. The method of claim 8, wherein the step of using the computer system to optimize the dynamic bundle price further includes storing the dynamic bundle price in a memory device of the computer system.

13. The method of claim 8, wherein the dynamic bundle price for the product bundle is updated at least once using at least one of the price-sales function and the price elasticity.

14. A computer program product for determining a dynamic bundle price for a group of sales products by means of an application running on a computer system, the computer product comprising:

means for providing electronic information pertaining to a selection of a sales product,

means for determining the group of sales products by selecting at least one additional product from a plurality of additional products and assigning it to the sales product, such that the group of sales products comprises at least one product to which a variable sales price is assigned,

means for determining a price-sales function for the group of sales products,

means for determining a price elasticity for the group of sales products,

means for determining the dynamic bundle price for the group of sales products from the price-sales function and the price elasticity, and

means for providing electronic output information, which displays product information pertaining to the group of sales products and displays price information pertaining to the dynamic bundle price.

15. The computer program according to claim 14, wherein the mean for determining the group of sales products further includes means for selecting the at least one additional product as part of a similarity selection.

16. The computer program according to claim 14, wherein the means for determining the group of sales products further includes means for selecting the at least one additional product as part of a reinforcement learning selection.

17. The computer program according to claim 14, wherein:

the means for determining the price elasticity further includes means for determining a profit function for the group of sales products from the price-sales function, and

the means for determining the dynamic bundle price further includes means for determining the dynamic bundle price for the group of sales products from the profit function and the price elasticity.

18. The computer program according to claim 14, wherein at least one of the means is a realtime application.

19. The computer program according to claim 14, wherein the means for providing the electronic output information further include means for storing the electronic output information in a memory device and means for transmitting the electronic output information in response to a user request.

20. The computer program according to claim 14, further comprising means for updating the dynamic bundle price for the group of sales products at least once using the price-sales function and the price elasticity.