System and method for assisting customers in choosing a bundled set of commodities using customer preferences

• US 7,430,531 B1
• Filed: 05/30/2000
• Issued: 09/30/2008
• Est. Priority Date: 02/04/2000
• Status: Expired due to Term

• Current Assignee: Globys Inc
• Original Assignee: VeriSign, Inc.
• Inventors: Snyder, Scott Andrew
• Primary Examiner(s): Subramanian; Narayanswamy
• Application Number:
  US09/580,448
• Time in Patent Office: 3,045 Days
• Field of Search: 705 35- 37, 705/5, 705/10, 705/16
• US Class Current: 705/35
• CPC Class Codes: G06Q 30/0207 Discounts or incentives, e.g. coupons or rebates
• CPC Class Codes: G06Q 30/0601 Electronic shopping [e-shopping]
• CPC Class Codes: G06Q 30/0603 Catalogue ordering
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis

Abstract

A system and method for assisting a customer in choosing a combination of commodities based on preferences of the customer. A combination is a set of related commodities, wherein bundling discounts may be applied to particular bundles of related commodities. Combination options are created by optimizing the categories within the combination in parallel, and then selecting the best value options from each category into a grouping. The effective cost of a grouping is calculated as a total of the effective costs of each option within the grouping. The effective costs consider weighted values of performance features in addition to the actual cost of a commodity. The groupings are ranked and presented to the user, so that the user may select a grouping as combination of commodities for purchase.

Claims

1. A method for assisting a customer in choosing a combination of commodities, wherein the commodities are organized into N categories, wherein a combination is composed of one commodity selected from each category, and each commodity category has at least two commodity options, and wherein N is an integer greater than one, the method comprising:

(a) ranking, using a computer, the options within each commodity category based, in part, on at least one optimization parameter;

(b) creating, with the computer, at least N combinations of commodity options by;

for each commodity category K of the N commodity categories, creating a corresponding combination K by;

(i) selecting a highest ranked option for the Kth commodity category;

(ii) automatically selecting any options in other commodity categories that are linked to the option selected in step(b)(i), wherein the other commodity categories includes any commodity category except the Kth commodity category;

(iii) selecting a valid option for each of the remaining commodity categories, wherein a remaining commodity category is any commodity category that has not had an option selected in steps (b)(i) or (b)(ii) for the Kth combination, wherein K is an integer running from 1 to N;

(c) calculating a total cost of each combination of commodity options; and

(d) presenting the at least N combinations of commodity options to the customer, whereby the customer selects a combination of commodity options for purchase.

2. The method of claim 1, further comprising the steps of:

visiting a web site by the customer; and

sending the preferences of the customer to the web site.

3. The method of claim 1, wherein step (b) comprises the additional steps of:

(iv) selecting a next ranked option for a commodity category;

(v) selecting any options in other commodity categories that are linked to the option selected in step (b)(iv); and

(vi) selecting valid options for remaining commodity categories until the combination of commodity options is complete.

4. The method of claim 3, wherein steps (b)(iv), (b)(v) and (b)(vi) are repeated for a plurality of ranks.

5. The method of claim 1, wherein step (d) comprises presenting the combinations of commodity options ranked by the total cost.

6. The method of claim 1, wherein step (a) comprises ranking the options within each category by effective cost.

7. The method of claim 1, wherein step (a) comprises calculating an effective cost for each option and ranking the options within each category by effective cost.

8. The method of claim 7, wherein the effective cost calculations include bundling discounts.

9. The method of claim 7, wherein step (c) comprises calculating a total effective cost for each of the combinations of commodity options by adding the effective costs of the selected options in the combinations of commodity options.

10. The method of claim 7, wherein step (a) comprises the steps of, for each category:

(i) identifying at least one first parameter associated with a commodity option;

(ii) associating at least one value to the at least one first parameters;

(iii) calculating an estimated cost of the commodity option based on features of the commodity category that are desired by the customer;

(iv) obtaining from the customer a preference weighting on at least one second parameter;

(v) calculating an effective cost of the commodity option by adjusting the estimated cost based on the preference weighting and the at least one value assigned to the parameters; and

(vi) ranking the options within each category by effective cost.

11. The method of claim 10, wherein the parameter is a feature, an attribute, or a performance characteristic associated with the commodity category.

12. The method of claim 10, wherein step (a)(ii) includes the steps of:

setting a range for the at least one first parameter;

sampling a random set of customers over the range; and

determining a best fit utility function using regression analysis on data received as a result of sampling.

13. The method of claim 12, wherein the utility function is stored in a first database, information about the commodity including bundling links is stored in a second database, and information about the customer is stored in a third database.

14. The method of claim 13, wherein the combinations created in step (b) are saved in the second database.

15. The method of claim 12, wherein the utility function is evaluated to obtain the at least one value, wherein the value represents a cost or benefit of the parameter to the customer.

16. The method of claim 15, wherein the value is subtracted from the estimated cost if it represents a benefit to the customer or the value is added to the estimated cost if it represents a cost to the customer.

17. The method of claim 1, wherein the commodity categories that are included in the combination are predefined.

18. The method of claim 1, wherein the commodity categories are services.

19. The method of claim 18, wherein the services include telephone service plans.

20. The method of claim 1, wherein the commodity are products and services.

21. The method of claim 20, wherein the commodity categories include wireless telephone services plans and handsets.

22. The method of claim 1, wherein step (d) comprises presenting the combinations of commodity options to the customer, whereby the customer selects a portion of a combination of commodity options for purchase.

23. The method of claim 1, wherein the optimization parameter comprises a utility function.

24. The method of claim 23, wherein the utility function is determined at least in part based on a regression analysis.

25. The method of claim 24, wherein the regression analysis employs at least one constant to determine the utility function.

26. The method of claim 23, wherein the utility function represents at least one of a cost or a benefit.

27. A computer-readable medium encoded with a plurality of instructions for controlling a computing system to perform an operation for assisting a customer in choosing a combination of commodities, wherein the commodities are organized into N categories, wherein a combination is composed of one commodity selected from each category, and each commodity category has at least two commodity options, and wherein N is an integer greater than one, the operation comprising:

(a) ranking the options within each commodity category based, in part, on at least one optimization parameter;

(b) creating at least N combinations of commodity options by;

for each commodity category K of the N commodity categories, creating a corresponding combination K by;

(i) selecting a highest ranked option for the Kth commodity category;

(ii) automatically selecting any options in other commodity categories that are linked to the option selected in step(b)(i), wherein the other commodity categories includes any commodity category except the Kth commodity category;

(iii) selecting a valid option for each of the remaining commodity categories, wherein a remaining commodity category is any commodity category that has not had an option selected in steps (b)(i) or (b)(ii) for the Kth combination, wherein K is an integer running from 1 to N;

(c) calculating a total cost of each combination of commodity options; and

(d) presenting the at least N combinations of commodity options to the customer, whereby the customer selects a combination of commodity options for purchase.

28. The computer-readable medium of claim 27, wherein the operation further comprises:

receiving a visit to a web site by the customer; and

sending the preferences of the customer to the web site.

29. The computer-readable medium of claim 27, wherein the creating the at least N commodity combinations additionally comprises:

selecting a next ranked option for a commodity category;

selecting any options in other commodity categories that are linked to the option selected by the selecting a next ranked option; and

selecting valid options for remaining commodity categories until the combination of commodity options is complete.

30. The computer-readable medium of claim 27, wherein the presenting the combinations comprises presenting the combinations of commodity options ranked by the total cost.

31. The computer-readable medium of claim 27, wherein the ranking the options comprises ranking the options within each category by effective cost.

32. The computer-readable medium of claim 27, wherein the ranking the options comprises calculating an effective cost for each option and ranking the options within each category by effective cost.

33. The computer-readable medium of claim 32, wherein the effective cost calculations include bundling discounts.

34. The computer-readable medium of claim 32, wherein the calculating a total cost of each combination comprises calculating a total effective cost for each of the combinations of commodity options by adding the effective costs of the selected options in the combinations of commodity options.

35. The computer-readable medium of claim 32, wherein the ranking the options comprises, for each category:

identifying at least one first parameter associated with a commodity option;

associating at least one value to the at least one first parameter;

calculating an estimated cost of the commodity option based on features of the commodity category that are desired by the customer;

obtaining from the customer a preference weighting on at least one second parameter;

calculating an effective cost of the commodity option by adjusting the estimated cost based on the preference weighting and the at least one value assigned to the parameters; and

ranking the options within each category by effective cost.

36. The computer-readable medium of claim 35, wherein the parameter is a feature, an attribute, or a performance characteristic associated with the commodity category.

37. The computer-readable medium of claim 35, wherein the associating at least one value includes:

setting a range for the at least one first parameter;

sampling a random set of customers over the range; and

determining a best fit utility function using regression analysis on data received as a result of sampling.

38. The computer-readable medium of claim 37, wherein the operation further comprises:

storing the utility function in a first database;

storing information about the commodity including bundling links in a second database; and

sorting information about the customer in a third database.

39. The computer-readable medium of claim 38, wherein the operation further comprises saving the combinations in the second database.

40. The computer-readable medium of claim 37, wherein the operation further comprises evaluating the utility function to obtain the at least one value, wherein the value represents a cost or benefit of the parameter to the customer.

41. The computer-readable medium of claim 40, wherein the value is subtracted from the estimated cost if it represents a benefit to the customer or the value is added to the estimated cost if it represents a cost to the customer.

42. The computer-readable medium of claim 27, wherein the operation further comprises predefining the commodity categories that are included in the combination.

43. The computer-readable medium of claim 27, wherein the presenting the combinations of commodity options additionally comprises providing to the customer means to select a portion of a combination of commodity options for purchase.

44. The computer-readable medium of claim 27, wherein the optimization parameter comprises a utility function.

45. The computer-readable medium of claim 44, wherein the utility function is determined at least in part based on a regression analysis.

46. The computer-readable medium of claim 45, wherein the regression analysis employs at least one constant to determine the utility function.

47. The computer-readable medium of claim 44, wherein the utility function represents at least one of a cost or a benefit.