Grocery scoring

• US 20060259358A1
• Filed: 05/16/2005
• Published: 11/16/2006
• Est. Priority Date: 05/16/2005
• Status: Abandoned Application

• Current Assignee: Grocery Shopping Network, Inc.
• Original Assignee: HomeTown Info, Inc.
• Inventors: Robinson, Andrew, Adam, Bill
• Application Number:
  US11/130,012
• Publication Number:
  US 20060259358A1
• Time in Patent Office: Days
• US Class Current: 705/14.410
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0242 Determining effectiveness of advertisements
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 30/0267 Wireless devices

Abstract

Providing product information to a consumer based on the likelihood the consumer will purchase the product. In one embodiment, a method recognizes a consumer. A data scoring algorithm is applied to sale products based on the shopping history of the consumer. The scoring algorithm is adapted to determine the likelihood of the consumer to purchase the sale items and display select sale products based on the data scoring algorithm.

Claims

1. A method of customizing display ads for select consumers, the method comprising:

recognizing a consumer;

applying a data scoring algorithm to sale products based on the shopping history of the consumer, the scoring algorithm being adapted to determine the likelihood of the consumer to purchase the sale items; and

displaying select sale products based on the data scoring algorithm.

2. The method of claim 1, wherein applying the data scoring algorithm further comprises:

determining levels of data scoring.

3. The method of claim 2, wherein the displaying select items further comprises:

displaying the select items that score the highest in the data scoring.

4. The method of claim 2, wherein determining the levels of data scoring includes considering at least one of exact match, level of attribute match, brand affinity and same shelf.

5. The method of claim 4, wherein the level of attribute match further comprises:

sorting the level of data scoring based on the number of matching attributes.

6. The method of claim 4, wherein the same self further comprises:

comparing location parameters of the sale item to location parameters of items in the consumer'"'"'s shopping history.

7. The method of claim 1, further comprising:

recording purchases of items by the consumer; and

placing the items purchased in the shopping history of the consumer.

8. The method of claim 1, further comprising:

determining if the consumer wants the customized sale ads displayed.

14. The method of claim 1, wherein the matching attributes includes at least one of type of product, color, flavor, size, packaging and ingredients.

9. A method of providing display ads to a consumer, the method providing:

identifying the shopping history of the consumer;

applying a data scoring algorithm to items for sale, the data scoring algorithm being adapted to score data related to each item for sale to determine if the consumer is likely to purchase the item, the scoring based at least in part on exact matches, level of attribute matches, brand affinity and product location; and

displaying items for sale to the consumer based on the data scoring algorithm.

10. The method of claim 9, wherein exact matches further comprises:

matching UPC code of the sale item to the UPC code of an item in the consumer'"'"'s shopping history.

11. The method of claim 9, wherein brand affinity further comprises:

matching brand names of the sale item to the brand name of an item in the consumer'"'"'s shopping history.

12. The method of claim 9, wherein the product location further comprises:

comparing location parameters of the sale item to location parameters of items in the consumer'"'"'s shopping history.

13. The method of claim 9, wherein the level of attribute match further comprises:

sorting the level of data scoring based on the number of matching attributes.

15. A data scoring method, the method comprising:

determining if an item for sale is an exact match with an item in a consumers shopping history;

when an exact match is determined, providing a highest score to the item for sale;

determining the number of attributes of an item for sale compared to items in the consumers shopping history;

when the number of attributes are above a select number, providing a score that is less than the highest score of an exact match;

determining brand affinity between an item for sale and items in a consumer'"'"'s shopping history;

when the brand affinity of the item for sale matches the brand affinity of items in the past history, providing a score that is less than the score provided by a number of attributes match;

determining the product location of an item for sale and comparing the location to locations of items purchase in the consumer'"'"'s shopping history; and

when the product location of an item for sale matches locations of items purchase in the consumer'"'"'s shopping history, proving a score that is less than a score provided by a brand affinity comparison.

16. The method of claim 15, wherein an exact match further comprises:

matching a UPC code of the sale item to a UPC code of an item in the consumer'"'"'s shopping history.

17. The method of claim 15, wherein matching brand affinity further comprises:

matching a brand name of the sale item to a brand name of an item in the consumer'"'"'s shopping history.

18. The method of claim 15, wherein the comparing product location further comprises:

comparing location parameters of the sale item to location parameters of items in the consumer'"'"'s shopping history.

19. The method of claim 18, wherein the location parameters include at least one of department, aisle, category and shelf.

20. The method of claim 15, wherein scoring based on the matching of attributes further comprises:

determining the level of scoring based on the number of matching attributes, wherein a high number of matching attributes corresponds to a higher scoring and a lower number of matching attributes corresponds to a lower scoring.

21. The method of claim 20, wherein the attributes includes at least one of type of product, color, flavor, size, packaging and ingredients.

22. A computer-readable medium having computer-executable instructions for performing a method comprising:

determining the shopping history of a consumer by tracking past purchases;

data scoring items for sale based on the shopping history of the consumer, the data scoring based at least in part on at least one of exact matches, number of attribute matches, brand affinity and product location; and

determining the likelihood of the consumer purchasing the sale items based on the data scoring.

23. The computer-executable instructions for performing a method of claim 22, further comprising:

displaying items for sale based on the determined likelihood the consumer purchasing the items for sale.

24. The method of claim 22, wherein an exact match further comprises:

matching a UPC code of the sale item to a UPC code of an item in the consumer'"'"'s shopping history.

25. The method of claim 22, wherein brand affinity further comprises:

matching a brand name of the sale item to a brand name of an item in the consumer'"'"'s shopping history.

26. The method of claim 22, wherein product location further comprises:

comparing location parameters of the sale item to location parameters of items in the consumer'"'"'s shopping history.

27. The method of claim 22, wherein number of attribute matches further comprises:

determining the level of scoring based on the number of matching attributes, wherein a high number of matching attributes corresponds to a higher scoring and a lower number of matching attributes corresponds to a lower scoring.

28. A method of determining the likelihood of a consumer to purchase a product, the method comprising:

a means for tracking the shopping history of a consumer;

a means for scoring items for sale based on the shopping history of the consumer, wherein the scoring is based on at least one of exact matches, number of attribute matches, brand affinity and product location; and

a means for determining the likelihood of the purchase of the item by the consumer based on the scoring of the items.

29. A method of determining the likelihood of a consumer to purchase a product, the method comprising:

creating a library of products cataloged by product location in a store;

tracking the purchase history of a consumer; and

evaluating items for sale to determine if any of the items for sale have a similar product location as products tracked in the purchase history of the consumer.

30. The method of claim 29, further comprising:

displaying sale items with similar product locations based on the evaluation.

31. The method of claim 29, wherein the product location includes at least one of department, aisle, category and shelf.