Methods and System for Providing and Analyzing Local Targeted Advertising Campaigns

• US 20120005016A1
• Filed: 06/30/2010
• Published: 01/05/2012
• Est. Priority Date: 06/30/2010
• Status: Abandoned Application

• Current Assignee: Uri Graff
• Original Assignee: Uri Graff
• Inventors: Graff, Uri
• Application Number:
  US12/827,350
• Publication Number:
  US 20120005016A1
• Time in Patent Office: Days
• US Class Current: 705/14.49
• CPC Class Codes: G06Q 30/0242 Determining effectiveness of advertisements
• CPC Class Codes: G06Q 30/0251 Targeted advertisements
• CPC Class Codes: G06Q 30/0641 Shopping interfaces

Abstract

The present invention relates to methods and a system for providing a local targeted advertising campaign with regard to at least one predefined physical site, one of said method comprising: (a) providing a semantic network having a plurality of segmentation paths, wherein each segmentation path is related to a predefined physical site and includes at least two interconnected nodes; (b) providing at least one metrics to a node from said at least two nodes; (c) inheriting said at least one metrics from said node to a higher-level node from among said at least two interconnected nodes; and (d) providing a local targeted advertising campaign with regard to at least one predefined physical site based on said inheriting of said at least one metrics.

Claims

1. A method of providing a local targeted advertising campaign with regard to at least one predefined physical site, said method comprising:

a) providing a semantic network having a plurality of segmentation paths, wherein each segmentation path is related to a predefined physical site and includes at least two interconnected nodes;

b) providing at least one metrics to a node from said at least two nodes;

c) inheriting said at least one metrics from said node to a higher-level node from among said at least two interconnected nodes; and

d) providing a local targeted advertising campaign with regard to at least one predefined physical site based on said inheriting of said at least one metrics.

2. The method according to claim 1, further comprising defining the semantic network by the following:

a) providing a spatial structure of physical locations of a plurality of entities within the predefined physical site;

b) providing a marketing environment of said predefined physical site, said marketing environment comprising data related to one of advertisements and sale promotions provided within said predefined physical site; and

c) providing a plurality of advertisers, which enable to interlink between said spatial structure with said marketing environment.

3. The method according to claim 2, wherein the spatial structure comprises at least one of the following:

a) a building;

a) a building floor;

b) open spaces;

c) cells;

d) cell layers; and

e) walking paths.

4. The method according to claim 2, wherein the local marketing environment comprises at least one of the following:

a) a plurality of targeted content data objects; and

b) a plurality of category objects.

5. The method according to claim 2, wherein the advertiser is related to at least one of the following:

a) a point of sale provider;

b) a service provider;

c) a product provider;

d) an advertising campaign provider; and

e) a local market operator.

6. The method according to claim 1, wherein the semantic network is dynamically updated.

7. The method according to claim 1, wherein the predefined physical site is a predefined shopping site.

8. The method according to claim 7, further comprising assigning each consumer with a unique identification number (ID), when said anonymous consumer connects to the predefined shopping site over the semantic network, thereby enabling said consumer to remain the anonymous consumer of said predefined shopping site.

9. The method according to claim 8, further comprising calculating the at least one metrics with regard to the behavior of the anonymous consumer who is connected to the predefined shopping site.

10. The method according to claim 7, further comprising calculating the at least one metrics with regard to a group of consumers of the predefined shopping site.

11. The method according to claim 2, further comprising providing at least one report to the plurality of advertisers with regard to the at least one calculated metrics.

12. The method according to claim 7, further comprising analyzing the a result of the at least one calculated metrics for determining a plurality of factors that influenced on said result, said factors comprising:

a) the location of the predefined physical site;

b) the location the point-of-sale (POS) within said predefined physical site;

c) the location of a particular region of each point-of-sale;

d) the spatial structure of said predefined physical site;

e) at least one targeted content item provided at the predefined physical site;

f) the location of a product or service related to at least one target content item provided within the predefined physical site;

g) a category of said targeted content item; and

h) the calendar time interval, to which the metrics is related.

13. The method according to claim 12, further comprising providing at least one report to the plurality of advertisers with regard to the analyzing of the result of the at least one metrics.

14. The method according to claim 13, further comprising providing the at least one report substantially in real-time.

15. The method according to claim 14, further comprising informing a plurality of consumers regarding a sale promotion for one of at least one product and service substantially in real-time.

16. The method according to claim 2, further comprising enabling to compare a plurality of metrics with regard to the semantic network and with regard to the at least one predefined physical site.

17. The method according to claim 1, wherein the metrics is selected from at least one of the following:

a) a statistical metrics;

b) a segmentation path rank metrics;

c) a personal consumer preferences rank metrics;

d) a consumer community preference rank metrics;

e) a target distance metrics; and

f) a market response metrics.

18. The method according to claim 1, further comprising providing the effectiveness rank of the local targeted campaign based on the at least one metrics.

35. The method according to claim 1, further comprising providing the effectiveness rank of the local targeted campaign based on the at least one metrics.

19. A method of providing a local targeted advertising campaign with regard to at least one predefined physical site, said method comprising:

a) providing a semantic network having a plurality of segmentation paths, wherein each segmentation path is related to a predefined physical site and includes at least two interconnected nodes, said semantic network defined by;

a.1. providing a spatial structure of physical locations of a plurality of entities within said predefined physical site;

a.2. providing a marketing environment of said predefined physical site, said marketing environment comprising data related to one of advertisements and sale promotions provided within said predefined physical site; and

a.3. providing a plurality of advertisers, which enable to interlink between said spatial structure with said marketing environment;

b) defining each node within said at least two interconnected nodes as one of a category, giving rise to a higher-level node, and a sub-category, giving rise to a lower-level node;

c) calculating the at least one metrics of the targeted advertising campaign by utilizing said semantic network, while providing at least one metrics to a node from said at least two nodes and inheriting said at least one metrics from said node to a higher-level node from among the at least two interconnected nodes; and

d) providing a local targeted advertising campaign with regard to the at least one predefined physical site based on the inheriting of said at least one metrics.

20. The method according to claim 19, wherein the spatial structure comprises at least one of the following:

a) a building;

b) a building floor;

c) open spaces;

d) cells;

e) cell layers; and

f) walking paths.

21. The method according to claim 19, wherein the local marketing environment comprises at least one of the following:

a) a plurality of targeted content data objects; and

b) a plurality of category objects.

22. The method according to claim 19, wherein the advertiser is related to at least one of the following:

a) a point of sale provider;

b) a service provider;

c) a product provider;

d) an advertising campaign provider; and

e) a local market operator.

23. The method according to claim 19, wherein the semantic network is dynamically updated.

24. The method according to claim 19, wherein the predefined physical site is a predefined shopping site.

25. The method according to claim 24, further comprising assigning each consumer with a unique identification number (ID), when said anonymous consumer connects to the predefined shopping site over the semantic network, thereby enabling said consumer to remain the anonymous consumer of said predefined shopping site.

26. The method according to claim 25, further comprising calculating the at least one metrics with regard to the behavior of the anonymous consumer who is connected to the predefined shopping site.

27. The method according to claim 24, further comprising calculating the at least one metrics with regard to a group of consumers of the predefined shopping site.

28. The method according to claim 19, further comprising providing at least one report to the plurality of advertisers with regard to the at least one calculated metrics.

29. The method according to claim 19, further comprising analyzing the a result of the at least one calculated metrics for determining a plurality of factors that influenced on said result, said factors comprising:

a) the location of the predefined physical site;

b) the location the point-of-sale (POS) within said predefined physical site;

c) the location of a particular region of each point-of-sale;

d) the spatial structure of said predefined physical site;

e) at least one targeted content item provided at the predefined physical site;

f) the location of a product or service related to at least one target content item provided within the predefined physical site;

g) a category of said targeted content item; and

h) the calendar time interval, to which the metrics is related.

30. The method according to claim 29, further comprising providing at least one report to the plurality of advertisers with regard to the analyzing of the result of the at least one metrics.

31. The method according to claim 30, further comprising providing the at least one report substantially in real-time.

32. The method according to claim 31, further comprising informing a plurality of consumers regarding a sale promotion for one of at least one product and service substantially in real-time.

33. The method according to claim 19, further comprising enabling to compare a plurality of metrics with regard to the semantic network and with regard to the at least one predefined physical site.

34. The method according to claim 19, wherein the metrics is selected from at least one of the following:

a) a statistical metrics;

b) a segmentation path rank metrics;

c) a personal consumer preferences rank metrics;

d) a consumer community preference rank metrics;

e) a target distance metrics; and

f) a market response metrics.

36. A method of enabling to perform a consolidation with regard to a predefined physical site, said method comprising:

a) providing a semantic network having a plurality of segmentation paths, wherein each segmentation path is related to a predefined physical site and includes at least two interconnected nodes;

b) determining at least two similar segmentation paths within said semantic network, according to at least one predefined criterion; and

c) performing a consolidation of at least two nodes of each of said at least two similar segmentation paths, based on said predefined criterion, giving rise to at least one consolidated segmentation path.

37. The method according to claim 36, wherein the at least one predefined criterion relates to at least one of the following:

a) a typo error;

b) a marketing filtering; and

c) a praise words usage.

38. The method according to claim 37, wherein the marketing filtering relates to the filtering of at least two similar market segments.

39. The method according to claim 36, further comprising defining the semantic network by the following:

a) providing a spatial structure of physical locations of a plurality of entities within the predefined physical site;

b) providing a marketing environment of said predefined physical site, said marketing environment comprising data related to one of advertisements and sale promotions provided within said predefined physical site; and

c) providing a plurality of advertisers, which enable to interlink between said spatial structure with said marketing environment.

40. The method according to claim 39, wherein the spatial structure comprises at least one of the following:

a) a building;

b) a building floor;

c) open spaces;

d) cells;

e) cell layers; and

f) walking paths.

41. The method according to claim 39, wherein the local marketing environment comprises at least one of the following:

a) a plurality of targeted content data objects; and

b) a plurality of category objects.

42. The method according to claim 39, wherein the advertiser is related to at least one of the following:

a) a point of sale provider;

b) a service provider;

c) a product provider;

d) an advertising campaign provider;

e) a local market operator.

43. The method according to claim 36, wherein the semantic network is dynamically updated.

44. The method according to claim 36, further comprising calculating at least one metrics of at least one of the following:

a) at least one segmentation path;

b) at least one consolidated segmentation path;

c) at least one node of said at least one segmentation path; and

d) at least one node of said at least one consolidated segmentation path.

45. The method according to claim 44, further comprising inheriting the at least one metrics between nodes of each segmentation path.

46. The method according to claim 36, wherein the nodes of each segmentation path form a hierarchical structure.

47. The method according to claim 36, wherein each node of the each segmentation path is one of the following:

a) a category, thereby the node as a higher-level node; and

b) a sub-category, thereby the node as a lower-level node.

48. The method according to claim 47, further comprising performing the consolidation of one of the category and sub-category.

49. The method according to claim 36, further comprising removing the segmentation paths which are at least one of the following:

a) not used for a predefined period of time;

b) are determined as redundant; and

c) are determined as non-effective.

50. A server configured to perform at least one metrics of a targeted advertising campaign by utilizing a semantic network with regard to a predefined physical site, said server comprising:

a) a local network database configured to store a semantic network that comprises a plurality of segmentation paths, wherein each segmentation path is related to a predefined physical site and includes at least two interconnected nodes, and wherein said semantic network is defined by;

a.1. a spatial structure of physical locations of a plurality of entities within said predefined physical site;

a.2. a marketing environment of said predefined physical site, said marketing environment comprising data related to one of advertisements and sale promotions provided within said predefined physical site; and

a.3. a plurality of advertisers, which enable to interlink between said spatial structure with said marketing environment; and

b) a targeted campaign analyzing unit configured to enable the advertisers to assess and control their advertising campaigns, and enabling to calculate at least one metrics of the targeted advertising campaign over said semantic network.

51. The server according to claim 50, wherein the spatial structure comprises at least one of the following:

a) a building;

b) a building floor;

c) open spaces;

d) cells;

e) cell layers; and

f) walking paths.

52. The server according to claim 50, wherein the local marketing environment comprises at least one of the following:

a) a plurality of targeted content data objects; and

b) a plurality of category objects.

53. The server according to claim 50, wherein the advertiser is related to at least one of the following:

a) a point of sale provider;

b) a service provider;

c) a product provider;

d) an advertising campaign provider; and

e) a local market operator.

54. The server according to claim 50, further comprising a search engine unit configured to enable conducting a search for at least one item over the semantic network.

55. The server according to claim 50, further comprising a locator unit configured to acquire locations of consumers'"'"' mobile devices.

56. The server according to claim 55, wherein the locator unit further is configured to identify anonymous consumers based on the acquired locations.

57. The server according to claim 50, further comprising a data acquiring unit configured to gather targeting information provided by a plurality of advertisers.

58. The server according to claim 57, wherein the data acquiring unit further processes and integrates the targeting information within the local network database.

59. The server according to claim 50, further comprising a shopping map unit configured to provide contextual information to a plurality of consumers by using location base services (LBS).

60. The server according to claim 50, further comprising a local content exposure unit configured to enable a plurality of consumers to operate the contextual information.

61. The server according to claim 50, further comprising a consumer preference unit configured to perform at least one of the following:

a) gather selections of each consumer, which are made by the mobile device of said each consumer, giving a rise to the gathered consumer selections;

b) analyze the gathered consumer selections and transform these selections to one or more consumer preferences; and

c) store said consumer preferences within a consumers preferences database.

62. The server according to claim 50, further comprising a targeted campaign monetization unit configured to monetize targeted campaigns by measuring traffic of the contextual information.

63. The server according to claim 50, further comprising an advertisers'"'"' database configured to store advertisers'"'"' details for managing and billing the advertisers'"'"' accounts.

64. The server according to claim 50, further comprising a local schedule unit for enabling synchronizing at least a portion of units, which are one of provided and connected to said server.

65. The server according to claim 50, wherein the metrics is selected from at least one of the following:

a) a statistical metrics;

b) a segmentation path rank metrics;

c) a personal consumer preferences rank metrics;

d) a consumer community preference rank metrics;

e) a target distance metrics; and

f) a market response metrics.

66. The server according to claim 50, wherein said server further enables to:

a) provide the at least one metrics to a node from the at least two nodes; and

b) inherit said at least one metrics from said node to a higher-level node from among said at least two interconnected nodes.

67. A system configured to provide a local targeted advertising campaign with regard to at least one predefined physical site , said system comprising at least one server of claim 50.

68. The system according to claim 67, further comprising a remote consumer services apparatus configured to enable connecting mobile devices of a plurality of consumers to the at least one server.

69. The system according to claim 67, further comprising a remote advertiser services apparatus configured to enable connecting devices of a plurality of advertisers to the at least one server.

70. A method of awarding a consumer based on the consumer'"'"'s activity with regard to a predefined physical site, said activity performed by means of the consumer'"'"'s mobile device, said method comprising:

a) providing a semantic network having a plurality of segmentation paths, wherein each segmentation path is related to a predefined physical site and includes at least two interconnected nodes, while each node within said at least two interconnected nodes is one of a category, giving rise to a higher-level node, and a sub-category, giving rise to a lower-level node;

b) enabling the consumer to perform an activity with regard to said predefined physical site and said semantic network; and

c) award said consumer based on said activity.

71. The method according to claim 70, wherein the awarding of the consumer is performed by an advertiser.

72. The method according to claim 70, wherein the activity is related to with the semantic network and to the predefined physical site.

73. The method according to claim 70, wherein the awarding comprises providing to the consumer at least one of the following:

a) a coupon;

b) a discount;

c) a voucher; and

d) a gift.

74. The method according to claim 70, further comprising defining the semantic network by the following:

a) providing a spatial structure of physical locations of a plurality of entities within the predefined physical site;

b) providing a marketing environment of said predefined physical site, said marketing environment comprising data related to one of advertisements and sale promotions provided within said predefined physical site; and

c) providing a plurality of advertisers, which enable to interlink between said spatial structure with said marketing environment.

75. The method according to claim 74, wherein the spatial structure comprises at least one of the following:

a) a building;

b) a building floor;

c) open spaces;

d) cells;

e) cell layers; and

f) walking paths.

76. The method according to claim 74, wherein the local marketing environment comprises at least one of the following:

a) a plurality of targeted content data objects; and

b) a plurality of category objects.

77. The method according to claim 74, wherein the advertiser is related to at least one of the following:

a) a point of sale provider;

b) a service provider;

c) a product provider;

d) an advertising campaign provider; and

e) a local market operator.

78. The method according to claim 70, wherein the semantic network is dynamically updated.

79. The method according to claim 70, further comprising assigning each consumer with a unique identification number (ID), when said anonymous consumer is connected the predefined shopping site over the semantic network, thereby enabling said consumer to remain the anonymous consumer of said predefined shopping site.

80. The method according to claim 79, further comprising calculating the at least one metrics with regard to the behavior of the anonymous consumer within the predefined shopping site.

81. The method according to claim 80, wherein the metrics is selected from at least one of the following:

a) a statistical metrics;

b) a segmentation path rank metrics;

c) a personal consumer preferences rank metrics;

d) a consumer community preference rank metrics;

e) a target distance metrics; and

f) a market response metrics.

82. The method according to claim 70, further comprising calculating the at least one metrics with regard to a group of consumers of the predefined shopping site.

83. The method according to claim 80, further comprising providing at least one report to the plurality of advertisers with regard to the at least one calculated metrics.

84. The method according to claim 80, further comprising analyzing the a result of the calculated at least one metrics for determining a plurality of factors that influenced on said result, said factors comprising:

a) the location of the predefined physical site;

b) the location the point-of-sale (POS) within said predefined physical site;

c) the location of a particular region of each point-of-sale;

d) the spatial structure of said predefined physical site;

e) at least one targeted content item being sold within the predefined physical site;

f) a category of said targeted content item; and

g) the calendar time interval, to which the metrics is related.

85. The method according to claim 84, further comprising providing at least one report to the plurality of advertisers with regard to the analyzing of the result of the at least one metrics.

86. The method according to claim 85, further comprising providing the at least one report substantially in real-time.

87. The method according to claim 86, further comprising awarding the consumer substantially in real-time.

88. A program storage device readable by machine, tangibly embodying a program of instructions executable by the machine to perform a method of providing a local targeted advertising campaign with regard to at least one predefined physical site said method comprising:

a) providing a semantic network having a plurality of segmentation paths, wherein each segmentation path is related to a predefined physical site and includes at least two interconnected nodes;

b) providing at least one metrics to a node from said at least two nodes;

c) inheriting said at least one metrics from said node to a higher-level node from among said at least two interconnected nodes; and

d) providing a local targeted advertising campaign with regard to at least one predefined physical site based on said inheriting of said at least one metrics.

89. A program storage device readable by machine, tangibly embodying a program of instructions executable by the machine to perform a method of providing a local targeted advertising campaign with regard to at least one predefined physical site, said method comprising:

a) providing a semantic network having a plurality of segmentation paths, wherein each segmentation path is related to a predefined physical site and includes at least two interconnected nodes, said semantic network defined by;

a.1. providing a spatial structure of physical locations of a plurality of entities within said predefined physical site;

a.2. providing a marketing environment of said predefined physical site, said marketing environment comprising data related to one of advertisements and sale promotions provided within said predefined physical site; and

a.3. providing a plurality of advertisers, which enable to interlink between said spatial structure with said marketing environment;

b) defining each node within said at least two interconnected nodes as one of a category, giving rise to a higher-level node, and a sub-category, giving rise to a lower-level node;

c) calculating the at least one metrics of the targeted advertising campaign by utilizing said semantic network, while providing at least one metrics to a node from said at least two nodes and inheriting said at least one metrics from said node to a higher-level node from among the at least two interconnected nodes; and

d) providing a local targeted advertising campaign with regard to the at least one predefined physical site based on the inheriting of said at least one metrics.

90. A program storage device readable by machine, tangibly embodying a program of instructions executable by the machine to perform a method of enabling to perform a consolidation with regard to a predefined physical site, said method comprising:

a) providing a semantic network having a plurality of segmentation paths, wherein each segmentation path is related to a predefined physical site and includes at least two interconnected nodes;

b) determining at least two similar segmentation paths within said semantic network, according to at least one predefined criterion; and

c) performing a consolidation of at least two nodes of each of said at least two similar segmentation paths, based on said predefined criterion, giving rise to at least one consolidated segmentation path.

91. A program storage device readable by machine, tangibly embodying a program of instructions executable by the machine to perform a method of awarding a consumer based on the consumer'"'"'s activity with regard to a predefined physical site, said activity performed by means of the consumer'"'"'s mobile device, said method comprising:

a) providing a semantic network having a plurality of segmentation paths, wherein each segmentation path is related to a predefined physical site and includes at least two interconnected nodes, while each node within said at least two interconnected nodes is one of a category, giving rise to a higher-level node, and a sub-category, giving rise to a lower-level node;

b) enabling the consumer to perform an activity with regard to said predefined physical site and said semantic network; and

c) awarding the consumer based on said activity.