AD PLACEMENT
This invention concerns optimal ad selection for Web pages by selecting and updating an attribute set, obtaining and updating an ad-attribute profile, and optimally choosing the next ad. The present invention associates a set of attributes with each customer. The attributes reflect the customers' interests and they incorporate the characteristics that impact ad selection. Similarly, the present invention associates with each ad an ad-attribute profile in order to calculate a customer's estimated ad selection probability and measure the uncertainty in that estimate. An ad selection algorithm optimally selects which ad to show based on the click probability estimates and the uncertainties regarding these estimates.
1 . A method comprising:
identifying a plurality of advertisement types;
determining, using at least one processor, the advertisement type with the highest click-thru-rate for each marketing medium of a plurality of marketing mediums;
grouping each marketing medium of the plurality of marketing mediums having a first advertisement type with the highest click-through-rate; and
serving advertisements of the first advertisement type to marketing mediums in the grouping.
2 . The method as recited in claim 1 , further comprising determining a percentage of impressions served to marketing mediums in the grouping.
3 . The method as recited in claim 2 , further comprising splitting marketing mediums in the grouping into two or more other groupings if the percentage of impressions served to marketing mediums in the grouping is greater than a predetermined percentage.
4 . The method as recited in claim 1 , wherein the advertisement types correspond to advertising campaign types.
5 . The method as recited in claim 4 , wherein the advertisement types include one or more of sports, personal finance, computers and technology, or entertainment.
6 . The method as recited in claim 1 , further comprising:
determining a probability that each advertisement of the first advertisement type will be selected by a user; and
serving the advertisement with a high probability to the user.
7 . The method as recited in claim 6 , wherein serving the advertisement with a high probability to the user comprises serving the advertisement to a marketing medium in the grouping when the user accesses the marketing medium.
8 . The method as recited in claim 1 , wherein the marketing medium comprises a software program.
9 . The method as recited in claim 8 , wherein the marketing medium comprises one or more web sites.
10 . The method as recited in claim 8 , wherein the marketing medium is a software program on a mobile device.
11 . The method as recited in claim 1 , wherein the marketing medium comprises a mobile device.
12 . A non-transitory computer-readable storage medium including a set of instructions that, when executed, cause at least one processor to perform steps comprising:
identifying a plurality of advertisement types;
determining the advertisement type with the highest click-thru-rate for each marketing medium of a plurality of marketing mediums;
grouping each marketing medium of the plurality of marketing mediums having a first advertisement type with the highest click-through-rate; and
serving advertisements of the first advertisement type to marketing mediums in the grouping.
13 . The computer-readable storage medium as recited in claim 12 , further comprising instructions that, when executed, cause at least one processor to determine a percentage of impressions served to marketing mediums in the grouping.
14 . The computer-readable storage medium as recited in claim 13 , further comprising instructions that, when executed, cause at least one processor to split marketing mediums in the grouping into two or more other groupings if the percentage of impressions served to marketing mediums in the grouping is greater than a predetermined percentage.
15 . The computer-readable storage medium as recited in claim 12 , wherein the advertisement types correspond to advertising campaign types.
16 . The computer-readable storage medium as recited in claim 12 , wherein the advertisement types include one or more of sports, personal finance, computers and technology, or entertainment.
17 . The computer-readable storage medium as recited in claim 12 , further comprising instructions that, when executed, cause at least one processor to:
determine a probability that each advertisement of the first advertisement type will be selected by a user; and
serve the advertisement with a high probability to the user.
18 . The computer-readable storage medium as recited in claim 17 , wherein serving the advertisement with a high probability to the user comprises serving the advertisement to a marketing medium in the grouping when the user accesses the marketing medium.
19 . The computer-readable storage medium as recited in claim 12 , wherein the marketing medium comprises a software program.
20 . The computer-readable storage medium as recited in claim 19 , wherein the marketing medium comprises one or more websites.
21 . The computer-readable storage medium as recited in claim 19 , wherein the marketing medium is a software program on a mobile device.
22 . The computer-readable storage medium as recited in claim 12 , wherein the marketing medium comprises a mobile device.
23 . A method comprising:
associating one or more advertisements with one or more interest categories;
serving the one or more advertisements to a plurality of marketing mediums;
tracking, using at least one processor, click-thru-rates for the one or more advertisements;
determining, using the at least one processor, an interest category having the highest click-thru-rate on a marketing medium of the plurality of marketing mediums; and
selecting at least one advertisement to serve to the marketing medium based on the marketing medium being associated with the interest category.
24 . The method as recited in claim 23 , further comprising grouping each marketing medium of the plurality of marketing mediums into groupings corresponding to the interest category with the highest click-through-rate for each marketing medium.
25 . The method as recited in claim 24 , further comprising tracking the number of advertisements served to marketing mediums of each grouping.
26 . The method as recited in claim 25 , further comprising splitting a grouping if the percentage of advertisements served to marketing mediums in the grouping is greater than a predetermined percentage.
27 . The method as recited in claim 23 , further comprising:
identifying a group of advertisements associated with the interest category;
determining a probability that each advertisement of the group of advertisements will be selected by a user; and
serving the advertisement with the highest probability to the user.
28 . The method as recited in claim 27 , wherein serving the advertisement with the highest probability to the user comprises serving the advertisement to the marketing medium when the user accesses the marketing medium.
29 . The method as recited in claim 23 , wherein the marketing medium comprises a software program.
30 . The method as recited in claim 29 , wherein the marketing medium comprises one or more websites.
31 . The method as recited in claim 29 , wherein the marketing medium is a software program on a mobile device.
32 . The method as recited in claim 23 , wherein the marketing medium comprises a mobile device.
33 . A non-transitory computer-readable storage medium including a set of instructions that, when executed, cause at least one processor to perform steps comprising:
associating one or more advertisements with one or more interest categories;
serving the one or more advertisements to a plurality of marketing mediums;
tracking click-thru-rates for the one or more advertisements;
determining an interest category having the highest click-thru-rate on a marketing medium of the plurality of marketing mediums; and
selecting at least one advertisement to serve to the marketing medium based on the marketing medium being associated with the interest category.
34 . The computer-readable storage medium as recited in claim 33 , further comprising instructions that, when executed, cause at least one processor to group each marketing medium of the plurality of marketing mediums into groupings corresponding to the interest category with the highest click-through-rate for each marketing medium.
35 . The computer-readable storage medium as recited in claim 34 , further comprising instructions that, when executed, cause at least one processor to track the number of advertisements served to marketing mediums of each grouping.
36 . The computer-readable storage medium as recited in claim 35 , further comprising instructions that, when executed, cause at least one processor to split a grouping if the percentage of advertisements served to marketing mediums in the grouping is greater than a predetermined percentage.
37 . The computer-readable storage medium as recited in claim 33 , further comprising instructions that, when executed, cause at least one processor to:
identify a group of advertisements associated with the interest category;
determine a probability that each advertisement of the group of advertisements will be selected by a user; and
serve the advertisement with the highest probability to the user.
38 . The computer-readable storage medium as recited in claim 37 , wherein serving the advertisement with the highest probability to the user comprises serving the advertisement to the marketing medium when the user accesses the marketing medium.
39 . The computer-readable storage medium as recited in claim 33 , wherein the marketing medium comprises a software program.
40 . The computer-readable storage medium as recited in claim 39 , wherein the marketing medium comprises one or more websites.
41 . The computer-readable storage medium as recited in claim 39 , wherein the marketing medium is a software program on a mobile device.
42 . The computer-readable storage medium as recited in claim 33 , wherein the marketing medium comprises a mobile device.
43 . A method comprising:
determining, using at least one processor, an advertisement type with a highest click-thru-rate for a marketing medium on a plurality of mobile devices;
identifying advertisements corresponding to the advertisement type; and
serving advertisements corresponding to the advertisement type to the marketing medium on the plurality of mobile devices.
44 . The method as recited in claim 43 , wherein the marketing medium comprises a software program.
45 . The method as recited in claim 44 , wherein the marketing medium comprises one or more websites.
46 . The method as recited in claim 43 , wherein the mobile devices comprise smart devices.
47 . The method as recited in claim 43 , wherein the advertisement types correspond to advertising campaign types.
48 . The method as recited in claim 43 , wherein the advertisement type is one of sports, personal finance, computers and technology, or entertainment.
49 . The method as recited in claim 43 , further comprising:
determining a probability that each advertisement corresponding to the advertisement type will be selected by a user; and
serving the advertisement with a high probability to the user.
50 . The method as recited in claim 49 , wherein serving the advertisement with a high probability to the user comprises serving the advertisement to the marketing medium when the user accesses the marketing medium.
51 . The method as recited in claim 43 , wherein the marketing medium is a software program.