ADVERTISING-BUYING OPTIMIZATION METHOD, SYSTEM, AND APPARATUS
First Claim
1. A method for optimizing advertising buying for one or more media buyers having a budget for each channel in a single or a multi-channel campaign, comprising:
- creating clusters based on media consumption habits of individuals;
creating media consumption profiles for each defined cluster;
optionally attaching costs to each potential buy for each defined cluster; and
selecting one or more of the buys for the one or more media buyers.
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Abstract
A method, system, and apparatus for optimizing advertising buying is disclosed. The method comprises: obtaining data on media consumption habits of a defined set of individuals; optionally matching the data on media consumption habits to a database containing information regarding the individuals; optionally aggregating the data on media consumption habits by each individual; optionally recoding the data on media consumption habits using predetermined criteria to obtain recoded data; optionally removing the data on media consumption habits to obtain the recoded data only; creating clusters based on media consumption habits of the individuals; optionally creating profiles of each cluster to obtain defined clusters; optionally identifying the defined clusters; creating media consumption profiles for each defined cluster; optionally determining non-targeted individuals reached by each potential buy for each defined cluster; optionally attaching costs to each potential buy for each defined cluster; defining buys based on maximum coverage of the targeted individuals, optionally minimum coverage of non-targeted individuals, and optionally the lowest cost; and obtaining an optimized rank-ordered list of buys for one or more one or more media buyers.
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Citations
77 Claims
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1. A method for optimizing advertising buying for one or more media buyers having a budget for each channel in a single or a multi-channel campaign, comprising:
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creating clusters based on media consumption habits of individuals; creating media consumption profiles for each defined cluster; optionally attaching costs to each potential buy for each defined cluster; and selecting one or more of the buys for the one or more media buyers. - View Dependent Claims (2, 3, 4, 5)
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6. A method for optimizing advertising buying for one or media buyers having a budget for a multi-channel campaign but not a specified division of the budget for various channels in the campaign, comprising:
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creating clusters based on media consumption habits of individuals; creating media consumption profiles for each defined cluster; optionally attaching costs to each potential buy for each defined cluster; and selecting one or more of the buys for the one or more media buyers. - View Dependent Claims (7, 8, 9, 10)
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11. A method for creating defined clusters for one or more media buyers seeking to buy advertising, comprising:
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obtaining data on media consumption habits of a defined set of individuals; optionally matching the data on media consumption habits to a database containing information regarding the individuals; optionally recoding the data on media consumption using predetermined criteria to obtain recoded data; optionally removing the data on media consumption to obtain the recoded data only; and creating clusters based on media consumption habits of the individuals. - View Dependent Claims (12, 13, 14, 15)
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16. A method for obtaining a cluster solution comprising:
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(A) loading database A2 into a computer program, wherein database A2 is obtained by;
obtaining data on media consumption habits of a defined set of individuals;
matching the data on media consumption habits to a database containing information regarding the individuals;
recoding the data on media consumption using predetermined criteria to obtain recoded data;
optionally removing the data on media consumption to obtain the recoded data only, identified as database A2;(B) selecting either manually or automatically the (i) optimal distance function, (ii) the clustering approach;
(iii) the optimal agglomeration method, (iv) the minimum cluster size, (v) the method for pruning smaller clusters, and (vi) the sensitivity level;(C) running the clustering program based on the selections in (B)(i)-(B)(v) to obtain a diagnostic output of clusters; (D) examining the diagnostic output of clusters; (E) repeating steps (B)-(D) until a cluster solution is obtained meeting the pre-determined criteria; and (F) optionally validating the cluster solution. - View Dependent Claims (17, 18, 19, 20, 21)
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22. A computer readable medium storing a computer program, the computer program when executed in a computer executing a method comprising:
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(A) selecting either manually or automatically the (i) optimal distance function, (ii) the clustering approach;
(iii) the optimal agglomeration method, (iv) the minimum cluster size, (v) the method for pruning smaller clusters, and (vi) the sensitivity level;(B) running the clustering program based on the selections in (A)(i)-(A)(vi) to obtain a diagnostic output of clusters and outliers; (C) examining the diagnostic output of clusters and outliers; (D) repeating steps (A)-(C) until a cluster solution is obtained meeting the pre-determined criteria; and (E) optionally validating the cluster solution. - View Dependent Claims (23, 24, 25)
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26. A method for optimizing advertising buying, comprising:
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(i) obtaining data on media consumption habits of a defined set of individuals; (ii) optionally matching the data on media consumption habits to a database containing information regarding the individuals; (iii) optionally aggregating the data on media consumption habits by each individual; (iv) optionally recoding the data on media consumption habits using predetermined criteria to obtain recoded data; (v) optionally removing the data on media consumption habits to obtain the recoded data only; (vi) creating clusters based on media consumption habits of the individuals; (vii) optionally creating profiles of each cluster to obtain defined clusters; (viii) optionally identifying the defined clusters; (ix) creating media consumption profiles for each defined cluster; (x) optionally determining non-targeted individuals reached by each potential buy for each defined cluster; (xi) optionally attaching costs to each potential buy for each defined cluster; (xii) defining buys based on maximum coverage of the targeted individuals, optionally minimum coverage of non-targeted individuals, and optionally the lowest cost; and (xiii) obtaining an optimized rank-ordered list of buys for one or more one or more media buyers. - View Dependent Claims (27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48)
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49. A method for creating clusters based on media consumption habits of individuals comprising:
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obtaining data on media consumption habits of the individuals; optionally aggregating the data on media consumption habits by each individual; optionally recoding the data on media consumption habits using predetermined criteria to obtain recoded data; and creating clusters based on media consumption habits of the individuals. - View Dependent Claims (50, 51)
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52. A method for creating clusters based on media consumption habits of individuals comprising:
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obtaining data on media consumption habits of the individuals; optionally matching the data on media consumption habits to a database containing information regarding the individuals; optionally aggregating the data on media consumption habits by each individual; optionally recoding the data on media consumption habits using predetermined criteria to obtain recoded data; and creating clusters based on media consumption habits of the individuals. - View Dependent Claims (53, 54)
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55. A method for optimizing advertising buying, comprising:
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(i) obtaining data on media consumption habits of a defined set of individuals; (ii) matching the data on media consumption habits to a database containing information regarding the individuals; (iii) aggregating the data on media consumption habits by each individual; (iv) recoding the data on media consumption habits using predetermined criteria to obtain recoded data; (v) removing the data on media consumption habits to obtain the recoded data only; (vi) creating clusters based on media consumption habits of the individuals; (vii) creating profiles of each cluster to obtain defined clusters; (viii) identifying the defined clusters; (ix) creating media consumption profiles for each defined cluster; (x) determining non-targeted individuals reached by each potential buy for each defined cluster; (xi) attaching costs to each potential buy for each defined cluster; (xii) defining buys based on maximum coverage of the targeted individuals, minimum coverage of non-targeted individuals, and the lowest cost; and (xiii) obtaining an optimized rank-ordered list of buys for one or more media buyers. - View Dependent Claims (56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77)
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Specification