Scoring Online Data for Advertising Servers
First Claim
1. A computer-implemented method, comprising:
- generating, on a computing device, a plurality of variables using signature data that includes historic clickstream data and current clickstream data associated with an entity;
identifying a subset of the plurality of variables using a covariance matrix for the plurality of variables;
generating scores by applying the subset of the plurality of variables to models;
generating weighted scores by associating weights with the scores, the weighted scores being usable for selecting online advertisements;
receiving target data including online advertisement click data associated with the entity;
generating new scores of the current data using the models; and
modifying the weights associated with the new scores using the target data.
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Abstract
Systems and methods for using online activity data in implementing a marketing strategy are provided. A system and method can include generating, on a computing device, variables using signature data that includes historic clickstream data and current clickstream data associated with an entity. A subset of the variables can be identified using a covariance matrix for the variables. Scores can be generated by applying the subset of the variables to models. Weighted scores can be generated by associating weights with the scores. The weighted scores can be used for selecting online advertisements. Target data can be received that includes online advertisement click data associated with the entity. New scores of the current data can be generated using the models. The weights associated with the new scores can be modified using the target data.
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Citations
30 Claims
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1. A computer-implemented method, comprising:
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generating, on a computing device, a plurality of variables using signature data that includes historic clickstream data and current clickstream data associated with an entity; identifying a subset of the plurality of variables using a covariance matrix for the plurality of variables; generating scores by applying the subset of the plurality of variables to models; generating weighted scores by associating weights with the scores, the weighted scores being usable for selecting online advertisements; receiving target data including online advertisement click data associated with the entity; generating new scores of the current data using the models; and modifying the weights associated with the new scores using the target data. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A system, comprising:
a server device that includes; a processor; and a non-transitory computer-readable storage medium containing instructions which when executed on the processor cause the processor to perform operations including; generating a plurality of variables using signature data that includes historic clickstream data and current clickstream data associated with an entity; identifying a subset of the plurality of variables using a covariance matrix for the plurality of variables; generating scores by applying the subset of the plurality of variables to models; generating weighted scores by associating weights with the scores, the weighted scores being usable for selecting online advertisements; receiving target data including online advertisement click data associated with the entity; generating new scores of the current clickstream data using the models; and modifying the weights associated with the new scores using the target data. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
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21. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause a data processing apparatus to:
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generate a plurality of variables using signature data that includes historic clickstream data and current clickstream data associated with an entity; identify a subset of the plurality of variables using a covariance matrix for the plurality of variables; generate scores by applying the subset of the plurality of variables to models; generate weighted scores by associating weights with the scores, the weighted scores being usable for selecting online advertisements; receive target data including online advertisement click data associated with the entity; generate new scores of the current clickstream data using the models; and modify the weights associated with the new scores using the target data. - View Dependent Claims (22, 23, 24, 25, 26, 27, 28, 29, 30)
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Specification