Data mining for managing marketing resources
First Claim
1. A method for managing a marketing campaign, comprising:
- providing a data mining engine capable of being trained with training data; and
capable thereafter of performing inferences relative to the training data and on additional data;
providing a user database containing observed characteristics of each one of a set of users, the characteristics comprising at least one of;
(a) at least one of the user'"'"'s attributes, (b) at least one of the user'"'"'s preferences;
training the data mining engine with a set of training data comprising the user database by clustering the user database into different segments of users distinguished by different states of one or more characteristics;
inputting to the data mining engine a predetermined set of characteristics including a predetermined set of user attributes likely to pertain to a product to which the marketing campaign is directed and, in response thereto, obtaining from the data mining engine a subset of the users in the database having the highest correlation to the characteristic by determining which of the segments found during clustering of the user database has the highest statistical correlation to the predetermined set of characteristics;
determining in the data mining engine a set of prevalent attributes of the subset of users;
defining a target database of users and determining in the data mining engine a target subset of users in the target database statistically correlated to the set of prevalent attributes;
conducting a presently conducted marketing campaign cycle directed at the target subset of users;
observing and analyzing responses of the target subset of users to the presently conducted marketing campaign cycle at least partly in real-time;
forming a focused group of the target subset of users whose observed response was a particular type of response;
determining, in the data mining engine, a group of prevalent characteristics of the focused group of users; and
defining a database to be mined and determining, in the data mining engine, a new set of users in the database to be mined whose characteristics are statistically correlated with the group of prevalent characteristics,determining, in the data mining engine, a complete set of statistically prevalent user attributes of the subset of users;
for any member of the subset of users having certain attributes which are undetermined in the user data base, filling in the certain undetermined attributes with the corresponding ones of the complete set of statistically prevalent user attributes of the subset of users.
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Abstract
Data mining for managing marketing resources is disclosed. In one embodiment, a method for managing a marketing campaign includes the following. First, the method provides a data mining engine capable of being trained with training data and capable thereof of performing inference relative to the training data and on future data. Next, the method provides a user database defining observed characteristics of each one of a set of users. The characteristics include at least one of one or more user'"'"'s attributes, and one or more of the user'"'"'s preferences. Finally, the data mining engine is trained with a set of training data comprising the user data base, and a predetermined characteristic pertaining to the market campaign is input to the engine, such that, in response to the input, a subset of the users in the database is obtained that have the highest correlation to the characteristic.
276 Citations
12 Claims
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1. A method for managing a marketing campaign, comprising:
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providing a data mining engine capable of being trained with training data; and
capable thereafter of performing inferences relative to the training data and on additional data;providing a user database containing observed characteristics of each one of a set of users, the characteristics comprising at least one of;
(a) at least one of the user'"'"'s attributes, (b) at least one of the user'"'"'s preferences;training the data mining engine with a set of training data comprising the user database by clustering the user database into different segments of users distinguished by different states of one or more characteristics; inputting to the data mining engine a predetermined set of characteristics including a predetermined set of user attributes likely to pertain to a product to which the marketing campaign is directed and, in response thereto, obtaining from the data mining engine a subset of the users in the database having the highest correlation to the characteristic by determining which of the segments found during clustering of the user database has the highest statistical correlation to the predetermined set of characteristics; determining in the data mining engine a set of prevalent attributes of the subset of users; defining a target database of users and determining in the data mining engine a target subset of users in the target database statistically correlated to the set of prevalent attributes; conducting a presently conducted marketing campaign cycle directed at the target subset of users; observing and analyzing responses of the target subset of users to the presently conducted marketing campaign cycle at least partly in real-time; forming a focused group of the target subset of users whose observed response was a particular type of response; determining, in the data mining engine, a group of prevalent characteristics of the focused group of users; and defining a database to be mined and determining, in the data mining engine, a new set of users in the database to be mined whose characteristics are statistically correlated with the group of prevalent characteristics, determining, in the data mining engine, a complete set of statistically prevalent user attributes of the subset of users; for any member of the subset of users having certain attributes which are undetermined in the user data base, filling in the certain undetermined attributes with the corresponding ones of the complete set of statistically prevalent user attributes of the subset of users. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A machine-readable medium having instructions stored thereon for execution by a processor to perform a method comprising:
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providing a data mining engine capable of being trained with training data and capable thereafter of performing inferences relative to the training data; and
on additional data;providing a user database defining the observed characteristics of each one of a set of users, the characteristics comprising at least one of;
(a) at least one of the user'"'"'s attributes, (b) at least one of the user'"'"'s preferences;training the data mining engine with a set of training data comprising the user database by clustering the user data base into different segments of user distinguished by different states of a characteristic; inputting to the data mining engine a predetermined set of characteristics including a predetermined set of user attributes likely to pertain to a product to which the marketing campaign is directed and, in response thereto, obtaining from the data mining engine a subset of the users in the data base having the highest correlation to the characteristic by determining which of the segments found during clustering of the user database has the highest statistical correlation to the predetermined characteristic; determining in the data mining engine a set of prevalent attributes of the subset of users; defining a target database of users and determining in the data mining engine a target subset of users in the target data base statistically correlated to the set of prevalent; conducting and analyzing a presently conducted marketing campaign cycle directed at the target subset of users at least partly in real-time; observing responses of the target subset of users to the presently conducted marketing campaign cycle; forming a focused group of the target subset of users whose observed response was a particular type of response; determining, in the data mining engine, a group of prevalent characteristics of the focused group of users; and defining a database to be mined and determining, in the data mining engine, a new set of users in the database to be mined whose characteristics are statistically correlated with the group of prevalent characteristics, determining, in the data mining engine, a complete set of statistically prevalent user attributes of the subset of users; for any member of the subset of users having certain attributes which are undetermined in the user data base, filling in the certain undetermined attributes with the corresponding ones of the complete set of statistically prevalent user attributes of the subset of users.
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Specification