Consumer profiling and advertisement selection system

Abstract

A consumer profiling and advertisement selection system is presented in which consumers or subscribers can be characterized based on their purchase or viewing habits. The result of this process is a consumer characterization vector describing the probabilistic demographics and product preferences of the subscriber or viewer. Advertisement characterization vectors describing an actual or hypothetical market for a product or desired viewing audience can be determined. The ad characteristics including an ad demographic vector, an ad product category and an ad product preference vector is transmitted along with a consumer ID. The consumer ID is used to retrieve a consumer characterization vector which is correlated with the ad characterization vector to determine the suitability of the advertisement to the consumer. A price for displaying the advertisement can be determined based on the results of the correlation of the ad characteristics with the consumer characterization vector.

1 . A method of profiling users in an computer environment, said method comprising: 
 (a) receiving a purchase history corresponding to a unique user and including information related to the purchase of at least one item by said unique user;    (b) receiving demographic information corresponding to said unique user;    (c) receiving product characterization information describing a statistical relationship between a particular product and demographic characteristics of purchasers of the product;    (d) extrapolating additional demographic information from said purchase history and said product characterization information; and    (e) updating said demographic information based on said additional demographic information.    
   
   
       2 . The method of  claim 1 , wherein said product characterization information is developed based on a market study of a user population that visits a particular website.  
   
   
       3 . The method of  claim 1 , wherein said demographic information is received from a user demographic profile corresponding to said unique user.  
   
   
       4 . The method of  claim 3 , further comprising: 
 (f) identifying missing demographic information in said user demographic profile.    
   
   
       5 . The method of  claim 5 , wherein at least a portion of said missing demographic information in said user demographic profile is obtained through said additional demographic information.  
   
   
       6 . The method of  claim 5 , further comprising: 
 (g) returning said updated demographic information of step (e) to said user demographic profile.    
   
   
       7 . The method of  claim 1 , wherein said product characterization information does not include information about specific users.  
   
   
       8 . The method of  claim 1 , further comprising: 
 (f) targeting advertisements based on said updated demographic information.    
   
   
       9 . The method of  claim 1 , further comprising: 
 (f) comparing said updated demographic information to a target expression;    (g) generating a score based on said comparing; and    (h) delivering an advertisement based on said score.    
   
   
       10 . A method of targeting ads in an computer environment based on user segmentation, said method comprising: 
 (a) receiving user profile information corresponding to a unique user;    (b) assigning, based on said user profile information, said unique user to a population segment; and    (c) comparing said population segment of said unique user to an ad segment characterization corresponding to at least one advertisement.    
   
   
       11 . The method of  claim 10 , further comprising: 
 (d) calculating a correlation factor between said ad segment characterization and said population segment of said unique user; and    (e) targeting an ad based on said correlation factor.    
   
   
       12 . The method of  claim 10 , wherein said assigning is realized by creating a population segment vector that describes the population segment of said unique user.  
   
   
       13 . The method of  claim 12 , wherein said ad segment characterization is represented by an ad segment vector.  
   
   
       14 . The method of  claim 13 , wherein said comparing is realized by comparing said population segment vector to said ad segment vector.  
   
   
       15 . The method of  claim 14 , further comprising: 
 (d) calculating a correlation factor between said ad segment vector and said population segment vector; and    (e) targeting an ad based on said correlation factor.    
   
   
       16 . The method of  claim 10 , wherein said user profile information includes purchase history information.  
   
   
       17 . The method of  claim 16 , wherein said purchase history information includes a record of at least one purchase.  
   
   
       18 . The method of  claim 10 , wherein said user profile information includes demographic information.  
   
   
       19 . A method of presenting cross-sell products in a computer environment, said method comprising: 
 (a) receiving a purchase history corresponding to a unique user and including information related to the purchase of at least one product;    (b) receiving product characterization information for said at least one product wherein said product characterization describes a relationship between said at least one product and demographic characteristics of purchasers of said at least one product;    (c) calculating a consumer characterization vector based on said purchase history and said product characterization information; and    (d) suggesting items based on said consumer characterization vector.    
   
   
       20 . The method of  claim 19 , wherein said consumer characterization vector is based on a combination of a demographic characterization vector and a product preference vector.  
   
   
       21 . The method of  claim 20 , wherein the suggestion made in step (d) is based on the product preference vector.  
   
   
       22 . A method of profiling users in a computer environment, said method comprising: 
 (a) receiving a purchase history wherein said purchase history corresponding to a unique user and said purchase history including information related to the purchase of at least one item by said unique user;    (b) receiving demographic information corresponding to said unique user;    (c) receiving product characterization information describing a statistical relationship between a particular product and demographic characteristics of purchasers of the product;    (d) calculating additional demographic information from said purchase history and said product characterization information; and    (e) updating said demographic information based on said additional demographic information.