Non-commerce data for commerce analytics

Abstract

In various example embodiments, a system and method for providing non-commerce data for commerce analytics are presented. Attribute data associated with a user may be received from an attribute sources. User characteristics may be inferred based on an analysis of at least a portion of the attribute data. Consumer profiles including consumer characteristics may be accessed. A specific consumer profile may be identified by correlating the inferred user characteristics with respective consumer characteristics of the consumer profiles. A commerce output may be identified based, at least in part, on the identified consumer profile. The identified commerce output may be recommended to the user.

What is claimed is: 
     
         1 . A system comprising:
 an attribute module to receive attribute data associated with a user from a plurality of attribute sources;   a characteristic module to infer user characteristics based on an analysis of at least a portion of the attribute data;   a profile module to access a plurality of consumer profiles that includes consumer characteristics;   the profile module further to identify a consumer profile from among the plurality of consumer profiles by correlating the inferred user characteristics with respective consumer characteristics of the plurality of consumer profiles;   a commerce module, executable by at least one processor of a machine, to identify a commerce output based, at least in part, on the identified consumer profile; and   a presentation module to cause recommendation of the identified commerce output to the user.   
     
     
         2 . The system of  claim 1 , further comprising:
 the profile module further to determine second order characteristics included in the identified consumer profile, the second order characteristics having an indirect relationship with the attribute data; and   the commerce module further to identify the commerce output based, at least in part, on the second order characteristics.   
     
     
         3 . The system of  claim 2 , further comprising:
 the profile module further to determine characteristic scores for at least a portion of the second order characteristics, the characteristic scores being based at least in part on a relevance metric that corresponds to respective second order characteristics;   the profile module further to rank the second order characteristics based on the characteristic scores; and   the commerce module further to identify the commerce output based, at least in part, on the ranked second order characteristics.   
     
     
         4 . The system of  claim 3 , wherein the relevance metric comprises an occurrence count of respective second order characteristics. 
     
     
         5 . The system of  claim 1 , further comprising:
 the profile module further to identify a complementary consumer profile by correlating the identified consumer profile with respective consumer profiles of the plurality of consumer profiles;   the profile module further to determine third order characteristics included in the complementary consumer profile, the third order characteristics having an indirect relationship with the attribute data; and   the commerce module further to identify the commerce output based, at least in part, on the third order characteristics.   
     
     
         6 . The system of  claim 5 , further comprising:
 the profile module further to filter the third order characteristics based on the attribute data; and   the commerce module further to identify the commerce output based on the filtered third order characteristics.   
     
     
         7 . The system of  claim 1 , further comprising:
 the profile module further to determine characteristic scores for at least a portion of the inferred user characteristics; and   the profile module further to rank the inferred user characteristics based on the determined characteristic scores, the profile module to identify the consumer profile using the ranked user characteristics.   
     
     
         8 . A method comprising:
 receiving attribute data associated with a user from a plurality of attribute sources;   inferring user characteristics based on an analysis of at least a portion of the attribute data;   accessing a plurality of consumer profiles including consumer characteristics;   identifying a consumer profile from among the plurality of consumer profiles by correlating the inferred user characteristics with respective consumer characteristics of the plurality of consumer profiles;   identifying, using a processor of a machine, a commerce output based, at least in part, on the identified consumer profile; and   causing recommendation of the identified commerce output to the user.   
     
     
         9 . The method of  claim 8 , further comprising:
 determining second order characteristics included in the identified consumer profile, the second order characteristics having an indirect relationship with the attribute data; and   identifying the commerce output based, at least in part, on the second order characteristics.   
     
     
         10 . The method of  claim 9 , further comprising:
 determining characteristic scores for at least a portion of the second order characteristics, the characteristic scores being based at least in part on a relevance metric corresponding to respective second order characteristics;   ranking the second order characteristics based on the characteristic scores; and   identifying the commerce output based, at least in part, on the ranked second order characteristics.   
     
     
         11 . The method of  claim 10 , wherein the relevance metric comprises an occurrence count of respective second order characteristics. 
     
     
         12 . The method of  claim 8 , further comprising:
 identifying a complementary consumer profile by correlating the identified consumer profile with respective consumer profiles of the plurality of consumer profiles;   determining third order characteristics included in the complementary consumer profile, the third order characteristics having an indirect relationship with the attribute data; and   identifying the commerce output based, at least in part, on the third order characteristics.   
     
     
         13 . The method of  claim 12 , further comprising:
 filtering the third order characteristics based on the attribute data; and   identifying the commerce output based on the filtered third order characteristics.   
     
     
         14 . The method of  claim 8 , further comprising:
 determining characteristic scores for at least a portion of the inferred user characteristics; and   ranking the inferred user characteristics based on the determined characteristic scores, the identifying of the consumer profile using the ranked user characteristics.   
     
     
         15 . The method of  claim 8 , further comprising:
 identifying a set of consumer profiles from among the plurality of consumer profiles by correlating the inferred user characteristic with respective consumer characteristics of the plurality of consumer profiles;   determining profile scores for at least a portion of the identified set of consumer profiles; and   ranking the identified set of consumer profiles based on determined profile scores, the identifying of the commerce output based, at least in part, on the ranked set of consumer profiles.   
     
     
         16 . The method of  claim 8 , further comprising:
 determining a weight factor corresponding to a portion of the attribute data, the analysis of at least the portion of the attribute data including using the determined weight factor.   
     
     
         17 . The method of  claim 16 , further comprising:
 adjusting the weight factor based on an indication of a commerce result of the recommended commerce output.   
     
     
         18 . The method of  claim 8 , further comprising:
 accessing secondary data that includes secondary attribute data associated with consumers; and   identifying the commerce output based on an analysis of the secondary data and the identified consumer profile.   
     
     
         19 . A machine readable medium having no transitory signals and storing instructions that, when executed by at least one processor of a machine, cause the machine to perform operations comprising:
 receiving attribute data associated with a user from a plurality of attribute sources;   inferring user characteristics based on an analysis of at least a portion of the attribute data;   accessing a plurality of consumer profiles including consumer characteristics;   identifying a consumer profile from among the plurality of consumer profiles by correlating the inferred user characteristics with respective consumer characteristics of the plurality of consumer profiles;   identifying, using a processor of a machine, a commerce output based, at least in part, on the identified consumer profile; and   causing recommendation of the identified commerce output to the user.   
     
     
         20 . The machine-readable medium of  claim 19 , wherein the operations further comprise:
 determining second order characteristics included in the identified consumer profile, the second order characteristics having an indirect relationship with the attribute data; and   identifying the commerce output based, at least in part, on the second order characteristics.