Inbox management system

Abstract

Systems and methods are presented for managing electronic promotion correspondence sent to consumers. A system may manage electronic promotion correspondence sent on a per-consumer basis. The system may access multiple electronic promotion correspondences generated for a particular consumer, select an electronic promotion correspondence from among the multiple electronic promotion correspondences, and determine to send the electronic promotion correspondence to the consumer according to any number of factors. The system may determine a target time to send the first electronic promotion correspondence to the consumer and selected communication channel to send the electronic promotion correspondence through.

1 .- 20 . (canceled) 
     
     
         21 . An apparatus comprising a processor and a non-transitory memory storing program instructions, wherein the non-transitory memory and the program instructions are configured to, with the processor, cause the apparatus to:
 determine a first attribute tuple data object associated with a first user profile, wherein the first attribute tuple data object comprises a plurality of engagement level attributes associated with a plurality of content item class attributes;   determine, from a plurality of user profiles, one or more user profiles that are similar to the first user profile based at least in part on the first attribute tuple data object and one or more attribute tuple data objects associated with the one or more user profiles;   retrieve one or more electronic correspondence feedback data objects that are associated with the one or more user profiles and one or more historical electronic correspondences; and   determine an adjusted target electronic correspondence cadence associated with the first user profile based at least in part on the one or more electronic correspondence feedback data objects.   
     
     
         22 . The apparatus of  claim 21 , wherein the non-transitory memory and the program instructions are configured to, with the processor, cause the apparatus to:
 determine one or more user profile segments associated with the one or more user profiles.   
     
     
         23 . The apparatus of  claim 22 , wherein the non-transitory memory and the program instructions are configured to, with the processor, cause the apparatus to:
 generate an experiment table data object comprising metadata defining correlations between one or more experimental cadences for transmitting electronic correspondences and the one or more user profile segments.   
     
     
         24 . The apparatus of  claim 23 , wherein the non-transitory memory and the program instructions are configured to, with the processor, cause the apparatus to:
 determine a user profile segment from the one or more user profile segments;   determine an experimental cadence from the one or more experimental cadences based on the user profile segment; and   transmit a plurality of experimental electronic correspondences to a plurality of client devices associated with the user profile segment based on the experimental cadence.   
     
     
         25 . The apparatus of  claim 21 , wherein the non-transitory memory and the program instructions are configured to, with the processor, cause the apparatus to:
 determine a content item class attribute from the plurality of content item class attributes; and   generate an electronic correspondence comprising a plurality of content items associated with the content item class attribute.   
     
     
         26 . The apparatus of  claim 25 , wherein the non-transitory memory and the program instructions are configured to, with the processor, cause the apparatus to determine the content item class attribute from the plurality of content item class attributes based at least in part on calculating cadence difference attributes associated with the plurality of content item class attributes. 
     
     
         27 . The apparatus of  claim 26 , wherein the cadence difference attributes are associated with class-specific target cadence attributes and class-specific actual cadence attributes. 
     
     
         28 . A computer-implemented method comprising:
 determining a first attribute tuple data object associated with a first user profile, wherein the first attribute tuple data object comprises a plurality of engagement level attributes associated with a plurality of content item class attributes;   determining, from a plurality of user profiles, one or more user profiles that are similar to the first user profile based at least in part on the first attribute tuple data object and one or more attribute tuple data objects associated with the one or more user profiles;   retrieving one or more electronic correspondence feedback data objects that are associated with the one or more user profiles and one or more historical electronic correspondences; and   determining an adjusted target electronic correspondence cadence associated with the first user profile based at least in part on the one or more electronic correspondence feedback data objects.   
     
     
         29 . The computer-implemented method of  claim 28 , further comprising:
 determining one or more user profile segments associated with the one or more user profiles.   
     
     
         30 . The computer-implemented method of  claim 29 , further comprising:
 generating an experiment table data object comprising metadata defining correlations between one or more experimental cadences for transmitting electronic correspondences and the one or more user profile segments.   
     
     
         31 . The computer-implemented method of  claim 30 , further comprising:
 determining a user profile segment from the one or more user profile segments;   determining an experimental cadence from the one or more experimental cadences based on the user profile segment; and   transmitting a plurality of experimental electronic correspondences to a plurality of client devices associated with the user profile segment based on the experimental cadence.   
     
     
         32 . The computer-implemented method of  claim 28 , further comprising:
 determining a content item class attribute from the plurality of content item class attributes; and   generating an electronic correspondence comprising a plurality of content items associated with the content item class attribute.   
     
     
         33 . The computer-implemented method of  claim 32 , wherein determining the content item class attribute from the plurality of content item class attributes comprises calculating cadence difference attributes associated with the plurality of content item class attributes. 
     
     
         34 . The computer-implemented method of  claim 33 , wherein the cadence difference attributes are associated with class-specific target cadence attributes and class-specific actual cadence attributes. 
     
     
         35 . A computer program product comprising at least one non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising an executable portion configured to:
 determine a first attribute tuple data object associated with a first user profile, wherein the first attribute tuple data object comprises a plurality of engagement level attributes associated with a plurality of content item class attributes;   determine, from a plurality of user profiles, one or more user profiles that are similar to the first user profile based at least in part on the first attribute tuple data object and one or more attribute tuple data objects associated with the one or more user profiles;   retrieve one or more electronic correspondence feedback data objects that are associated with the one or more user profiles and one or more historical electronic correspondences; and   determine an adjusted target electronic correspondence cadence associated with the first user profile based at least in part on the one or more electronic correspondence feedback data objects.   
     
     
         36 . The computer program product of  claim 35 , wherein the computer-readable program code portions comprise the executable portion that is configured to:
 determine one or more user profile segments associated with the one or more user profiles.   
     
     
         37 . The computer program product of  claim 36 , wherein the computer-readable program code portions comprise the executable portion that is configured to:
 generate an experiment table data object comprising metadata defining correlations between one or more experimental cadences for transmitting electronic correspondences and the one or more user profile segments.   
     
     
         38 . The computer program product of  claim 37 , wherein the computer-readable program code portions comprise the executable portion that is configured to:
 determine a user profile segment from the one or more user profile segments;   determine an experimental cadence from the one or more experimental cadences based on the user profile segment; and   transmit a plurality of experimental electronic correspondences to a plurality of client devices associated with the user profile segment based on the experimental cadence.   
     
     
         39 . The computer program product of  claim 35 , wherein the computer-readable program code portions comprise the executable portion that is configured to:
 determine a content item class attribute from the plurality of content item class attributes; and   generate an electronic correspondence comprising a plurality of content items associated with the content item class attribute.   
     
     
         40 . The computer program product of  claim 39 , wherein the computer-readable program code portions comprise the executable portion that is configured to determine the content item class attribute from the plurality of content item class attributes based at least in part on calculating cadence difference attributes associated with the plurality of content item class attributes.