Systems and methods for identification of predicted consumer spend based on historical purchase activity progressions

Abstract

Technologies for identifying prospective marketing targets based on payment vehicle-based payment transactions processed over electronic payment networks are disclosed. Payment vehicle-based payment transactions are analyzed to determine historical purchase activity progressions. Consumer behavior can be mapped to a historical purchase activity progression so that future spend behavior of the consumer can be identified.

1 - 20 . (canceled) 
     
     
         21 . A computer-implemented method comprising:
 collecting, by a server, activity information associated with at least one first user at a plurality of terminals;   determining, by the server, behavioral patterns, historical activities progression, or a combination thereof associated with the at least one first user based upon statistical analysis of the collected activity information;   mapping, by the server, the behavioral patterns of the at least one first user to the historical activities progression of the at least one first user to predict activities by the at least one first user in next instance of time; and   generating, by the server, an attrition indicator based on the behavioral patterns, the historical activities progression, or a combination thereof, wherein at least one second user provides targeted offers to the at least one first user within a predetermined activity window.   
     
     
         22 . The computer-implemented method of  claim 21 , wherein the historical activities progression includes temporal parameters, and wherein the historical activities progression indicates a series of activities performed by the at least one user within a specified time period. 
     
     
         23 . The computer-implemented method of  claim 22 , further comprising:
 determining, by the server, completion of at least one activity from the series of activities previously performed by the at least one user; and   predicting, by the server, completion of remaining series of activities by the at least one first user in the next instance of time based on behavioral patterns.   
     
     
         24 . The computer-implemented method of  claim 23 , wherein the completion of the remaining series of activities by the at least one first user in the next instance of time is based on location information, contextual information, or a combination thereof associated with the at least one first user. 
     
     
         25 . The computer-implemented method of  claim 23 , further comprising:
 determining, by the server, an occurrence of at least one of the remaining series of activities pursuant to at least one in-progress activity upon satisfaction of at least one parameter, wherein the at least one parameter include a location within a geo-radius of the in-progress activity.   
     
     
         26 . The computer-implemented method of  claim 21 , further comprising:
 generating, by the server, a notification pertaining to the predicted activity in a user interface of a device associated with the at least on second user, wherein the at least on second user performs one or more actions to modify the behavioral patterns of the at least one first user.   
     
     
         27 . The computer-implemented method of  claim 21 , further comprising:
 determining, by the server, a progression to another activity by the at least one first user does not map to the historical activities progression; and   storing, by the server, the activity information associated with the at least one first user.   
     
     
         28 . The computer-implemented method of  claim 21 , activity information includes an authorization request, identifying indicia, or a combination thereof, and wherein the identifying indicia include user identification information, a media access control (MAC) identifier, an internet protocol (IP) identifier, a device fingerprint, a geographic identifier, a payment type identifier, or a combination thereof. 
     
     
         29 . The computer-implemented method of  claim 21 , wherein the mapping is based on temporal parameter, geographical parameters, user categories, purchase velocity, and purchase amounts. 
     
     
         30 . The computer-implemented method of  claim 21 , wherein conversion percentage, redemption volume, perceived relevance 
     
     
         31 . A system comprising:
 collecting, by a server, activity information associated with at least one first user at a plurality of terminals;   determining, by the server, behavioral patterns, historical activities progression, or a combination thereof associated with the at least one first user based upon statistical analysis of the collected activity information;   mapping, by the server, the behavioral patterns of the at least one first user to the historical activities progression of the at least one first user to predict activities by the at least one first user in next instance of time; and   generating, by the server, an attrition indicator based on the behavioral patterns, the historical activities progression, or a combination thereof, wherein at least one second user provides targeted offers to the at least one first user within a predetermined activity window.   
     
     
         32 . The system of  claim 31 , wherein the historical activities progression includes temporal parameters, and wherein the historical activities progression indicates a series of activities performed by the at least one user within a specified time period. 
     
     
         33 . The system of  claim 32 , further comprising:
 determining, by the server, completion of at least one activity from the series of activities previously performed by the at least one user; and   predicting, by the server, completion of remaining series of activities by the at least one first user in the next instance of time based on behavioral patterns.   
     
     
         34 . The system of  claim 33 , wherein the completion of the remaining series of activities by the at least one first user in the next instance of time is based on location information, contextual information, or a combination thereof associated with the at least one first user. 
     
     
         35 . The system of  claim 33 , further comprising:
 determining, by the server, an occurrence of at least one of the remaining series of activities pursuant to at least one in-progress activity upon satisfaction of at least one parameter, wherein the at least one parameter include a location within a geo-radius of the in-progress activity.   
     
     
         36 . The system of  claim 31 , further comprising:
 generating, by the server, a notification pertaining to the predicted activity in a user interface of a device associated with the at least on second user, wherein the at least on second user performs one or more actions to modify the behavioral patterns of the at least one first user.   
     
     
         37 . An apparatus comprising:
 at least one processor; and   at least one memory including computer program code for one or more programs,   the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following,
 collect, by a server, activity information associated with at least one first user at a plurality of terminals; 
 determine, by the server, behavioral patterns, historical activities progression, or a combination thereof associated with the at least one first user based upon statistical analysis of the collected activity information; 
 map, by the server, the behavioral patterns of the at least one first user to the historical activities progression of the at least one first user to predict activities by the at least one first user in next instance of time; and 
 generate, by the server, an attrition indicator based on the behavioral patterns, the historical activities progression, or a combination thereof, wherein at least one second user provides targeted offers to the at least one first user within a predetermined activity window. 
   
     
     
         38 . The apparatus of  claim 37 , wherein the historical activities progression includes temporal parameters, and wherein the historical activities progression indicates a series of activities performed by the at least one user within a specified time period. 
     
     
         39 . The apparatus of  claim 38 , further comprising:
 determine, by the server, completion of at least one activity from the series of activities previously performed by the at least one user; and   predict, by the server, completion of remaining series of activities by the at least one first user in the next instance of time based on behavioral patterns.   
     
     
         40 . The apparatus of  claim 39 , wherein the completion of the remaining series of activities by the at least one first user in the next instance of time is based on location information, contextual information, or a combination thereof associated with the at least one first user.