Methods and systems for predicting consumer behavior from transaction card purchases

Abstract

A computer-based method for predicting consumer behavior is provided. The method is performed using a computer system coupled to a database. The method includes recording consumer data in the database for each consumer of a global population of consumers including historical purchases made by each consumer using a transaction card, defining a life event by assigning spending variables to the life event, determining a sample group of consumers that are experiencing the life event based on the consumer data stored within the database with respect to the spending variables, generating a predictive model based on historical purchases made by consumers within the sample group, and applying the predictive model to predict each consumer within the global population that will experience the life event. The predictive model is applied using the computer system. A list of consumers predicted to experience the life event within a predetermined time period is output.

1 .- 22 . (canceled) 
     
     
         23 . A computer-based method for predicting consumer behavior, said method performed using a computer device coupled to a database, said method comprising:
 storing consumer data in the database for consumers of a global population of consumers including historical purchases made by the consumers using a transaction card;   defining a life event by assigning spending variables to the life event;   generating an anticipated spend for each spending variable assigned to the life event by extrapolating a historical actual spend based on the historical purchases stored in the database, wherein the anticipated spend represents anticipated spending in each spending variable assigned to the life event;   creating a sample group of consumers from the global population of consumers that represents consumers that are experiencing the life event;   generating a predictive model based on the historical purchases made by the consumers within the sample group;   applying the predictive model to predict each consumer within the global population and outside of the sample group that will experience the life event; and   outputting a list of consumers outside of the sample group that are predicted to experience the life event within a predetermined time period.   
     
     
         24 . A computer-based method in accordance with  claim 23  wherein determining a sample group of consumers that are experiencing the life event further comprises:
 analyzing historical purchases for each consumer within the global population having an account in the database during a historical time period for each spending variable in a consumption bundle of the life event; 
 determining a historical actual spending curve for each consumer within the global population during the historical time period using the historical purchases made in the consumption bundle; 
 determining an anticipated spending curve for each consumer within the global population during a second time period after the historical time period using the historical actual spending curve, wherein the anticipated spending curve is determined before the second time period occurs; 
 collecting purchase data for each consumer within the global population over the second time period; and 
 determining an actual spending curve during the second time period for each consumer within the global population using the collected purchase data, wherein the actual spending curve is determined after the second time period occurs. 
 
     
     
         25 . A computer-based method in accordance with  claim 24  wherein creating the sample group of consumers that are experiencing the life further comprises:
 comparing the actual spending curve and the anticipated spending curve for each consumer within the global population during the second time period; 
 determining a variance between the actual spending curve and the anticipated spending curve for each consumer within the global population; and 
 assigning each consumer within the global population to the sample group using the determined variance. 
 
     
     
         26 . A computer-based method in accordance with  claim 23  further comprising:
 causing a customized offer to be sent to at least one consumer on the output list for a product related to the life event being experienced by the at least one consumer. 
 
     
     
         27 . A computer-based method in accordance with  claim 23  further comprising refining the predictive model using actual spending by at least one consumer on the output list of consumers based on purchases made on a transaction card. 
     
     
         28 . A computer-based method in accordance with  claim 23  wherein storing consumer data in the database for each consumer of a global population further comprises:
 storing transaction card purchase data for each consumer within the global population to the database; and 
 storing third party data about each consumer within the global population to the database. 
 
     
     
         29 . A computer-based method in accordance with  claim 23  wherein defining a life event by assigning spending variables to the life event further comprises:
 selecting a life event a consumer may experience; and 
 assigning at least one spending variable to the selected life event to form a consumption bundle for the selected life event. 
 
     
     
         30 . A computer-based method in accordance with  claim 29  wherein defining a life event by assigning spending variables to the life event further comprises assigning at least one demographic variable to the life event, the consumption bundle comprising the at least one assigned demographic variable and the at least one assigned spending variable. 
     
     
         31 . A computer-based method in accordance with  claim 23  wherein generating a predictive model based on historical purchases made by consumers within the sample group further comprises:
 analyzing each consumer in the sample group during a historical time period to determine spending trends that are common to consumers within the sample group based on the historical purchases, wherein a spending trend represents at least one of an increase, a decrease or no change in an amount of spending in a spending variable; and 
 generating a predictive model that includes the determined spending trends. 
 
     
     
         32 . A computer-based method in accordance with  claim 23  wherein applying the predictive model to predict each consumer within the global population that will experience the life event further comprises:
 applying the predictive model to all consumers within the global population having an account in the database; 
 modeling actual spending trends of each consumer within the global population using the predictive model, the predictive model including a set of spending trends; and 
 when the actual spending trends of a consumer within the global population are similar to the set of spending trends included within the predictive model, predicting that the consumer will experience the life event. 
 
     
     
         33 . A computer for predicting behavior of a consumer, said computer comprising a processor, computer-readable instructions executable by the processor, and a database, said computer configured to:
 store consumer data in the database for consumers of a global population of consumers including historical purchases made by the consumers using a transaction card;   define a life event by assigning spending variables to the life event;   generate an anticipated spend for each spending variable assigned to the life event by extrapolating a historical actual spend based on the historical purchases stored in the database, wherein the anticipated spend represents anticipated spending in each spending variable assigned to the life event;   create a sample group of consumers from the global population of consumers that represents consumers that are experiencing the life event;   generate a predictive model based on the historical purchases made by the consumers within the sample group;   apply the predictive model to predict each consumer within the global population and outside of the sample group that will experience the life event; and   output a list of consumers outside of the sample group that are predicted to experience the life event within a predetermined time period.   
     
     
         34 . A computer in accordance with  claim 33  further configured to:
 analyze historical purchases for a consumer within the global population having an account in said database during a historical time period for each spending variable in a consumption bundle of the life event; 
 determine a historical actual spending curve during the historical time period for the consumer using the historical purchases made in the consumption bundle; 
 determine an anticipated spending curve during a second time period after the historical time period for the consumer using the historical actual spending curve, wherein the anticipated spending curve is determined before the second time period occurs; 
 collect purchase data for the consumer over the second time period; 
 determine an actual spending curve during the second time period for the consumer using the collected purchase data, wherein the actual spending curve is determined after the second time period occurs; 
 compare the actual spending curve and the anticipated spending curve for the consumer during the second time period; 
 determine a variance between the actual spending curve and the anticipated spending curve for the consumer; and 
 assign the consumer to the sample group using the determined variance. 
 
     
     
         35 . A computer in accordance with  claim 33  further configured to:
 map anticipated consumer needs to at least one offer stored within the database based on the defined life event; and 
 provide the least one offer to the consumer. 
 
     
     
         36 . A computer in accordance with  claim 33  further configured to refine the predictive model using actual spending of the consumers within the sample group based on purchases made on transaction cards by at least one of adding a spending variable to a definition of the life event and removing a spending variable from the definition of the life event. 
     
     
         37 . A computer in accordance with  claim 33  further configured to:
 select a life event a consumer may experience; and 
 assign at least one spending variable to the selected life event to form a consumption bundle for the selected life event. 
 
     
     
         38 . A computer in accordance with  claim 33  further configured to:
 analyze each consumer in the sample group during a historical time period to determine spending trends that are common to consumers within the sample group based on the stored consumer data, wherein a spending trend represents at least one of an increase, a decrease or no change in an amount of spending in a spending variable; and 
 generate a predictive model that includes the determined spending trends. 
 
     
     
         39 . A computer in accordance with  claim 38  further configured to:
 apply the predictive model to each consumer within the global population; 
 model actual spending trends of each consumer within the global population with the predictive model; and 
 when actual spending trends of a consumer within the global population are similar to spending trends within the predictive model, predict that the consumer will experience the life event. 
 
     
     
         40 . A network based system for predicting behavior of a consumer, said system comprising:
 a client computing device;   a database for storing information; and   a server computing device comprising a processor and computer-readable instructions executable by said processor, said server computing device configured to be coupled to said client computing device and said database, said server computing system further configured to:
 store consumer data in the database for consumers of a global population of consumers including historical purchases made by the consumers using a transaction card; 
 define a life event by assigning spending variables to the life event; 
 generate an anticipated spend for each spending variable assigned to the life event by extrapolating a historical actual spend based on the historical purchases stored in the database, wherein the anticipated spend represents anticipated spending in each spending variable assigned to the life event; 
 create a sample group of consumers from the global population of consumers that represents consumers that are experiencing the life event; 
 generate a predictive model based on the historical purchases made by the consumers within the sample group; 
 apply the predictive model to predict each consumer within the global population and outside of the sample group that will experience the life event; and 
 output a list of consumers outside of the sample group that are predicted to experience the life event within a predetermined time period. 
   
     
     
         41 . A network based system in accordance with  claim 40 , wherein said server computing device is further configured to:
 analyze historical purchases of a consumer within the global population having an account in said database during a historical time period for each spending variable in a consumption bundle of the life event;   determine a historical actual spending curve during the historical time period for the consumer using the historical purchases made in the consumption bundle;   determine an anticipated spending curve during a second time period after the historical time period for the consumer using the historical actual spending curve, wherein the anticipated spending curve is determined before the second time period occurs;   collect purchase data for the consumer over the second time period;   determine an actual spending curve during the second time period for the consumer using the collected purchase data, wherein the actual spending curve is determined after the second time period occurs;   compare the actual spending curve and the anticipated spending curve for the consumer during the second time period;   determine a variance between the actual spending curve and the anticipated spending curve; and   assign the consumer to the sample group using the determined variance.   
     
     
         42 . A network based system in accordance with  claim 40 , wherein said server computing device is further configured to:
 analyze each consumer in the sample group during a historical time period to determine spending trends that are common to consumers within the sample group, wherein a spending trend represents at least one of an increase, a decrease or no change in an amount of spending in a spending variable;   generate a predictive model that includes the determined spending trends;   apply the predictive model to each consumer within the global population;   model actual spending trends of each consumer within the global population with the spending trends included within the predictive model; and   when actual spending trends of a consumer within the global population are similar to spending trends within the predictive model, predict that the consumer will experience the life event.