Method and apparatus for targeting best customers based on spend capacity

Abstract

Share of Wallet (“SOW”) is a modeling approach that utilizes various data sources to provide outputs that describe a consumers spending capability, tradeline history including balance transfers, and balance information. These outputs can be appended to data profiles of customers and prospects and can be utilized to support decisions involving prospecting, new applicant evaluation, and customer management across the lifecycle. A SOW score focusing on a consumer's spending capability can be used in the same manner as a credit bureau score.

1 - 20 . (canceled) 
     
     
         21 . A method of targeted marketing, comprising:
 modeling, by a targeted marketing computer-based system, consumer spending patterns using individual and aggregate consumer data, including tradeline data, internal customer data, and consumer panel data to produce a model of consumer spending patterns, wherein the model is stored in the memory of the computer-based system;   estimating, by the computer-based system, an existence of an estimated previous balance transfer of the customer;   wherein the existence of the estimated previous balance transfer is estimated based on a change in the magnitude of a balance on a tradeline of the individual consumer during a given time period as compared to a balance of the tradeline of the individual consumer in a time period prior to the given time period such that the change meets or exceeds a given threshold;   determining, by the computer-based system, spend capacities for existing customers based on, for each existing customer, tradeline data of the customer that comprises a spend amount associated with each existing customer, and the model of consumer spending patterns;   offsetting, by the computer-based system, the spend capacity by the estimated previous balance transfer; and   identifying, by the computer-based system, preferred customers based on spend capacities.   
     
     
         22 . The method of  claim 21 , further comprising:
 identifying characteristics common to the preferred customers; and   targeting consumers having the characteristics common to the preferred customers.   
     
     
         23 . The method of  claim 22 , wherein the targeted consumers are prospective customers. 
     
     
         24 . The method of  claim 22 , wherein the targeted consumers are existing customers. 
     
     
         25 . The method of  claim 21 , further comprising targeting customers identified as preferred customers. 
     
     
         26 . The method of  claim 21 , further comprising:
 estimating a spend capacity for consumers using, for a customer, tradeline data of the customer, balance transfer data of the customer, and the model of consumer spending patterns; and   targeting consumers having spend capacities similar to spend capacities of the preferred consumers.   
     
     
         27 . A method of targeted marketing, comprising:
 segmenting, by a targeted marketing computer-based system, existing customers into categories, wherein the segmented existing customer data is stored in a memory of the computer-based system;   modeling, by the computer-based system, characteristics of the existing customers using individual and aggregate data of the existing customers, including tradeline data, internal customer data, and consumer panel data;   determining, by the computer-based system, correlations between the categories and the characteristics of the existing customers;   targeting, by the computer-based system, consumers having characteristics correlated to a particular category, wherein the targeting comprises:
 estimating identifying, by the computer-based system, an existence of an estimated previous balance transfers of a customer, 
 wherein the existence of the estimated previous balance transfer is estimated based on a change in the magnitude of a balance on at least one tradeline of the individual consumer during a given time period as compared to a balance of the at least one tradeline of the individual consumer in a time period prior to the given time period such that the change meets or exceeds a given threshold; 
 estimating characteristics of the customer based on, for a customer, tradeline data of the customer that comprises a spend amount associated with the customer, balance transfers of the customer, and a model of consumer spending patterns, 
 offsetting, by the computer-based system, the spend capacity by the estimated previous balance transfer; 
 determining a subset of consumers having characteristics similar to the characteristics correlated to a particular category; and 
 targeting the subset of consumers. 
   
     
     
         28 . The method of  claim 27 , wherein the particular category represents preferred customers. 
     
     
         29 . The method of  claim 27 , wherein the targeted consumers are prospective customers. 
     
     
         30 . The method of  claim 27 , wherein the targeted consumers are existing customers. 
     
     
         31 . The method of  claim 27 , wherein consumers are targeted based on an individual spend capacity. 
     
     
         32 . An apparatus for targeted marketing, comprising:
 a targeted marketing processor; and   a tangible, non-transitory memory in communication with the processor, wherein the memory stores a plurality of processing instructions for directing the processor to:
 model consumer spending patterns using individual and aggregate consumer data, including tradeline data, internal customer data, and consumer panel data; 
 estimate, by the processor, an existence of an estimated previous balance transfers of existing customers, 
 wherein the existence of the estimated previous balance transfer is estimated based on a change in the magnitude of a balance on at least one tradeline of the individual consumer during a given time period as compared to a balance of the at least one tradeline of the individual consumer in a time period prior to the given time period such that the change meets or exceeds a given threshold; 
 determine spend capacities for the existing customers based on, for each existing customer, tradeline data of the customer that comprises a spend amount associated with each existing customer, an existence of previous balance transfers of the customer, and the model of consumer spending patterns; and 
 offset, by the processor, the spend capacity by the estimated previous balance transfer; 
 identify preferred customers based on spend capacity. 
   
     
     
         33 . The apparatus of  claim 32 , wherein the processing instructions further direct the processor to:
 identify characteristics common to the preferred customers; and   identify consumers having the characteristics common to the preferred customers.   
     
     
         34 . The apparatus of  claim 33 , wherein the consumers are existing customers. 
     
     
         35 . The apparatus of  claim 33 , wherein the consumers are potential customers. 
     
     
         36 . The apparatus of  claim 34 , wherein the processing instructions further direct the processor to:
 estimate a spend capacity for consumers using, for a customer, tradeline data of the customer, balance transfer data of the customer, and the model of consumer spending patterns; and   identify consumers having spend capacities similar to spend capacities of the preferred consumers.   
     
     
         37 . An article of manufacture including a non-transitory computer readable medium having instructions stored thereon that, in response to execution by a targeted marketing computing device, cause the computing device to perform operations comprising:
 modeling, by the computing device, consumer spending patterns using individual and aggregate consumer data, including tradeline data, internal customer data, and consumer panel data;   estimating, by the computing device, an existence of an estimated previous balance transfers of existing customers,   wherein the existence of the estimated previous balance transfer is estimated based on a change in the magnitude of a balance on at least one tradeline of the individual consumer during a given time period as compared to a balance of the at least one tradeline of the individual consumer in a time period prior to the given time period such that the change meets or exceeds a given threshold;   determining, by the computing device, spend capacities for the existing customers based on, for each existing customer, tradeline data of the customer that comprises a spend amount associated with each existing customer, an existence of previous balance transfers of the customer, and the model of consumer spending patterns;   offsetting, by the computing device, the spend capacity by the estimated previous balance transfer; and   identifying preferred customers based on spend capacity.   
     
     
         38 . The article of manufacture of  claim 37 , further comprising:
 identifying characteristics common to the preferred customers; and   identifying consumers having the characteristics common to the preferred customers.   
     
     
         39 . The article of manufacture of  claim 37 , further comprising
 estimating a spend capacity for consumers using, for a customer, tradeline data of the customer, balance transfer data of the customer, and the model of consumer spending patterns; and   identifying consumers having spend capacities similar to spend capacities of the preferred consumers.