Methods and systems for predicting consumer behavior from transaction card purchases
First Claim
1. A computer-based method for predicting consumer behavior within a predetermined time period, said method performed using a payment processor computer device coupled to a database, said method comprising:
- recording, by the payment processor computer device, electronic consumer data in the database for each consumer of a global population of consumers including historical purchases made by each consumer, including at least one electronic transaction initiated by a consumer using a transaction card at a point-of-sale (POS) device;
defining, by the payment processor computer device, a life event by assigning spending variables to the life event, wherein a spending variable represents a quantity of consumer spending associated with one or more of (i) a particular merchant and (ii) a particular type of good or service;
generating an anticipated spend for each spending variable assigned to the life event by extrapolating a historical actual spend based on the historical purchases, wherein the anticipated spend represents anticipated spending in each spending variable assigned to the life event;
calculating a residual value of a determined variance between the anticipated spend and an actual spend for each consumer by calculating a quotient of (i) the difference between the anticipated spend and the actual spend and (ii) the anticipated spend;
determining that a residual value of a determined variance between the anticipated spend and the actual spend for each consumer exceeds a predefined threshold;
assigning, by the payment processor computer device, the consumer to a sample group, wherein the sample group represents consumers that are experiencing the life event;
generating, by the payment processor computer device, a predictive model based on historical purchases made by consumers within the sample group;
applying, by the payment processor computer device, 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, by the payment processor computer device, a list of consumers outside of the sample group that are predicted to experience the life event within the predetermined time period.
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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.
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Citations
22 Claims
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1. A computer-based method for predicting consumer behavior within a predetermined time period, said method performed using a payment processor computer device coupled to a database, said method comprising:
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recording, by the payment processor computer device, electronic consumer data in the database for each consumer of a global population of consumers including historical purchases made by each consumer, including at least one electronic transaction initiated by a consumer using a transaction card at a point-of-sale (POS) device; defining, by the payment processor computer device, a life event by assigning spending variables to the life event, wherein a spending variable represents a quantity of consumer spending associated with one or more of (i) a particular merchant and (ii) a particular type of good or service; generating an anticipated spend for each spending variable assigned to the life event by extrapolating a historical actual spend based on the historical purchases, wherein the anticipated spend represents anticipated spending in each spending variable assigned to the life event; calculating a residual value of a determined variance between the anticipated spend and an actual spend for each consumer by calculating a quotient of (i) the difference between the anticipated spend and the actual spend and (ii) the anticipated spend; determining that a residual value of a determined variance between the anticipated spend and the actual spend for each consumer exceeds a predefined threshold; assigning, by the payment processor computer device, the consumer to a sample group, wherein the sample group represents consumers that are experiencing the life event; generating, by the payment processor computer device, a predictive model based on historical purchases made by consumers within the sample group; applying, by the payment processor computer device, 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, by the payment processor computer device, a list of consumers outside of the sample group that are predicted to experience the life event within the predetermined time period. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A computer for predicting behavior of a consumer within a predetermined time period based on the consumer'"'"'s purchases made using a transaction card, said computer comprising a processor, computer-readable instructions executable by the processor, and a database, said computer configured to:
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record electronic consumer data in said database for each consumer of a global population of consumers including historical purchases made by each consumer, including at least one electronic transaction initiated by a consumer using a transaction card at a point-of-sale (POS) device; define a life event by assigning spending variables to the life event, wherein a spending variable represents a quantity of consumer spending associated with one or more of (i) a particular merchant and (ii) a particular type of good or service; generate an anticipated spend by extrapolating a historical actual spend based on the historical purchases, wherein the anticipated spend represents anticipated spending in each spending variable assigned to the life event; calculate a residual value of a determined variance between the anticipated spend and an actual spend for each consumer by calculating a quotient of (i) the difference between the anticipated spend and the actual spend and (ii) the anticipated spend; determine that a residual value of a determined variance between the anticipated spend and the actual spend for each consumer exceeds a predefined threshold; assign the consumer to a sample group, wherein the sample group represents consumers that are experiencing the life event; generate a predictive model based on historical purchases made by consumers within the sample group, wherein the predictive model is generated using the processor; 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 the predetermined time period. - View Dependent Claims (13, 14, 15, 16, 17, 18)
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19. A network based system for predicting behavior of a consumer within a predetermined time period based on the consumer'"'"'s purchases made using a transaction card, said system comprising:
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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; record electronic consumer data in said centralized database for each consumer of a global population of consumers including historical purchases made by each consumer, including at least one electronic transaction initiated by a consumer using a transaction card at a point-of-sale (POS) device; define a life event by assigning spending variables to the life event, wherein a spending variable represents a quantity of consumer spending associated with one or more of (i) a particular merchant and (ii) a particular type of good or service; generate an anticipated spend by extrapolating a historical actual spend based on the historical purchases, wherein the anticipated spend represents anticipated spending in each spending variable assigned to the life event; calculate a residual value of a determined variance between the anticipated spend and an actual spend for each consumer by calculating a quotient of (i) the difference between the anticipated spend and the actual spend and (ii) the anticipated spend; determine that a residual value of a determined variance between the anticipated spend and the actual spend for each consumer exceeds a predefined threshold; assign the consumer to a sample group, wherein the sample group represents consumers that are experiencing the life event; generate a predictive model based on historical purchases made by 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 the predetermined time period. - View Dependent Claims (20, 21)
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22. A computer program embodied on a non-transitory computer readable storage medium for predicting consumer behavior, said program comprising at least one code segment that, when executed by a computing device including at least one processor in communication with a memory, causes the computing device to:
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record electronic consumer data in the database for each consumer of a global population of consumers including historical purchases made by each consumer, including at least one electronic transaction initiated by a consumer using a transaction card at a point-of-sale (POS) device; define a life event by assigning spending variables to the life event, wherein a spending variable represents a quantity of consumer spending associated with one or more of (i) a particular merchant and (ii) a particular type of good or service; generate an anticipated spend by extrapolating a historical actual spend based on the historical purchases, wherein the anticipated spend represents anticipated spending in each spending variable assigned to the life event; calculate a residual value of a determined variance between the anticipated spend and an actual spend for each consumer by calculating a quotient of (i) the difference between the anticipated spend and the actual spend and (ii) the anticipated spend; determine that a residual value of a determined variance between the anticipated spend and the actual spend for each consumer exceeds a predefined threshold; assign the consumer to a sample group, wherein the sample group represents consumers that are experiencing the life event; generate a predictive model based on historical purchases made by 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 the predetermined time period.
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Specification