Systems and methods for tracking consumer electronic spend behavior to predict attrition

1. A computer-implemented method of tracking consumer spend behavior to predict attrition, comprising:

receiving, in a database associated with a profiler computing system, past transaction data related to a plurality of past payment transactions of a consumer from an acquiring financial institution of a merchant;

receiving, in the database associated with the profiler computing system, environmental and/or behavioral data associated with each of the past payment transactions of the consumer from the acquiring financial institution of the merchant;

determining, by a processor of the profiler computing system, based on an analysis of the past transaction data and environmental and/or behavioral data associated with each of the past payment transactions, a spend behavior model of the consumer, wherein the spend behavior model predicts one or more spend behaviors of a consumer over a duration of time;

subsequent to determining the spend behavior model of the consumer, receiving, in the database associated with the profiler computing system by way of a networked user device, transaction data related to one or more current payment transactions of the consumer using a payment vehicle;

generating, by an electronic transaction processor, payment vehicle tokens based on the transaction data related to one or more current payment transactions;

affiliating, by the processor of the profiler computing system, the one or more current payment transactions of the consumer to one or more of the payment vehicle tokens;

receiving, in the database associated with the profiler computing system, environmental and/or behavioral data associated with the one or more current payment transactions associated with one or more of the payment vehicle tokens;

determining, by the processor of the profiler computing system, based on an analysis of the current transaction data and environmental and/or behavioral data associated with each of the current payment transactions and one or more of the payment vehicle tokens, a current spend behavior of the consumer;

identifying one or more anomalous transactions among the one or more current payment transactions associated with one or more of the payment vehicle tokens based on the spend behavior model; and

determining, by the processor of the profiler computing system, based on the identified one or more anomalous transactions, a likelihood of an attrition of the current spend behavior.

2. The method of claim 1 , wherein determining the likelihood of an attrition of the current spend behavior further comprises:

determining an inconsistency in a spend behavior of the consumer, by comparing the current spend behavior with the spend behavior model; and

determining the likelihood of an attrition of the current spend behavior based on the determined inconsistency.

3. The method of claim 2 , wherein the determined inconsistency in a spend behavior includes one or more of:

a missed payment transaction that is otherwise predicted to occur based on the spend behavior model;

a change in a relative proportion of one or more payment networks used by the consumer in payment transactions;

a change in an environmental and/or behavioral data; or

a change in a purchasing behavior related to a good, service and/or stock keeping unit.

4. The method of claim 1 , further comprising:

updating the spend behavior model based on the determined likelihood of the attrition of the current spend behavior.

5. The method of claim 1 , further comprising:

predicting a customer lifetime value for a merchant based on one or more of the spend behavior model, the current spend behavior, or the likelihood of the attrition of the current spend behavior.

6. The method of claim 1 , wherein transaction data is data electronically received from one or more merchants to effectuate an electronic transfer of funds via an electronic payment network.

7. The method of claim 1 , wherein one or more of the transaction data or environmental and/or behavioral data is further received from one or more of:

a stock keeping unit of a merchant; or

an authorized third party.

8. The method of claim 1 , wherein the environmental and/or behavioral data associated with a payment transaction includes, one or more of:

data related to a channel of purchase used in the payment transaction;

temporal data related to the payment transaction;

data related to a geographical location of the consumer or merchant in the payment transaction;

data related to the merchant in the payment transaction;

data related to a good or service being transacted for in the payment transaction;

data related to any online activity of the consumer; and

transaction data related to the payment transaction.

9. The method of claim 1 , wherein a spend behavior includes one or more of:

habitually purchasing from a merchant and/or group of merchants; habitually purchasing using one or more channels of purchase;

habitually purchasing a good and/or service, or a category of good and/or service;

habitually using of one or more payment methods and/or payment vehicles; and

trends in the above described spend behaviors.

10. A system of tracking consumer spend behavior to predict attrition, comprising a data storage device storing instructions for tracking consumer spend behavior to predict attrition; and a processor configured to execute the instructions to perform a method including:

receiving, in a database associated with a profiler computing system, past transaction data related to a plurality of past payment transactions of a consumer from an acquiring financial institution of a merchant;

receiving, in the database associated with the profiler computing system, environmental and/or behavioral data associated with each of the past payment transactions of the consumer from the acquiring financial institution the merchant;

determining, by a processor of the profiler computing system, based on an analysis of the past transaction data and environmental and/or behavioral data associated with each of the past payment transactions, a spend behavior model of the consumer, wherein the spend behavior model predicts one or more spend behaviors of a consumer over a duration of time;

subsequent to determining the spend behavior model of the consumer, receiving, in the database associated with the profiler computing system by way of a networked user device, transaction data related to one or more current payment transactions of the consumer using a payment vehicle;

generating, by an electronic transaction processor, payment vehicle tokens based on the transaction data related to one or more current payment transactions;

affiliating, by the processor of the profiler computing system, the one or more current payment transactions of the consumer to one or more of the payment vehicle tokens;

receiving, in the database associated with the profiler computing system, environmental and/or behavioral data associated with the one or more current payment transactions associated with one or more of the payment vehicle tokens;

determining, by the processor of the profiler computing system, based on an analysis of the current transaction data and environmental and/or behavioral data associated with each of the current payment transactions and one or more of the payment vehicle tokens, a current spend behavior of the consumer;

identifying one or more anomalous transactions among the one or more current payment transactions associated with one or more of the payment vehicle tokens based on the spend behavior model; and

determining, by the processor of the profiler computing system, based on the identified one or more anomalous transactions, a likelihood of an attrition of the current spend behavior.

11. The system of claim 10 , wherein determining the likelihood of an attrition of the current spend behavior further comprises:

determining an inconsistency in a spend behavior of the consumer, by comparing the current spend behavior with the spend behavior model; and

determining the likelihood of an attrition of the current spend behavior based on the determined inconsistency.

12. The system of claim 11 , wherein the determined inconsistency in a spend behavior includes one or more of:

a missed payment transaction that is otherwise predicted to occur based on the spend behavior model;

a change in a relative proportion of one or more payment networks used by the consumer in payment transactions;

a change in an environmental and/or behavioral data; or

a change in a purchasing behavior related to a good, service and/or stock keeping unit.

13. The system of claim 10 , further comprising:

updating the spend behavior model based on the determined likelihood of the attrition of the current spend behavior.

14. The system of claim 10 , further comprising:

predicting a customer lifetime value for a merchant based on one or more of the spend behavior model, the current spend behavior, or the likelihood of the attrition of the current spend behavior.

15. The system of claim 10 , wherein transaction data is data electronically received from one or more merchants to effectuate an electronic transfer of funds via an electronic payment network.

16. The system of claim 10 , wherein one or more of the transaction data or environmental and/or behavioral data is further received from one or more of:

a stock keeping unit of a merchant; or

an authorized third party.

17. The system of claim 10 , wherein the environmental and/or behavioral data associated with a payment transaction includes, one or more of:

data related to a channel of purchase used in the payment transaction;

temporal data related to the payment transaction;

data related to a geographical location of the consumer or merchant in the payment transaction;

data related to the merchant in the payment transaction;

data related to a good or service being transacted for in the payment transaction;

data related to any online activity of the consumer; and

transaction data related to the payment transaction.

18. The system of claim 10 , wherein a spend behavior includes one or more of:

habitually purchasing from a merchant and/or group of merchants;

habitually purchasing using one or more channels of purchase;

habitually purchasing a good and/or service, or a category of good and/or service;

habitually using of one or more payment methods and/or payment vehicles; and

trends in the above described spend behaviors.

19. A non-transitory machine-readable medium stores instructions that, when executed by profiler computing system, causes the profiler computing system to perform a method for tracking consumer spend behavior to predict attrition, the method comprising:

receiving, in a database associated with a profiler computing system, past transaction data related to a plurality of past payment transactions of a consumer from an acquiring financial institution of a merchant;

receiving, in the database associated with the profiler computing system, environmental and/or behavioral data associated with each of the past payment transactions of the consumer from the acquiring financial institution of the merchant;

determining, by a processor of the profiler computing system, based on an analysis of the past transaction data and environmental and/or behavioral data associated with each of the past payment transactions, a spend behavior model of the consumer, wherein the spend behavior model predicts one or more spend behaviors of a consumer over a duration of time;

subsequent to determining the spend behavior model of the consumer, receiving, in the database associated with the profiler computing system by way of a networked user device, transaction data related to one or more current payment transactions of the consumer using a payment vehicle;

generating, by an electronic transaction processor, payment vehicle tokens based on the transaction data related to one or more current payment transactions;

affiliating, by the processor of the profiler computing system, the one or more current payment transactions of the consumer to one or more of the payment vehicle tokens;

receiving, in the database associated with the profiler computing system, environmental and/or behavioral data associated with the one or more current payment transactions associated with one or more of the payment vehicle tokens;

determining, by the processor of the profiler computing system, based on an analysis of the current transaction data and environmental and/or behavioral data associated with each of the current payment transactions and one or more of the payment vehicle tokens, a current spend behavior of the consumer;

identifying one or more anomalous transactions among the one or more current payment transactions associated with one or more of the payment vehicle tokens based on the spend behavior model; and

determining, by the processor of the profiler computing system, based on the identified one or more anomalous transactions, a likelihood of an attrition of the current spend behavior.

20. The non-transitory machine-readable medium of claim 19 , wherein determining the likelihood of an attrition of the current spend behavior further comprises:

determining an inconsistency in a spend behavior of the consumer, by comparing the current spend behavior with the spend behavior model; and

determining the likelihood of an attrition of the current spend behavior based on the determined inconsistency.