Method and system for identifying optimal rewards programs

Patent metadata

Field	Value
Publication number	US-10181129-B2
Application number	US-201314103132-A
Country	US
Kind code	B2
Filing date	Dec 11, 2013
Priority date	Dec 11, 2013
Publication date	Jan 15, 2019
Grant date	Jan 15, 2019

Abstract

A method and a system are provided for identifying optimal rewards programs. The method involves retrieving from one or more databases a first set of information comprising payment card holder transaction information including at least purchasing activities and optionally payment activities attributable to the payment card holder, and retrieving from one or more databases a second set of information comprising a plurality of rewards programs. One or more predictive behavioral spend profiles are generated based on the first set of information. The one or more predictive behavioral spend profiles and the second set of information are then analyzed to identify one or more correlations between the one or more predictive behavioral spend profiles and the plurality of rewards programs. Targeted information, based on the one or more correlations, is provided to one or more entities (e.g., payment card holder or merchant).

First claim

What is claimed is:

1. A method performed by a data processing apparatus, the method comprising:
retrieving from one or more searchable databases a first set of information comprising payment card holder transaction information including transaction history for a payment card attributable to a payment card holder, wherein the transaction history has been obtained from a plurality of point of sale (POS) devices of a plurality of merchants;
retrieving from the one or more searchable databases a second set of information comprising a plurality of rewards programs;
analyzing the first set of information to determine behavioral information of the payment card holder;
extracting information related to an intent of the payment card holder from the behavioral information as a basis to predict desirable spend behaviors, as well as a specific future spend behavior of the payment card holder;
generating one or more predictive behavioral spend profiles based on both the behavioral information and the predicted desirable spend behavior of the payment card holder;
analyzing the one or more predictive behavioral spend profiles with respect to the second set of information;
identifying one or more correlations between the one or more predictive behavioral spend profiles and the plurality of rewards programs;
determining whether the payment card holder is enrolled in at least one of the plurality of rewards programs;
determining a plurality of payment card holder affiliation rewards programs in which the payment card holder is not enrolled for the payment card holder based on the one or more correlations;
prioritizing the plurality of payment card holder affiliation rewards programs and the enrolled rewards program in an order in which the payment card holder realizes greater savings with one or more of the plurality payment card holder affiliation rewards programs in which the payment card holder is not enrolled than with the enrolled rewards program;
providing a proposal for a specific one of the plurality of payment card holder affiliation rewards programs to one or more entities based on the prioritizing;
and
receiving a response to enroll in the specific one of the plurality of payment card holder affiliation rewards programs from the payment card holder via a payment card holder station of the payment card holder.

2. The method of claim 1 , wherein the one or more entities comprise the payment card holder or a merchant.

3. The method of claim 1 ,
receiving a response to the proposal; and
enrolling the payment card holder in the specific one of the plurality of payment card holder affiliation rewards programs based on the response.

4. The method of claim 1 , further comprising algorithmically analyzing the first set of information to generate the one or more predictive behavioral spend profiles.

5. The method of claim 1 , further comprising algorithmically analyzing the one or more predictive behavioral spend profiles and the second set of information to identify one or more correlations between the one or more predictive behavioral spend profiles and the plurality of rewards programs.

6. The method of claim 1 , wherein the first set of information comprises payment card billing, purchasing and/or payment transactions, and optionally demographic and/or geographic information attributable to the payment card holder.

7. The method of claim 1 , wherein the second set of information comprises rewards programs that are structured marketing efforts that reward loyal buying behavior or behavior that is beneficial to a merchant.

8. The method of claim 1 , wherein the rewards programs are offered by major retailers, pharmacies, supermarkets, hardware stores, hotel chains, airlines, car rentals, and banks.

9. The method of claim 1 , further comprising:
tracking and measuring impact of the proposal based on the one or more correlations after the targeted information has been provided to the one or more entities.

10. The method of claim 1 , wherein the one or more predictive behavioral spend profiles provides a behavioral propensity score that is analyzed with the second set of information to identify one or more correlations between the one or more predictive behavioral spend profiles and the plurality of rewards programs, and wherein the behavioral propensity score is indicative of a propensity to exhibit a certain behavior.

11. A system comprising:
one or more databases configured to store a first set of information comprising payment card holder transaction information including at least purchasing activities attributable to a payment card holder;
one or more databases configured to store a second set of information comprising a plurality of rewards programs;
a memory comprising program instructions; and
a processor configured to execute the program instructions in the memory to:
analyze the first set of information to determine behavioral information of the payment card holder;
extracting information related to an intent of the payment card holder from the behavioral information as a basis to predict specific future spend behavior and future desirable spend behaviors;
generate one or more predictive behavioral spend profiles based on the behavioral information and the predicted future desirable spend behaviors of the payment card holder;
analyze the one or more predictive behavioral spend profiles and the second set of information;
identify one or more correlations between the one or more predictive behavioral spend profiles and the plurality of rewards programs;
determine whether the payment card holder is enrolled in at least one of the plurality of rewards programs;
determine a plurality of payment card holder affiliation rewards programs in which the payment card holder is not enrolled for the payment card holder based on the one or more correlations;
prioritize the determined card holder affiliation rewards programs in an order in which the payment card holder realizes the greatest savings out of the determined payment card holder affiliation rewards programs in which the payment card holder is not enrolled; and
provide targeted information, based on the one or more correlations and prioritized card holder affiliation rewards programs, to a merchant station, a payment card holder statement, or both.

12. The system of claim 11 , wherein the targeted information includes at least one or more suggestions or proposals for payment card holder affiliation with one or more rewards programs.

13. The system of claim 11 , wherein the processor is further configured to perform one of the following functions selected from the group consisting of (a) algorithmically analyze the first set of information to generate the one or more predictive behavioral spend profiles, and (b) algorithmically analyze the one or more predictive behavioral spend profiles and the second set of information to identify one or more correlations between the one or more predictive behavioral spend profiles and the plurality of rewards programs.

14. The system of claim 11 , wherein the first set of information comprises payment card billing, purchasing and/or payment transactions, and optionally demographic and/or geographic information attributable to the payment card holder.

15. The system of claim 11 , wherein the second set of information comprises rewards programs that are structured marketing efforts that reward loyal buying behavior or behavior that is beneficial to a merchant.

16. The system of claim 11 , wherein the processor is further configured to:
track and measure impact of the targeted information, based

Assignees

• Mastercard International Inc

Inventors

• Unser Kenny
• Bernard Serge
• Malgatti Nikhil

Classifications

• G06Q30/0255

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• G06Q30/0269

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