METHOD AND SYSTEM FOR DETERMINING WHEN AND WHAT TIME TO GO SHOPPING

• US 20160063513A1
• Filed: 08/28/2014
• Published: 03/03/2016
• Est. Priority Date: 08/28/2014
• Status: Abandoned Application

• Current Assignee: Mastercard International Incorporated (MasterCard Incorporated)
• Original Assignee: Mastercard International Incorporated (MasterCard Incorporated)
• Inventors: Lee, Edward M., Berntsson, Kent Olof Niklas, Polycarpe, Luckner
• Application Number:
  US14/471,923
• Publication Number:
  US 20160063513A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data

Abstract

A method and a system are provided for determining when and what time a shopper should go shopping at a merchant. In particular, the present disclosure provides a method and a system for conveying to a shopper various shopping patterns by date and time, and by gender or age group, of a plurality of payment card holders at one or more merchants, based on one or more payment card holder purchase behaviors, to enable the shopper to select a date and time, or gender or age group, for shopping at the one or more merchants. The method and system can be used by shoppers to choose the preferred date/time, or gender or age group of fellow shoppers, to visit a specific merchant.

Claims

1. A method comprising:

retrieving from one or more databases a first set of information comprising payment card transaction information of a plurality of payment card holders;

retrieving from one or more databases a second set of information comprising merchant information of one or more merchants;

retrieving from one or more databases a third set of information comprising external information;

analyzing the first set of information, the second set of information, and the third set of information to identify one or more associations between the payment card transaction information, the merchant information and the external information;

identifying one or more payment card holder purchase behaviors based on the one or more associations; and

determining shopping patterns by date and time of the plurality of payment card holders at the one or more merchants based on the one or more payment card holder purchase behaviors.

2. The method of claim 1, further comprising:

conveying to an entity the shopping patterns by date and time of the plurality of payment card holders based on the one or more payment card holder purchase behaviors, to enable the entity to select a date and time for shopping at the one or more merchants.

3. The method of claim 1, further comprising:

conveying to an entity the shopping patterns by gender or age of the plurality of payment card holders based on the one or more payment card holder purchase behaviors, to enable the entity to select a date and time for shopping at the one or more merchants.

4. The method of claim 3, wherein the entity is a payment card holder, and wherein the conveying to the payment card holder is carried out by e-mails, text messages, phone calls, television or internet.

5. The method of claim 1, wherein the external information comprises geographic data and demographic data.

6. The method of claim 1, wherein the payment card transaction information comprises at least date of payment card transaction, time of payment card transaction, and payment card number, wherein the merchant information comprises at least merchant name and merchant geolocation, and wherein the external information comprises at least gender of payment card holder and age of payment card holder.

7. The method of claim 1, further comprising algorithmically analyzing the first set of information, the second set of information, and the third set of information to identify one or more associations amongst the payment card transaction information, the merchant information and the external information;

algorithmically identifying one or more payment card holder purchase behaviors based on the one or more associations; and

algorithmically determining shopping patterns by date and time of the plurality of payment card holders at the one or more merchants based on the one or more payment card holder purchase behaviors.

8. The method of claim 1, further comprising creating one or more datasets to store information relating to the one or more associations amongst the payment card transaction information, the merchant information and the external information, the one or more payment card holder purchase behaviors, and the shopping patterns by date and time of the plurality of payment card holders at the one or more merchants.

9. The method of claim 1, further comprising developing logic for analyzing the first set of information, the second set of information, and the third set of information to identify one or more associations amongst the payment card transaction information, the merchant information and the external information, and applying the logic to a universe of payment card holders to identify one or more payment card holder purchase behaviors of the universe of payment card holders, and to determine shopping patterns by date and time of the universe of payment card holders.

10. The method of claim 9, further comprising quantifying the strength of the one or more associations amongst the payment card transaction information, the merchant information and the external information, to identify the strength of the one or more payment card holder purchase behaviors and the shopping patterns by date and time of the plurality of payment card holders at the one or more merchants.

11. The method of claim 9, further comprising assigning attributes to the one or more associations amongst the payment card transaction information, the merchant information, and the external information, wherein the attributes are selected from the group consisting of one or more of confidence, time, and frequency.

12. The method of claim 1, wherein the one or more associations amongst the payment card transaction information, the merchant information, and the external information, the one or more payment card holder purchase behaviors, and the shopping patterns by date and time of the plurality of payment card holders are constructed by statistical analysis selected from the group consisting of clustering, regression, correlation, segmentation, and raking.

13. The method of claim 1, further comprising algorithmically constructing the one or more associations amongst the payment card transaction information, the merchant information, and the external information, the one or more payment card holder purchase behaviors, and the shopping patterns by date and time of the plurality of payment card holders.

14. A system comprising:

one or more databases configured to store a first set of information comprising payment card transaction information of a plurality of payment card holders;

one or more databases configured to store a second set of information comprising merchant information of one or more merchants;

one or more databases configured to store a third set of information comprising external information;

a processor configured to;

analyze the first set of information, the second set of information, and the third set of information to identify one or more associations amongst the payment card transaction information, the merchant information and the external information;

identify one or more payment card holder purchase behaviors based on the one or more associations; and

determine shopping patterns by date and time of the plurality of payment card holders at the one or more merchants based on the one or more payment card holder purchase behaviors.

15. The system of claim 14, wherein the processor is configured to:

convey to an entity the shopping patterns either (a) by date and time of the plurality of payment card holders based on the one or more payment card holder purchase behaviors, to enable the entity to select a date and time for shopping at the one or more merchants, or (b) by gender or age of the plurality of payment card holders based on the one or more payment card holder purchase behaviors, to enable the entity to select a date and time for shopping at the one or more merchants.

16. The system of claim 15, wherein the entity is a payment card holder, and wherein the conveying to the payment card holder is carried out by e-mails, text messages, phone calls, television or internet.

17. The system of claim 14, wherein the payment card transaction information comprises at least date of payment card transaction, time of payment card transaction, and payment card number, wherein the merchant information comprises at least merchant name and merchant geolocation, and wherein the external information comprises at least gender of payment card holder and age of payment card holder.

18. The system of claim 14, wherein the processor is configured to perform one or more functions selected from the group consisting of:

(a) algorithmically analyze the first set of information, the second set of information, and the third set of information to identify one or more associations between the payment card transaction information, the merchant information and the external information;

algorithmically identify one or more payment card holder purchase behaviors based on the one or more associations; and

algorithmically determine shopping patterns by date and time of the plurality of payment card holders at the one or more merchants based on the one or more payment card holder purchase behaviors;

(b) create one or more datasets to store information relating to the one or more associations between the payment card transaction information, the merchant information and the external information, the one or more payment card holder purchase behaviors, and the shopping patterns by date and time of the plurality of payment card holders at the one or more merchants;

(c) to develop logic for analyzing the first set of information, the second set of information, and the third set of information to identify one or more associations between the payment card transaction information, the merchant information and the external information, and applying the logic to a universe of payment card holders to identify one or more payment card holder purchase behaviors of the universe of payment card holders, and to determine shopping patterns by date and time of the universe of payment card holders;

(d) quantify the strength of the one or more associations between the payment card transaction information, the merchant information and the external information to identify the strength of the one or more payment card holder purchase behaviors and the shopping patterns by date and time of the plurality of payment card holders at the one or more merchants;

(e) assign attributes to the one or more associations between the payment card transaction information, the merchant information, and the external information, wherein the attributes are selected from the group consisting of one or more of confidence, time, and frequency; and

(f) algorithmically construct the one or more associations between the payment card transaction information, the merchant information, and the external information, the one or more payment card holder purchase behaviors, and the shopping patterns by date and time of the plurality of payment card holders

19. The system of claim 14, wherein the one or more associations amongst the payment card transaction information, the merchant information, and the external information, the one or more payment card holder purchase behaviors, and the shopping patterns by date and time of the plurality of payment card holders are constructed by statistical analysis selected from the group consisting of clustering, regression, correlation, segmentation, and raking.

20. A method for generating one or more predictive behavioral models, the method comprising:

retrieving from one or more databases a first set of information comprising payment card transaction information of a plurality of payment card holders;

retrieving from one or more databases a second set of information comprising merchant information of one or more merchants;

retrieving from one or more databases a third set of information comprising external information;

analyzing the first set of information, the second set of information, and the third set of information to identify one or more associations between the payment card transaction information, the merchant information and the external information;

identifying one or more payment card holder purchase behaviors based on the one or more associations;

determining shopping patterns by date and time of the plurality of payment card holders at the one or more merchants based on the one or more payment card holder purchase behaviors; and

generating one or more predictive behavioral models based on the shopping patterns by date and time of the plurality of payment card holders at the one or more merchants.