SYSTEMS AND METHODS FOR APPENDING PAYMENT NETWORK DATA TO NON-PAYMENT NETWORK TRANSACTION BASED DATASETS THROUGH INFERRED MATCH MODELING

• US 20150220937A1
• Filed: 01/31/2014
• Published: 08/06/2015
• Est. Priority Date: 01/31/2014
• Status: Abandoned Application

• Current Assignee: Mastercard International Incorporated (MasterCard Incorporated)
• Original Assignee: Mastercard International Incorporated (MasterCard Incorporated)
• Inventors: Iannace, Marianne, Polycarpe, Luckner B.
• Application Number:
  US14/169,749
• Publication Number:
  US 20150220937A1
• 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 includes receiving a first data set and a second data set. The first data set may include anonymized transaction data that represents purchase transactions made by customers of a merchant. The second data set may include anonymized transaction data that represents purchase transactions made by cardholders in a payment network. The method further includes filtering the second data set to remove therefrom data relating to cardholders who are not customers of the merchant, and processing the first data set and the filtered second data set using a probabilistic engine to establish linkages between data in the first data set and data in the filtered second data set. The method may also include analyzing data in the filtered second data set to generate one or more of shopping habits data, classification data and attribute data with respect to customers of the merchant.

Claims

1. A method comprising:

receiving a first data set, the first data set including anonymized transaction data representing purchase transactions made by customers of a merchant;

receiving a second data set, the second data set including anonymized transaction data representing purchase transactions made by cardholders in a payment network;

filtering the second data set to remove therefrom data relating to cardholders who are not customers of the merchant;

processing said first data set and said filtered second data set using a probabilistic engine to establish linkages between data in the first data set and data in the filtered second data set;

analyzing data in the filtered second data set for which linkages exist with data in the first data set to generate shopping habits data for customers of the merchant; and

appending the shopping habits data to the first data set.

2. The method of claim 1, wherein the shopping habits data includes data that indicates a proportion of a customer'"'"'s spending with the merchant relative to the customer'"'"'s total spending in a group of merchants that includes the merchant.

3. The method of claim 1, wherein the shopping habits data includes data that indicates a proportion of a customer'"'"'s transactions with the merchant relative to a total number of transactions by the customer in a group of merchants that includes the merchant.

4. The method of claim 1, wherein the shopping habits data is indicative of a proportion of return transactions engaged in by a customer relative to the customer'"'"'s total purchase transactions.

5. The method of claim 1, wherein the shopping habits data is indicative of an average dollar amount of transactions engaged in by the customers in a group of merchants.

6. The method of claim 1, wherein the shopping habits data is indicative of a frequency of transactions engaged in by the customers in a group of merchants.

7. The method of claim 1, wherein the shopping habits data includes data that indicates a proportion of a customer'"'"'s spending with the merchant relative to the customer'"'"'s overall spending in the payment network.

8. The method of claim 1, wherein the shopping habits data includes data that indicates a proportion of a customer'"'"'s spending in online transactions with the merchant relative to the customer'"'"'s overall spending in online transactions in a group of merchants that includes the merchant.

9. The method of claim 1, wherein the shopping habits data includes data that indicates a proportion of a customer'"'"'s online transactions with the merchant relative to the customer'"'"'s overall number of online transactions in a group of merchants that includes the merchant.

10. The method of claim 1, wherein the shopping habits data includes data that indicates a proportion of a customer'"'"'s spending in online transactions for a category of purchases relative to the customer'"'"'s total spending in the category of purchases.

11. The method of claim 1, wherein the shopping habits data includes data indicative of a customer'"'"'s favorite retail segments as indicated by quantity of spending and/or quantity of transactions.

12. A method comprising:

receiving a first data set, the first data set including anonymized transaction data representing purchase transactions made by customers of a merchant;

receiving a second data set, the second data set including anonymized transaction data representing purchase transactions made by cardholders in a payment network;

filtering the second data set to remove therefrom data relating to cardholders who are not customers of the merchant;

processing said first data set and said filtered second data set using a probabilistic engine to establish linkages between data in the first data set and data in the filtered second data set;

analyzing data in the filtered second data set for which linkages exist with data in the first data set to generate classification data for classifying the merchant'"'"'s customers into groups of customers; and

appending the classification data to the first data set.

13. The method of claim 12, wherein the classification data is indicative of the customers'"'"' loyalty to the merchant.

14. The method of claim 12, wherein the classification data is indicative of the customers'"'"' respective total amounts spent for a category of purchases.

15. The method of claim 12, wherein the classification data is indicative of the customers'"'"' respective total amounts spent with a group of merchants.

16. A method comprising:

receiving a first data set, the first data set including anonymized transaction data representing purchase transactions made by customers of a merchant;

receiving a second data set, the second data set including anonymized transaction data representing purchase transactions made by cardholders in a payment network;

filtering the second data set to remove therefrom data relating to cardholders who are not customers of the merchant;

processing said first data set and said filtered second data set using a probabilistic engine to establish linkages between data in the first data set and data in the filtered second data set;

analyzing data in the filtered second data set for which linkages exist with data in the first data set to generate attribute data for indicating at least one attribute of customers of the merchant; and

appending the attribute data to the first data set.

17. The method of claim 16, wherein the attribute data is indicative of a customer'"'"'s behavior with respect to return transactions.

18. The method of claim 16, wherein the attribute data is indicative of a customer'"'"'s degree of loyalty to the merchant.

19. The method of claim 16, wherein the attribute data is indicative of at least one shopping habit of a customer.

20. The method of claim 19, wherein the attribute data is indicative of a customer'"'"'s average transaction amount in a category of transactions.