METHOD AND SYSTEM FOR INFERRING AN INDIVIDUAL CARDHOLDER'S DEMOGRAPHIC DATA FROM SHOPPING BEHAVIOR AND EXTERNAL SURVEY DATA USING A BAYESIAN NETWORK

• US 20150220951A1
• Filed: 02/25/2015
• Published: 08/06/2015
• Est. Priority Date: 01/21/2009
• Status: Abandoned Application

• Current Assignee: Truaxis, Inc. (MasterCard Incorporated)
• Original Assignee: Truaxis, Inc. (MasterCard Incorporated)
• Inventors: Kurapati, Kaushal, Zhang, Fan, Kashyap, Abhijith Ramesh
• Application Number:
  US14/631,130
• Publication Number:
  US 20150220951A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: H04M 15/44 Augmented, consolidated or itemized billing statement or bill presentation
• CPC Class Codes: H04M 15/58 based on statistics of usage or network monitoring
• CPC Class Codes: H04M 15/745 Customizing according to wishes of subscriber, e.g. friends or family
• CPC Class Codes: H04M 15/80 Rating or billing plans; Tariff determination aspects
• CPC Class Codes: H04M 15/8011 using class of subscriber
• CPC Class Codes: H04M 15/8044 Least cost routing
• CPC Class Codes: H04M 15/805 Bidding
• CPC Class Codes: H04M 15/8083 involving reduced rates or discounts, e.g. time-of-day reductions or volume discounts
• CPC Class Codes: H04M 15/83 Notification aspects
• CPC Class Codes: H04M 15/84 Types of notifications
• CPC Class Codes: H04M 15/85 characterised by the type of condition triggering a notification
• CPC Class Codes: H04M 15/851 Determined tariff
• CPC Class Codes: H04M 2215/0104 Augmented, consolidated or itemised billing statement, e.g. additional billing information, bill presentation, layout, format, e-mail, fax, printout, itemised bill per service or per account, cumulative billing, consolidated billing
• CPC Class Codes: H04M 2215/0108 Customization according to wishes of subscriber, e.g. customer preferences, friends and family, selecting services or billing options, Personal Communication Systems [PCS]
• CPC Class Codes: H04M 2215/018 On-line real-time billing, able to see billing information while in communication, e.g. via the internet
• CPC Class Codes: H04M 2215/0184 involving reduced rates or discounts, e.g. time-of-day reductions, volume discounts, cell discounts, group billing, frequent calling destination(s) or user history list
• CPC Class Codes: H04M 2215/0188 Network monitoring; statistics on usage on called/calling number
• CPC Class Codes: H04M 2215/74 : Rating aspects, e.g. rating parameters or tariff determination apects
• CPC Class Codes: H04M 2215/7407 : class of subscriber
• CPC Class Codes: H04M 2215/745 : Least cost routing, e.g. Automatic or manual, call by call or by preselection
• CPC Class Codes: H04M 2215/7457 : Biding
• CPC Class Codes: H04M 2215/81 : Notifying aspects, e.g. notifications or displays to the user
• CPC Class Codes: H04M 2215/8129 : Type of notification
• CPC Class Codes: H04M 2215/815 : Notification when a specific condition, service or event is met

Abstract

Disclosed herein is a method for identifying a member of a group with particular demographic characteristics. The method involves identifying purchasing behaviors of members of a group with particular demographic characteristics via market research, gathering transaction data from a financial account of a user at a financial institution for processing transactions of the user with the multiple of merchants, using the market research to set prior probabilities that a user is a member of the group in a Bayesian belief network, analyzing the transaction data of the user via a Bayesian belief network to determine whether the transaction data of the user indicates that the user is an individual member of the group with the particular demographic characteristics, and presenting to the user at least one of an offer and a savings opportunity from a merchant seeking to contact members of the group with the particular demographic characteristics.

Claims

1. A method for identifying a member of a group with particular demographic characteristics, comprising:

identifying purchasing behaviors of members of a group with particular demographic characteristics via market research;

gathering transaction data from a financial account of a user at a financial institution for processing transactions of the user with the multiple of merchants;

using the market research to set prior probabilities that a user is a member of the group in a Bayesian belief network;

analyzing the transaction data of the user via a Bayesian belief network to determine whether the transaction data of the user indicates that the user is an individual member of the group with the particular demographic characteristics; and

presenting to the user at least one of an offer and a savings opportunity from a merchant seeking to contact members of the group with the particular demographic characteristics.

2. The method as recited in claim 1, wherein the particular demographic characteristics include gender.

3. The method as recited in claim 1, wherein the particular demographic characteristics includes race.

4. The method as recited in claim 1, wherein the particular demographic characteristics include ethnicity.

5. The method as recited in claim 1, wherein the particular demographic characteristics include an age range.

6. The method as recited in claim 1, wherein the particular demographic characteristics include family income.

7. The method as recited in claim 1, wherein the particular demographic characteristics include personal income.

8. The method as recited in claim 1, wherein the particular demographic characteristics include educational level.

9. The method as recited in claim 1, wherein gathering transaction data from the financial account of the user includes a past spend history.

10. The method as recited in claim 1, wherein gathering transaction data from the financial account of the user includes avoiding any personally identifiable information.

11. The method as recited in claim 1, wherein user is a particular credit card holder.

12. A method for identifying a member of a group with particular demographic characteristics, comprising:

identifying purchasing behaviors of members of a group with particular demographic characteristics via survey data from each of a multiple of merchants;

gathering transaction data from a financial account of a user at a financial institution for processing transactions of the user with the multiple of merchants;

analyzing the transaction data of the user via a Bayesian belief network and the survey data from each of the multiple of merchants to determine whether the transaction data of the user indicates that the user is an individual member of the group with the particular demographic characteristics; and

presenting to the user a savings opportunity from a merchant seeking to contact members of the group with the particular demographic characteristics.

13. The method as recited in claim 12, wherein gathering transaction data from the financial account of the user includes avoiding any personally identifiable information.

14. The method as recited in claim 12, wherein user is a particular credit card holder.

15. A method for identifying a member of a group with particular demographic characteristics, comprising:

identifying purchasing behaviors of members of a group with particular demographic characteristics via survey data from each of a multiple of merchants;

gathering transaction data without personally identifiable information from a financial account of a user at a financial institution for processing transactions of the user with the multiple of merchants;

analyzing the transaction data of the user via a Bayesian belief network and the survey data from each of the multiple of merchants to determine whether the transaction data of the user indicates that the user is an individual member of the group with the particular demographic characteristics; and

presenting to the user, via an online statement of the financial account of the user, a savings opportunity from a merchant seeking to contact members of the group with the particular demographic characteristics.

16. The method as recited in claim 15, wherein analyzing the transaction data includes a Markov process.

17. The method as recited in claim 15, wherein analyzing the transaction data includes iterating probabilities until a predetermined stabilization occurs.