SYSTEMS AND METHODS FOR OFFER SCORING

• US 20130325608A1
• Filed: 05/29/2013
• Published: 12/05/2013
• Est. Priority Date: 01/21/2009
• Status: Abandoned Application

• Current Assignee: Truaxis, Inc. (MasterCard Incorporated)
• Original Assignee: Truaxis, Inc. (MasterCard Incorporated)
• Inventors: Kothari, Samir, Kulkarni, Nilesh Vijay, Somashekar, Manjunath Sulibele, Shah, Akshit Mukesh
• Application Number:
  US13/904,548
• Publication Number:
  US 20130325608A1
• Time in Patent Office: Days
• US Class Current: 705/14.53
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: H04M 15/00 Arrangements for metering, time-control or time indication ; Metering, charging or billing arrangements for voice wireline or wireless communications, e.g. VoIP
• 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 providing users with saving opportunities of interest. The method includes developing a predictive model of user purchase behavior, evaluating a plurality of available savings opportunities from a savings opportunity data base using the model of user purchase behavior, selecting from the plurality of savings opportunities one with the highest likelihood of acceptance and communicating the selected savings opportunity to the user.

Claims

1. A method for providing users with saving opportunities of interest, comprising:

developing a predictive model of user purchase behavior;

identifying a savings opportunity from a database based on the model; and

communicating the savings opportunity to the user.

2. The method of claim 1, wherein the model is developed using historic user transaction data.

3. The method of claim 1, wherein the model comprises at least one of merchant category preferences, transaction category preferences, product category preferences, merchant preferences, geographic locations, seasonal variety, spending level and recent changes from historic spending patterns.

4. The method of claim 1, wherein the model is developed using historic user transaction data and data related to past user responses to savings opportunities.

5. The method of claim 1, further comprising:

gathering public or inferred data relevant to the user, wherein the model is further developed using the public or inferred data.

6. The method of claim 1, wherein the predictive model is developed using machine learning techniques.

7. The method of claim 6, wherein the machine learning techniques comprise at least one of logistic regression, neural nets, lasso algorithms, elastic-net regularized generalized linear models, support vector machines (SVM), ensembles of decision trees, and random forests.

8. The method of claim 1, wherein the savings opportunity is communicated to the user via one or more of a financial institution web site, a financial institution application, a separate application, and a digital wallet.

9. A method for providing users with saving opportunities of interest, comprising:

developing a predictive model of user purchase behavior;

evaluating a plurality of available savings opportunities from a savings opportunity data base using the model of user purchase behavior;

selecting from the plurality of savings opportunities the savings opportunity with the highest likelihood of acceptance; and

communicating the selected savings opportunity to the user.

10. The method of claim 9, wherein the model comprises at least one of merchant category preferences, transaction category preferences, product category preferences, merchant preferences, geographic locations, seasonal variety, spending level and recent changes from historic spending patterns.

11. The method of claim 9, further comprising:

gathering data on one or more past user responses to one or more savings opportunities, wherein the model is further developed using the past user response data.

12. The method of claim 9, further comprising:

gathering public or inferred data relevant to the user, wherein the model is further developed using the public or inferred data.

13. The method of claim 9, wherein the selected savings opportunity is communicated to the user via at least one of a financial institution web site, a financial institution application, a separate application, and a digital wallet.