System and method of obtaining merchant sales information for marketing or sales teams

• US 10,504,126 B2
• Filed: 05/29/2013
• Issued: 12/10/2019
• Est. Priority Date: 01/21/2009
• Status: Active Grant

• Current Assignee: Truaxis, Inc. (MasterCard Incorporated)
• Original Assignee: Truaxis, Inc. (MasterCard Incorporated)
• Inventors: Kulkarni, Nilesh Vijay, Kothari, Samir, Thornton, John Michael
• Primary Examiner(s): Marcus, Leland
• Application Number:
  US13/904,624
• Publication Number:
  US 20130325548A1
• Time in Patent Office: 2,386 Days
• Field of Search: None
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• 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 of obtaining merchant sales information for marketing or sales teams. Including accessing transaction data from one or more financial institutions and extracting metadata associated with the transaction data in accordance with at least one rule. The metadata is then analyzed to identify transaction data associated with one or more merchants. The transaction data associated with the one or more merchants is provided to the marketing or sales teams.

Claims

1. A method for providing a geo-enhanced savings opportunity to a mobile device, comprising:

obtaining, with a central server, a request for sales information for one or more merchants;

accessing, with the central server, transaction data of multiple customers including sales information from multiple financial institutions;

generating, with the central server, processed transaction data by executing a variable extraction process on the transaction data including;

generating sorted transaction data by sorting the transaction data based on criteria,

generating consolidated transaction data by consolidating the sorted transaction data after removing duplicate entries therein, and

generating merged transaction data by merging previous customer data after the step of sorting or consolidating;

generating, with the central server, a customer model for each of the multiple customers by executing a machine learning process on the processed transaction data, the customer model identifying a relative likelihood of a customer to act upon a plurality of offers;

identifying, with the central server, deals offered by the one or more merchants to the customer based on the customer model, the deals including a geo-enhanced savings opportunity;

determining, with the central server, whether the customer has used an initial savings opportunity presented to the customer;

receiving, with a customer interface of the central server, location data from a mobile device of the customer, the location data indicating that the customer has entered a geographic location set by a merchant offering the geo-enhanced savings opportunity; and

transmitting, with the central server, the geo-enhanced savings opportunity to the mobile device in response to determining that the customer has not used the initial savings opportunity presented to the customer and in response to receiving the location data.

2. The method of claim 1, further comprising:

extracting, with the one or more processors, metadata associated with the transaction data in accordance with at least one rule;

analyzing, with the central server, the metadata to identify transaction data associated with one or more merchants;

correcting, with the central server, the metadata concerning the one or more merchants using the at least one rule via a Radix tree to correctly identify each of the one or more merchants over multiple transactions; and

providing the corrected metadata to a sales team via a sales dashboard associated with the central server.

3. The method of claim 2, further comprising analyzing the metadata using one or more of a constant-time data structure, a Radix tree, a Lucene tree or fuzzy logic.

4. The method of claim 3, further comprising creating the constant-time data structure by using segmented transaction data related to the one or more merchants as an input during creation of the constant-time data structure.

5. The method of claim 4, wherein the transaction data is segmented by at least one of a merchant and a location.

6. The method of claim 4, further comprising assigning a unique ID to at least one of a merchant and a location found in the segmented transaction data.

7. The method of claim 6, further comprising adding the unique ID to a constant-time database for future searching.

8. The method of claim 3, wherein the constant-time data structure comprises a search of the Lucene tree.

9. The method of claim 8, wherein the constant-time data structure comprises a fuzzy logic method after the Lucene tree.

10. The method of claim 2, wherein the at least one rule is a processing rule for conversion among letters, numbers and characters in extracting and correcting the metadata.

11. The method of claim 2, further comprising analyzing the corrected metadata and the transaction data associated with the one or more merchants to determine sales trends over a period of time, wherein the sales trends comprise at least one of revenue, profitability, number of sales, seasonal sales, sales by location, and sales during an offer campaign.

12. The method of claim 2, further comprising determining a geographic location of the one or more merchants over multiple transactions via a Radix tree.

13. The method of claim 2, wherein the metadata is extracted using a description splitter.

14. The method of claim 13, further comprising generating by the description splitter a sequence of tokens relating to a location of each of the one or more merchants for searching for the location of each of the one or more merchants in each of the transaction data.

15. The method of claim 13, further comprising generating by the description splitter a sequence of tokens relating to a name of each of the one or more merchants for searching for the name of the merchant in each of the transaction data.

16. The method of claim 13, wherein the description splitter is adapted to include a location tokenizer and a merchant tokenizer, the location tokenizer for generating tokens relating to a location of each of the one or more merchants and the merchant tokenizer for generating tokens relating to a similar name for each of the one or more merchants.

17. The method of claim 1, the variable extraction process including one or more of a hadoop reduce map, a relational database, and an elastic map reduce.

18. The method of claim 1, the machine learning process including one or more of logistic regression, neural nets, algorithms such as lasso, elastic-net regularized generalized linear and non-linear models, support vector machines (SVM), ensembles of decision trees, and random forests processes.

19. The method of claim 1, the previous customer data including tracked responses of the customer to previously presented savings opportunities.

20. The method of claim 1, the previous customer data including tracked responses of a first customer to a shared savings opportunity, and tracked response of a second customer to the shared savings opportunity received by the second customer from the first customer.

21. The method of claim 1, further comprising:

providing the one or more merchants and the deals to a sales team via a sales dashboard associated with the central server.