TECHNIQUES FOR REAL-TIME CUSTOMER PREFERENCE LEARNING

• US 20130159224A1
• Filed: 12/20/2011
• Published: 06/20/2013
• Est. Priority Date: 12/20/2011
• Status: Active Grant

• Current Assignee: NCR Voyix Corporation
• Original Assignee: NCR Corporation
• Inventors: Blake, Norman Perkins, Koser, Vincent Robert
• Granted Patent: US 8,832,008 B2
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06Q 30/01 Customer relationship services
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data

Abstract

Techniques for real-time offer customer preference learning are presented. Local agents on communication channels are equipped with predefined rules that capture actions and behaviors of customers interacting with an enterprise. The metrics associated with these actions and behaviors are plugged into the rules and in some cases combined with known pre-existing preferences for the customers for purposes of evaluating the rules and creating newly learned preferences for the customers. The newly learned preferences are dynamically fed into offer evaluation processing to determine whether to make offers to the customers.

Claims

1. A processor-implemented method programmed in a non-transitory processor-readable medium and to execute on a device, which is configured to execute the method, comprising:

identifying a customer engaging in an interaction with the device;

obtaining predefined rules pertinent to preference determination for preferences of the customer;

gathering actions of the customer during the interaction;

feeding metrics for the actions to the predefined rules; and

evaluating the predefined rules using the metrics to learn a new preference to assign to the customer or to update an existing preference value for an existing preference based on evaluation of the predefined rules.

2. The method of claim 1 further comprising, using loyalty rules with the new preference or the updated existing preference value to make a dynamic offer to the customer.

3. The method of claim 1 further comprising, dynamically updating an enterprise preference manager with the new preference or the updated existing preference value for distributing to any channel that the customer subsequently interacts through.

4. The method of claim 1, wherein identifying further includes processing the interaction on the device, where the device is one of:

a self-service kiosk, an Automated Teller Machine (ATM), a mobile device of the customer, and a website portal server.

5. The method of claim 1, wherein identifying further includes obtaining at a start of the interaction a loyalty account for the customer, via one or more of:

a loyalty scan of a loyalty card, manual entry of the loyalty account to the device, and/or identifying information scanned or entered that assists in automatically identifying the customer to the loyalty account.

6. The method of claim 1, wherein obtaining further includes requesting the predefined rules that are tied to the preference determinations via an Application Programming Interface (API) to a loyalty database.

7. The method of claim 1, wherein gathering further includes monitoring items purchased and selections made by the customer over a present communication channel that the customer is using to acquire the metrics for the actions.

8. The method of claim 1, wherein feeding further includes populating the metrics and existing preference values associated with the customer into variables defined in the predefined rules.

9. The method of claim 8 further comprising, acquiring the existing preference values from an enterprise preference manager.

10. The method of claim 8 further comprising, acquiring the existing preference values from a loyalty database agent that acquired the existing preference values on behalf of the method from an enterprise preference manager.

11. The method of claim 1, wherein evaluating further includes dynamically and in real time evaluating the predefined rules with the metrics before the interaction concludes with the customer.

12. The method of claim 1 further comprising, altering an existing offer that was to be presented to the customer based on the presence of the new preference or the updated existing preference value from the existing preference.

13. The method of claim 1 further comprising, providing another offer in addition to an existing offer to the customer before the interaction concludes based on the presence of the new preference or the updated existing preference value from the existing preference.

14. A processor-implemented method programmed in a non-transitory processor-readable medium and configured to execute on one or more processors, the method comprising:

receiving a request for preferences tied to a customer interacting with an enterprise;

acquiring the preferences from an enterprise preference manager;

delivering the preferences to a client loyalty preference agent interacting with the customer; and

receiving updates to the preferences in real time from the client loyalty preference agent based on actions taken by the customer when interacting with the client loyalty preference agent.

15. The method of claim 14, wherein receiving further includes receiving the request from the client loyalty preference agent, the device of the client loyalty preference agent is one of:

a customer mobile device and an enterprise terminal device.

16. The method of claim 14, wherein receiving further includes receiving the request from an Application Programming Interface (API) of a loyalty database managed by the enterprise and in communication with the client loyalty preference agent.

17. The method of claim 14, wherein receiving further includes authenticating the request, the customer, and/or the client loyalty preference agent based on a policy before proceeding.

18. The method of claim 14 further comprising, generating trend reports for changes in the preferences obtained by mining the history of changes to the preferences.

19. A system, comprising:

a first machine having a client loyalty preference agent implemented in a non-transitory computer-readable storage medium and which processes on the first machine; and

a second machine having a preference learning agent implemented in a non-transitory computer-readable storage medium and which processes on the second machine;

the client loyalty preference agent configured to interact with a customer that is interacting with an enterprise via the first machine to capture actions taken and selections made by the customer, the client loyalty preference agent also configured to apply the actions and selections to the evaluation of predefined rules to determine when new preferences for the customer are to be made and to determine when updates to existing preferences are to be made, the preference learning agent is configured to receive the new preferences and the updates to the existing preferences and make them available in real time across disparate communication channels of an enterprise dynamically.

20. The system of claim 19, wherein the first machine is a mobile phone of the customer and the second machine is located in a cloud processing environment.