System and method for suggesting interaction strategies to a customer service representative

• US 20020087385A1
• Filed: 12/28/2000
• Published: 07/04/2002
• Est. Priority Date: 12/28/2000
• Status: Abandoned Application

• Current Assignee: Teradata US, Inc. (Teradata Corporation)
• Original Assignee: Teradata US, Inc. (Teradata Corporation)
• Inventors: Vincent, Perry G.
• Application Number:
  US09/750,948
• Publication Number:
  US 20020087385A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: H04M 3/5191 interacting with the Internet

Abstract

A system and methods for suggesting interaction strategies to customer service representatives in a customer relationship management environment which includes analyzing customer data to determine one or more patterns and generating a set of rules based upon the patterns. A recommendation engine is used to compare the rules to a current customer interaction to recognize one or more of the patterns in the interaction. Real-time interaction strategies are then suggested which correspond to the recognized patterns.

Claims

1. A method of suggesting an interaction strategy to a customer service representative in a customer relationship management environment, said method comprising the steps of:

analyzing customer data to determine one or more patterns;

generating a set of rules based upon said patterns;

identifying a current customer interaction;

applying said rules to said current customer interaction to recognize one or more of said patterns in said interaction; and

suggesting an interaction strategy corresponding to said recognized patterns.

2. The method of claim 1, wherein said applying step further comprises inputting data from said current customer interaction and recognizing one or more of said patterns from said input data.

3. The method of claim 2, wherein said applying step further comprises using a recommendation engine to recognize said patterns in said input data.

4. The method of claim 3, wherein said recommendation engine recognizes said patterns from said input data in real-time.

5. The method of claim 1, wherein said customer data includes a customer interaction history with said business.

6. The method of claim 1, wherein said patterns are individually determined for customers of said business.

7. The method of claim 1, further comprising the step of capturing said customer data from a plurality of different interaction data sources.

8. The method of claim 7, wherein said interaction channels are both virtual and physical.

9. The method of claim 1, wherein said current customer interaction is a telephone contact with a call center representative.

10. The method of claim 1, wherein said current customer interaction is through a self-service application.

11. A system for recommending a strategy for managing a customer interaction, said system comprising:

a plurality of interaction channels for capturing customer data;

one or more data analysis tools comprising executable instructions for analyzing said customer data from said plurality of channels and determining one or more patterns from said data; and

a recommendation engine for analyzing a current customer interaction and recognizing one or more of said patterns in said interaction, said recommendation engine recommending strategies corresponding to said recognized patterns.

12. The system of claim 11, further comprising an interaction management application for directing said customer interaction, said application including a user interface for inputting data regarding said current interaction.

13. The system of claim 12, wherein said user interface includes a first display panel for inputting notes regarding said interaction and a second display panel for displaying recommended strategies from said recommendation engine.

14. The system of claim 13, wherein said recommendation engine uses said interaction notes to determine said recommended strategies.

15. The system of claim 11, further comprising a configuration tool for developing scripts corresponding to said recommended strategies.

17. The method of claim 16, wherein said recommendation engine detects said affinities and recommends said interaction strategies in real-time.

18. The method of claim 17, wherein said recommendation uses a context of the current customer interaction to detect affinities to said patterns.

19. The method of claim 17, further comprising the step of inputting information from the current customer interaction and using said input information to detect affinities to said patterns.

20. The method of claim 16, wherein said patterns include customer product ownership, customer interaction history, customer interaction behavior, and product affinities.

16. A method of suggesting an interaction strategy to a customer service representative in an automated customer relationship management environment, said method comprising the steps of:

storing customer data from a plurality of different interaction sources;

analyzing said customer data to determine one or more patterns;

identifying a current customer interaction;

using a recommendation engine to detect affinities between the current customer interaction and said patterns; and

recommending an interaction strategy based on any detected affinities.