Enhancing insight-driven customer interactions with an engine

• US 20070083418A1
• Filed: 03/26/2004
• Published: 04/12/2007
• Est. Priority Date: 03/26/2004
• Status: Active Grant

• Current Assignee: Accenture Global Services Limited (Accenture PLC)
• Original Assignee: Accenture Global Services GmbH (Accenture PLC)
• Inventors: Lew, Steven, Shapiro, David, Dell'Anno, Vincent, Quiring, Kevin, Wollan, Robert, Whitsett, Rodney, Palmer, Dawn, Kornfeld, Alyse, Usman, Sajid, Berg, Tore, Slaw, David, Hernandez, Julio
• Granted Patent: US 8,103,530 B2
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G06Q 30/00 Commerce
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data

Abstract

Insight-driven interactions with customers may be enhanced in a holistic approach. A customer relationship management (“CRM”) methodology may include: (1) evaluating a customer strategy; (2) identifying customer segments from a customer base; (3) forming an interaction strategy; (4) defining a series of experiences based on the strategy; (5) applying those interactions with customers during interactions; and (6) monitoring the results of the customer interactions. A computer aid may preferably guide a user through some of these steps. A modular, vendor-independent, centralized, rules-based engine may perform processing to deliver tailored customer experiences, relying on values for prioritized experiences identified through use of the computer aid.

Claims

1. A method for optimizing customer interactions, comprising:

interfacing with a plurality of communication channels;

receiving at least one request from at least one customer for a customer interaction over at least one of the plurality of communication channels;

identifying the at least one customer;

choosing at least one treatment for processing the customer interaction using a central processing engine that is consistent for the plurality of communication channels; and

processing the customer interaction based on the at least one treatment chosen.

2. The method from claim 1, wherein choosing the at least one treatment is a function of a customer segment, an interaction type and an interaction channel.

3. The method from claim 1, further comprising:

inserting data correlating to the at least one treatment into a customer intelligence record; and

returning the customer intelligence record to one of the plurality communication channels for instructing the channel on the treatments to present to the customer.

4. The method from claim 1, wherein the step of choosing at least one treatment accesses a central repository where treatments have been stored by an independent design tool.

5. The method from claim 1, wherein the central processing engine processes grouped rules in a hierarchy.

6. The method from claim 1, further comprising sending the at least one treatment to one of the plurality of communication channels via a plurality of services.

7. The method from claim 1, wherein the step of choosing at least one treatment comprises choosing at least one treatment that has been derived from insight about customers using analytical models.

8. The method from claim 7, wherein the step of leveraging insight from analytical models comprises:

extracting customer data for a plurality of customers from at least one database;

training analytical models to predict customer behavior, wherein the analytical models are trained using the customer data extracted from at least one database;

gathering the customer interaction results; and

retraining the analytic models to refine the customer behavior prediction, wherein the analytical models are re-trained using the customer data extracted from at least one database as well as the customer interaction results.

9. A computer program stored on a computer readable medium for execution by a computer, the computer program comprising:

a code segment for receiving at least one request from at least one customer for a customer interaction over at least one of the plurality of communication channels;

a code segment for identifying the at least one customer;

a code segment for choosing at least one treatment for processing the customer interaction using a central processing engine that is consistent for the plurality of communication channels; and

a code segment for processing the customer interaction based on the at least one treatment chosen.

10. The computer program from claim 9, wherein the code segment for choosing the at least one treatment leverages a function of a customer segment, an interaction type and an interaction channel.

11. The computer program from claim 9, further comprising:

a code segment for inserting data correlating to the at least one treatment into a customer intelligence record; and

a code segment for returning the customer intelligence record to one of the plurality communication channels for instructing the channel on the treatments to present to the customer.

12. The computer program from claim 9, wherein the code segment for choosing at least one treatment accesses a central repository where treatments have been stored by an independent design tool.

13. The computer program from claim 9, wherein the central processing engine processes grouped rules in a hierarchy.

14. The computer program from claim 9, further comprising a code segment for sending the at least one treatment to one of the plurality of communication channels via a plurality of services.

15. The computer program from claim 9, wherein the code segment for choosing at least one treatment comprises a code segment choosing at least one treatment that has been derived from insight about customers using analytical models.

16. The computer program from claim 15, wherein the code segment for leveraging insight from analytical models comprises:

a code segment for extracting customer data for a plurality of customers from at least one database;

a code segment for training analytical models to predict customer behavior, wherein the analytical models are trained using the customer data extracted from at least one database;

a code segment for gathering the customer interaction results; and

a code segment for retraining the analytic models to refine the customer behavior prediction, wherein the analytical models are re-trained using the customer data extracted from at least one database as well as the customer interaction results.

17. A system for optimizing customer interactions, comprising:

a channel layer for communicating with a plurality of communication channels;

a services layer for processing at least one request from at least one customer for a customer interaction over at least one of the plurality of communication channels, and for identifying the at least one customer; and

an interaction optimizing subsystem for choosing at least one treatment for processing the customer interaction;

wherein the interaction optimizing subsystem comprises a central processing engine that is consistent for the plurality of communication channels and for processing the customer interaction based on the at least one treatment chosen.

18. The system from claim 17, wherein the interaction optimizing subsystem chooses the at least one treatment as a function of a customer segment, an interaction type and an interaction channel.

19. The system from claim 17, further comprising:

a customer intelligence record that is used by the interaction optimizing subsystem and is returned to one of the plurality of communication channels for instructing the channel on the treatments to present to the customer;

wherein data correlating to the at least one treatment is stored in the customer intelligence record.

20. The system from claim 17, wherein the interaction optimizing subsystem chooses at least one treatment from a central repository;

wherein the treatments have been stored on the central repository by an independent design tool.

21. The system from claim 17, wherein the central processing engine processes grouped rules in a hierarchy.

22. The system from claim 17, wherein the interaction optimizing subsystem comprises a plurality of services for sending the at least one treatment to one of the plurality of communication channels.

23. The system from claim 17, wherein the interaction optimizing subsystem chooses the at least one treatment that has been derived from insight about customers using analytical models.

24. The system from claim 23, further comprising:

at least one database upon which is stored customer data for a plurality of customers;

wherein the interaction optimizing subsystem further comprises a results gathering module for gathering customer interaction results;

wherein the gathered customer interaction results are used to re-trained the analytical models.