Enhancing insight-driven customer interactions with an optimizing engine

• US 8,103,530 B2
• Filed: 03/26/2004
• Issued: 01/24/2012
• Est. Priority Date: 03/26/2004
• Status: Active Grant

• Current Assignee: Accenture Global Services Limited (Accenture PLC)
• Original Assignee: Accenture Global Services Limited (Accenture PLC)
• Inventors: Berg, Tore, Slaw, David, Usman, Sajid, Wollan, Robert E., Quiring, Kevin N., Dell'Anno, Vincent U., Hernandez, Julio J., Kornfeld, Alyse S., Lew, Steven L., Palmer, Dawn E., Shapiro, David A., Whitsett, Rodney B.
• Primary Examiner(s): Loftis, Johnna
• Assistant Examiner(s): Parker, Brandi P
• Application Number:
  US10/811,367
• Publication Number:
  US 20070083418A1
• Time in Patent Office: 2,860 Days
• Field of Search: 705/7.11
• US Class Current: 705/7.11
• 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 different types of communication channels;

receiving requests from at least one customer for customer interactions over at least two different types of communication channels;

generating a customer interaction record with a plurality of data fields for the at least one customer, the customer interaction record comprising a batch data section, a customer experience packet section, and a real time data section;

generating an XML document from the customer interaction record;

using a web services layer to pass the XML document to a central, channel-independent rule processing engine;

identifying the at least one customer;

for each requested customer interaction, choosing at least one treatment for processing the customer interaction using the central, channel-independent processing engine;

updating the customer experience packet section of the customer interaction record and the XML document to indicate at least one treatment for the customer;

using the web services layer to send the XML document to a channel to indicate the at least one treatment for the customer, and

processing the customer interactions by the central, channel-independent processing engine, based on the treatments 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, further comprising sending the at least one treatment to one of the plurality of communication channels via a plurality of services.

6. The method from claim 1, wherein the step of choosing at least one treatment comprises leveraging insight about customers from analytical models to derive at least one treatment.

7. The method from claim 6, wherein 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.

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

a code segment for receiving requests from at least one customer for customer interactions over at least two different types of communication channels from a plurality of communication channels;

a code segment for generating a customer interaction record with a plurality of data fields for the at least one customer, the customer interaction record comprising a batch data section, a customer experience packet section, and a real time data section;

a code segment for generating an XML document from the customer interaction record;

a code segment for using a web services layer to pass the XML document to a central, channel-independent rule processing engine;

a code segment for identifying the at least one customer;

a code segment for choosing, for each requested customer interaction, at least one treatment for processing the customer interaction using the central, channel-independent processing engine;

a code segment for updating the customer experience packet section of the customer interaction record and the XML document to indicate at least one treatment for the customer;

a code segment for using the web services layer to send the XML document to a channel to indicate the at least one treatment for the customer, and

a code segment for processing the customer interactions by the central, channel-independent processing engine, based on the treatments chosen.

9. The computer program from claim 8, 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.

10. The computer program from claim 8, 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.

11. The computer program from claim 8, wherein the code segment for choosing at least one treatment accesses a central repository where treatments have been stored by an independent design tool.

12. The computer program from claim 8, 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.

13. The computer program from claim 8, 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.

14. The computer program from claim 13, 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.

15. A system for optimizing customer interactions, comprising:

a computer system configured to provide

a channel layer for communicating with a plurality of different types of communication channels and

a services layer for processing requests from at least one customer for customer interactions over at least two different types of communication channels, and for identifying the at least one customer;

the computer system also including an interaction optimizing subsystem for choosing, for each requested customer interaction, at least one treatment for processing the customer interactions, wherein

the interaction optimizing subsystem comprises a central, channel-independent processing engine and a customer interaction record with a plurality of data fields for a customer;

the customer interaction record comprises a batch data section, a customer experience packet section, and a real time data section, and is used to generate an XML document that is passed to the central, channel-independent processing engine;

the customer experience packet section of the customer interaction record and the XML document are updated to indicate at least one treatment for the customer, and the XML document is sent to a channel to indicate the at least one treatment for the customer; and

the processing for the customer interactions is by the central, channel-independent processing engine, and based on the treatments chosen.

16. The computer-implemented system from claim 15, 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.

17. The system from claim 15, further comprising:

wherein the interaction optimizing subsystem uses a customer intelligence record, which 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.

18. The system from claim 15, 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.

19. The system from claim 15, 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.

20. The system from claim 15, wherein the interaction optimizing subsystem chooses the at least one treatment that has been derived from insight about customers using analytical models.

21. The system from claim 20, 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.