Enhancing insight-driven customer interactions with a workbench

• US 20070239515A1
• Filed: 03/26/2004
• Published: 10/11/2007
• Est. Priority Date: 03/26/2004
• Status: Abandoned Application

• Current Assignee: Accenture Global Services GmbH (Accenture PLC)
• Original Assignee: Accenture Global Services GmbH (Accenture PLC)
• Inventors: Shapiro, David, Dell'Anno, Vincent, Quiring, Kevin, Wollan, Robert, Lew, Steven, Whitsett, Rodney, Palmer, Dawn, Kornfeld, Alyse, Usman, Sajid, Berg, Tore, Slaw, David, Hernandez, Julio
• Application Number:
  US10/810,910
• Publication Number:
  US 20070239515A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 10/0637 Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
• CPC Class Codes: G06Q 10/06393 Score-carding, benchmarking or key performance indicator [KPI] analysis
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0204 Market segmentation

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 defining how to optimize customer experiences, the method comprising:

defining a plurality of prioritized experiences correlating to an interaction strategy, wherein each prioritized experience has at least one associated treatment;

storing the plurality of prioritized experiences for consistent treatment among a plurality of channels;

wherein the step of storing is done to a central repository where the stored experiences are available for application across a plurality of communication channels.

2. The method from claim 1, further comprising:

evaluating a customer strategy for a company;

identifying a plurality of customer segments for a customer base of a company; and

formulating an interaction strategy based on value opportunities.

3. The method from claim 1, further comprising deriving insight about customers from analytical models, wherein defining the prioritized experiences is based on the derived insight.

4. The method from claim 3, wherein the step of deriving 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 analytical model is trained using the customer data extracted from at the east one database;

gathering the customer interaction results; and

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

5. The method from claim 2, wherein evaluating the customer strategy comprises:

evaluating business value drivers;

defining key performance indicators; and

defining business constraints.

6. The method from claim 2, wherein identifying the plurality of customer segments comprises:

segmenting a plurality of customers by behavior data stored in a data warehouse;

segmenting the plurality of customers by value data stored in a data warehouse; and

generating a two-dimensional matrix for cross-segmenting the plurality of customers by both behavior data and value data.

7. The method from claim 2, wherein formulating the interaction strategy comprises choosing a subset of interaction reasons from a pre-defined repository of interactions for a specified industry.

8. The method from claim 2, wherein the step of formulating the new interaction strategy comprises capturing current channel mix for all experiences and future channel mix for prioritized experiences.

9. The method from claim 2, wherein the step of formulating the interaction strategy comprises modeling value opportunities.

10. The method from claim 2, wherein formulating the interaction strategy comprises ranking interaction reasons to determine a primary set of interaction reasons.

11. The method from claim 2 wherein formulating the interaction strategy comprises:

defining a plurality of treatments; and

assigning each of the plurality of the plurality of treatments to a prioritized interaction.

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

a code segment for defining a plurality of prioritized experiences correlating to an interaction strategy, wherein each prioritized experience has at least one associated treatment;

a code segment for storing the plurality of prioritized experiences for consistent treatment among a plurality of channels;

wherein the code segment for storing stores the prioritized experiences to a central repository where the stored experiences are available for application across a plurality of communication channels.

12. The method from claim 21, wherein the step of assigning is based on a hierarchy of grouped rules.

14. The computer program from claim 13, further comprising:

a code segment for evaluating a customer strategy for a company;

a code segment for identifying a plurality of customer segments for a customer base of a company; and

a code segment for formulating an interaction strategy based on value opportunities.

15. The computer program from claim 13, further comprising a code segment for deriving insight about customers from analytical models, wherein defining the prioritized experiences is based on the derived insight.

16. The computer program from claim 15, wherein the code segment for deriving 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 model to predict customer behavior, wherein analytical model is trained using the customer data extracted from the at least one database;

a code segment for gathering the customer interaction results; and

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

17. The computer program from claim 14, wherein the code segment for evaluating the customer strategy comprises:

a code segment for evaluating business value drivers;

a code segment for defining key performance indicators; and

a code segment for defining business constraints.

18. The computer program from claim 14, wherein the code segment for identifying the plurality of customer segments comprises:

a code segment for segmenting a plurality of customers by behavior data stored in a data warehouse;

a code segment for segmenting the plurality of customers by value data stored in a data warehouse; and

a code segment for generating a two-dimensional matrix for cross-segmenting the plurality of customers by both behavior data and value data.

19. The computer program from claim 14, wherein the code segment for formulating the interaction strategy comprises a code segment for choosing a subset of interaction reasons from a pre-defined repository of interactions for a specified industry.

20. The computer program from claim 14, wherein the code segment for formulating the new interaction strategy comprises a code segment for capturing current channel mix for all experiences and future channel mix for prioritized experiences.

21. The computer program from claim 14, wherein the code segment for formulating the interaction strategy comprises a code segment for modeling value opportunities.

22. The computer program from claim 14, wherein the code segment for formulating the interaction strategy comprises a code segment for ranking interaction reasons to determine a primary set of interaction reasons.

23. The computer program from claim 14, wherein the code segment for formulating the interaction strategy comprises:

a code segment for defining a plurality of treatments; and

a code segment for assigning each of the plurality of the plurality of treatments to a prioritized interaction.

24. The computer program from claim 23, wherein the code segment for assigning is based on a hierarchy of grouped rules.

25. A system for optimizing customer experiences, the system comprising:

a workbench analysis subsystem for defining a plurality of prioritized experiences correlating to an interaction strategy, wherein each prioritized experience has at least one associated treatment;

a central repository for storing the plurality of prioritized experiences for consistent treatment among a plurality of channels;

wherein the stored experiences are available for application across a plurality of communication channels.

26. The system from claim 25, further comprising a plurality of customer segments for a customer base of a company;

and

an interaction strategy module for formulating an interaction strategy based on value opportunities.

27. The system from claim 25, further comprising at least one analytical model for use in deriving insight about customers, wherein the derived insight is leveraged by the workbench analysis subsystem for defining the prioritized experiences.

28. The system from claim 27, further comprising:

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

wherein the at least one analytical model is trained to predict customer behavior using the customer data extracted from the at least one database; and

wherein the at least one analytical model is re-trained using the customer interaction results.

29. The system from claim 25, further comprising:

a first set of customer segments based on behavior data stored in a data warehouse;

a second set of customer segments based on value data stored in the data warehouse; and

a two-dimensional matrix for cross-segmenting the plurality of customers as a function of the first set of customer segments and the second set of customer segments;

wherein the plurality of customer segments are determined from the two-dimensional matrix.

30. The system from claim 25, further comprising a pre-defined repository of interactions for a specified industry;

wherein workbench analysis subsystem leverages the pre-defined repository of interactions for defining the plurality of prioritized experiences.

31. The system from claim 26, wherein the interaction strategy module captures current channel mix for all experiences and future channel mix for prioritized experiences.

32. The system from claim 26, wherein the interaction strategy module models value opportunities.

33. The system from claim 26, wherein the interaction strategy module ranks interaction reasons to determine a primary set of interaction reasons.

34. The system from claim 26, wherein the interaction strategy module defines a plurality of treatments, and assigns each of the plurality of treatments to a prioritized interaction.

35. The system from claim 34, wherein the interaction strategy module bases the assignment on a hierarchy of grouped rules.