Enhancing insight-driven customer interactions

• US 20070100679A1
• Filed: 03/26/2004
• Published: 05/03/2007
• 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: 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
• Granted Patent: US 8,103,531 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 30/0201 Market modelling; Market analysis; Collecting market data
• 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 optimizing customer experiences, the method comprising:

defining a plurality of prioritized experiences correlating to a customer 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;

dynamically applying the plurality of stored defined experiences during interactions with customers; and

capturing customer interaction results, for refining future targeted interactions.

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 1, wherein the step of storing the plurality of prioritized experiences stores experience data in a central repository;

and

wherein the step of dynamically applying the plurality of defined experiences retrieves experience data from the central repository.

5. 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 the analytical models are trained using the customer data extracted from at least one database;

gathering the customer interaction results; and

re-training 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.

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

evaluating business value drivers;

defining key performance indicators; and

defining business constraints.

7. 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 the data warehouse; and

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

8. 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.

9. 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.

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

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

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

defining a plurality of treatments; and

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

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

14. The method from claim 1, wherein the step of defining the plurality of prioritized experiences enables a business user to define a plurality of treatments.

15. The method from claim 1, wherein the step of dynamically applying the plurality of defined experiences comprises leveraging a centralized rules engine;

wherein the rules engine is independent of and consistent for a plurality of channels.

16. The method from claim 15, wherein the rules engine applies treatments as a function of a customer segment, an interaction type, and an interaction channel.

17. The method from claim 1, wherein the step of applying the plurality of defined experiences comprises:

building a customer intelligence record for representing a plurality of data fields for a customer;

passing the customer intelligence record to a central, channel-independent rules engine;

processing a plurality of rules for updating the customer intelligence record to indicate at least one treatment for the customer; and

sending data from the customer intelligence record to the channel for instructing the channel on the treatments to present to the customer.

18. The method from claim 17, further comprising

scoring customer information;

and

storing scored information in the customer intelligence record.

19. The method from claim 1, wherein the plurality of prioritized experiences support marketing, sales, service and billing functions executed by a customer.

20. The method from claim 2, wherein formulating an interaction strategy includes assessment of a business and identification of opportunities to create value.

21. 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;

a code segment for dynamically applying the plurality of stored defined experiences during interactions with customers; and

a code segment for capturing customer interaction results, for refining future targeted interactions.

22. The computer program from claim 21, 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.

23. The computer program from claim 21, further comprising a code segment for deriving insight about customers from analytical models, wherein defining the prioritized experiences is based on the derived insight.

24. The computer program from claim 21, wherein the code segment for storing the plurality of prioritized experiences stores experience data in a central repository;

and

wherein the code segment for dynamically applying the plurality of defined experiences retrieves experience data from the central repository.

25. The computer program from claim 23, 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 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 re-training 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.

26. The computer program from claim 22, 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.

27. The computer program from claim 22, 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.

28. The computer program from claim 22, 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.

29. The computer program from claim 22, 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.

30. The computer program from claim 22, wherein the code segment for formulating the interaction strategy comprises a code segment for modeling value opportunities.

31. The computer program from claim 22, 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.

32. The computer program from claim 22, 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 treatments to a prioritized interaction.

33. The computer program from claim 32, wherein the code segment for assigning is based on a hierarchy of grouped rules.

34. The computer program from claim 21, wherein the code segment for defining the plurality of prioritized experiences enables a business user to define a plurality of treatments.

35. The computer program from claim 21, wherein the code segment for dynamically applying the plurality of defined experiences comprises a code segment for leveraging a centralized rules engine;

wherein the rules engine is independent of and consistent for a plurality of channels.

36. The computer program from claim 35 wherein the centralized rules engine applies treatments as a function of a customer segment, an interaction type, and an interaction channel.

37. The computer program from claim 21, wherein the code segment for applying the plurality of defined experiences comprises:

a code segment for building a customer intelligence record for representing a plurality of data fields for a customer;

a code segment for passing the customer intelligence record to a central, channel-independent rules engine;

a code segment for processing a plurality of rules for updating the customer intelligence record to indicate at least one treatment for the customer; and

a code segment for sending data from the customer intelligence record to the channel for instructing the channel on the treatments to present to the customer.

38. The computer program from claim 37, further comprising:

a code segment for scoring customer information; and

a code segment for storing the scored information in the customer intelligence record.

39. The computer program claim from 21, wherein the plurality of prioritized experiences support marketing, sales, service and billing functions executed by a customer.

40. The computer program claim from claim 22, wherein formulating an interaction strategy includes assessment of a business and identification of opportunities to create value.

41. 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;

an interaction optimizing subsystem for dynamically applying the plurality of stored defined experiences during interactions with customers; and

a subsystem for capturing customer interaction results, for refining future targeted interactions.

42. The system from claim 41, wherein the interaction optimizing subsystem comprises:

a rules-based engine for choosing from the plurality of prioritized experiences in the central repository; and

a plurality of services for interfacing data between the rules-based engine and the plurality of communication channels.

43. The system from claim 42, wherein the plurality of services comprise:

a plurality of web services; and

a plurality of common customer services.

44. The system from claim 41, 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.

45. The system from claim 41, 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.

46. The system from claim 44, further comprising:

at least one database upon which is stored customer data;

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

wherein the at least one analytical model is re-trained using the customer data extracted from the at least one database and the gathered customer interaction results from the subsystem for capturing customer interaction results.

47. The system from claim 44, 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.

48. The system from claim 41, further comprising a pre-defined repository of interactions for a specified industry;

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

49. The system from claim 44, wherein the interaction strategy module captures current channel mix for all experiences and future channel mix for prioritized experiences.

50. The system from claim 44, wherein the interaction strategy module models value opportunities.

51. The system from claim 44, wherein the interaction strategy module ranks interaction reasons to determine a primary set of interaction reasons.

52. The system from claim 44, wherein the interaction strategy module defines a plurality of treatments and assigns each of the plurality of treatments to a prioritized interaction.

53. The system from claim 52, wherein the interaction strategy module bases the assignment on a hierarchy of grouped rules.

54. The system from claim 41 wherein the workbench analysis subsystem enables a business user to define a plurality of treatments.

55. The system from claim 42, wherein the rules-based engine is independent of and consistent for the plurality of channels.

56. The system from claim 55, wherein the rules-based engine applies treatments as a function of a customer segment, an interaction type, and an interaction channel.

57. The system from claim 42, further comprising:

a customer intelligence record for representing a plurality of data fields for a customer;

wherein the customer intelligence record is passed to the rules-based engine;

wherein the customer intelligence record is updated to indicate at least one treatment for the customer; and

wherein the customer intelligence record is passed to the channel for instructing the channel on the treatments to present to the customer.

58. The system from claim 57, further comprising a scoring module for scoring customer information;

wherein scored information from the scoring module is stored in the customer intelligence record.

59. The system from claim 41, wherein the plurality of prioritized experiences support marketing, sales, service and billing functions executed by a customer.

60. The system from claim 44, wherein formulating an interaction strategy includes assessment of a business and identification of opportunities to create value.