Method and system for conducting customer needs, staff development, and persona-based customer routing analysis

• US 20050187802A1
• Filed: 02/14/2005
• Published: 08/25/2005
• Est. Priority Date: 02/13/2004
• Status: Abandoned Application

• Current Assignee: Citibank NA (Citigroup Incorporated)
• Original Assignee: Citibank NA (Citigroup Incorporated)
• Inventors: Koeppel, Harvey Richard
• Application Number:
  US11/057,771
• Publication Number:
  US 20050187802A1
• Time in Patent Office: Days
• US Class Current: 705/4
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

Computer-implemented methods and systems for conducting customer needs, staff development, or persona-based call routing analyses in which a recommendation engine receives baseline information regarding a current status and one or more objectives of a subject and generates assumed information about the subject based on a statistical evaluation of current status and objectives of a plurality of third parties having pre-determined characteristics in common with the subject. The recommendation engine determines a gap between the current status and objectives of the subject and generates and prioritizes a recommendation for one or more proposals for the subject based on the gap. Thereafter, the recommendation engine formulates one or more follow-up questions for the subject.

Claims

1. A method for conducting customer needs, staff development, or persona-based call routing analyses, comprising:

receiving baseline information regarding a current status of a subject and at least one objective of the subject by a recommendation engine;

generating assumed information regarding the current status of the subject and the at least one objective of the subject by the recommendation engine based on a statistical evaluation of current status and objectives of a plurality of third parties having pre-determined characteristics in common with the subject according to the baseline information for the subject;

determining a gap between the current status of the subject and the at least one objective of the subject by the recommendation engine based on the baseline information and the assumed information;

generating and prioritizing a recommendation for at least one proposal for the subject by the recommendation engine based on the gap between the current status of the subject and the at least one objective of the subject; and

formulating at least one follow-up question for the subject by the recommendation engine based on a statistical analysis of the baseline information compared to the assumed information.

2. The method of claim 1, wherein receiving the baseline information further comprises receiving at least one of demographic information about a customer, current financial condition information about the customer, and information about a relationship between the customer and at least one enterprise.

3. The method of claim 1, wherein receiving the baseline information further comprises receiving at least one of information regarding transactions between a customer and at least one enterprise and information regarding a propensity of the customer to interact in at least one pre-determined manner with the at least one enterprise.

4. The method of claim 1, wherein receiving the baseline information further comprises receiving the baseline information by a recommendation engine sales process module having functionality related to customer asset or wealth management, customer liability or debt management, customer cash management, and customer insurance or risk management.

5. The method of claim 1, wherein receiving the baseline information further comprises receiving vital statistics for the customer consisting at least in part of one of actual customer data in connection with a customer account with an enterprise, actual customer data concerning at least one of a credit score, a debt to income ratio, a remaining term of debt, and a total liabilities for the customer.

6. The method of claim 1, wherein receiving the baseline information further comprises receiving known baseline information articulated by a customer.

7. The method of claim 1, wherein receiving the baseline information further comprises receiving data regarding a current career status of an employee of an enterprise and at least one career objective of the employee.

8. The method of claim 7, wherein receiving data regarding the current career status of the employee further comprises receiving data regarding a current skill level of the employee.

9. The method of claim 8, wherein receiving the data regarding the current skill level of the employee further comprises receiving information maintained by a human resources department of the enterprise regarding the current skill level of the employee.

10. The method of claim 9, wherein receiving the information maintained by the human resources department further comprises receiving data from the human resources department of the enterprise regarding an employment level of the employee with which a pre-determined skill set is associated that is pre-defined as demonstrating competency in executing tasks of a pre-determined type.

11. The method of claim 10, wherein receiving the data regarding the employment level of the employee further comprises receiving data regarding a licensing level of the employee.

12. The method of claim 1, wherein receiving the baseline information further comprises receiving data regarding a plurality of pre-determined characteristics of a persona of a customer of an enterprise and at least one objective of the customer in connection with one of an inbound call to and an outbound call from the enterprise.

13. The method of claim 1, wherein generating the assumed information further comprises generating assumed information regarding the current status of a customer of an enterprise and the at least one objective of the customer.

14. The method of claim 13, wherein generating the assumed information further comprises storing the received baseline information and the assumed information regarding the current status of the customer and the at least one objective of the customer in a customer information database by the recommendation engine.

15. The method of claim 14, wherein generating the assumed information further comprises receiving additional baseline customer information articulated by the customer in a customer interaction with the enterprise.

16. The method of claim 15, wherein receiving the additional baseline customer information further comprises supplementing at least part of the assumed information with the additional baseline customer information in the customer information database.

17. The method of claim 1, wherein generating the assumed information further comprises generating assumed baseline information regarding a current career status of an employee of an enterprise and at least one career objective of the employee.

18. The method of claim 1, wherein generating the assumed information further comprises generating assumed baseline information regarding a plurality of pre-determined characteristics of a persona of a customer of an enterprise and at least one objective of the customer in connection with one of an inbound call to and an outbound call from the enterprise.

19. The method of claim 1, wherein determining the gap between the current status of the subject and the at least one objective of the subject further comprises determining the gap between the current status of a customer of an enterprise and at least one objective of the customer.

20. The method of claim 1, wherein determining the gap between the current status of the subject and the at least one objective of the subject further comprises determining a gap between a current career status of an employee of an enterprise and at least one career objective of the employee.

21. The method of claim 1, wherein determining the gap between the current status of the subject and the at least one objective of the subject further comprises comparing a plurality of predetermined characteristics of a persona of a customer of an enterprise to a plurality of corresponding pre-determined characteristics of personas of a plurality of service representatives of the enterprise.

22. The method of claim 1, wherein generating and prioritizing a recommendation for the at least one proposal for the subject further comprises generating and prioritizing a recommendation by a sales process module of the recommendation engine for at least one financial product for a customer of an enterprise based on the gap between a current status of the customer and at least one objective of the customer.

23. The method of claim 22, wherein generating and prioritizing the recommendation by the sales process module further comprises prompting a sales representative of the enterprise for a conversation with the customer about the recommended financial product.

24. The method of claim 1, wherein generating and prioritizing the recommendation for at least one proposal for the subject further comprises generating and prioritizing a recommendation for at least one next experience for an employee of the enterprise based on the gap between a current career status of the employee and at least one career objective of the employee.

25. The method of claim 24, wherein generating and prioritizing the recommendation for the at least one next experience for the employee further comprises generating a recommendation for achieving a skill set necessary for the employee to acquire in order to reach a level of competence corresponding to the at least one career objective of the employee.

26. The method of claim 1, wherein generating and prioritizing the recommendation for the at least one proposal for the subject further comprises generating and prioritizing a referral of a service representative of an enterprise for a customer of the enterprise in connection with one of an inbound call to and an outbound call from the enterprise based at least in part on a comparison of a plurality of pre-determined characteristics of a persona of the customer to a plurality of corresponding pre-determined characteristics of personas of a plurality of service representatives of the enterprise.

27. The method of claim 26, wherein generating and prioritizing the referral to the service representative for the customer further comprises identifying and matching characteristics of a persona of the customer with a persona of the customer service representative.

28. The method of claim 1, wherein formulating the follow-up question for the subject further comprises formulating the at least one follow-up question for a customer of an enterprise by the recommendation engine.

29. The method of claim 28, wherein formulating the at least one follow-up question for the customer further comprises receiving additional information from the customer in response to the follow-up question for an iteration by the recommendation engine.

30. The method of claim 29, wherein formulating the at least one follow-up question for the customer further comprises generating a recommended solution by the sales process module for a follow-up discussion with the customer based at least in part on additional information received from the customer and stored baseline and assumed customer information from a client information database.

31. The method of claim 1, wherein formulating the follow-up question for the subject further comprises scheduling training for an employee of an enterprise in an area of demonstrated weakness of the employee by the recommendation engine

32. The method of claim 1, wherein formulating the follow-up question for the subject further comprises routing one of an inbound and outbound call between a customer of an enterprise and a service representative of the enterprise based on a match between a plurality of pre-determined characteristics of a persona of the customer with corresponding pre-determined characteristics of a persona of the service representative.

33. A computer-implemented system for conducting customer needs, staff development, or persona-based call routing analyses, comprising:

means for receiving baseline information regarding a current status of a subject and at least one objective of the subject by a recommendation engine;

means for generating assumed information regarding the current status of the subject and the at least one objective of the subject by the recommendation engine based on a statistical evaluation of current status and objectives of a plurality of third parties having pre-determined characteristics in common with the subject according to the baseline information for the subject;

means for determining a gap between the current status of the subject and the at least one objective of the subject by the recommendation engine based on the baseline information and the assumed information;

means for generating and prioritizing a recommendation for at least one proposal for the subject by the recommendation engine based on the gap between the current status of the subject and the at least one objective of the subject; and

means for formulating at least one follow-up question for the subject by the recommendation engine based on a statistical analysis of the baseline information compared to the assumed information.

34. A machine-readable medium on which is encoded program code for conducting customer needs, staff development, or persona-based call routing analyses, the program code comprising instructions for:

receiving baseline information regarding a current status of a subject and at least one objective of the subject by a recommendation engine;

generating assumed information regarding the current status of the subject and the at least one objective of the subject by the recommendation engine based on a statistical evaluation of current status and objectives of a plurality of third parties having pre-determined characteristics in common with the subject according to the baseline information for the subject;

determining a gap between the current status of the subject and the at least one objective of the subject by the recommendation engine based on the baseline information and the assumed information;

generating and prioritizing a recommendation for at least one proposal for the subject by the recommendation engine based on the gap between the current status of the subject and the at least one objective of the subject; and

formulating at least one follow-up question for the subject by the recommendation engine based on a statistical analysis of the baseline information compared to the assumed information.