Method for identifying and prioritizing customer care automation

• US 20060026049A1
• Filed: 07/28/2004
• Published: 02/02/2006
• Est. Priority Date: 07/28/2004
• Status: Abandoned Application

• Current Assignee: SBC Knowledge Ventures LP (AT&T, Inc.)
• Original Assignee: SBC Knowledge Ventures LP (AT&T, Inc.)
• Inventors: Knott, Benjamin Anthony, Bushey, Robert R., Joseph, Kurt M., Pasquale, Theodore
• Application Number:
  US10/901,926
• Publication Number:
  US 20060026049A1
• Time in Patent Office: Days
• US Class Current: 705/8
• CPC Class Codes: G06Q 10/0635 Risk analysis of enterprise or organisation activities
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 10/0639 Performance analysis of employees; Performance analysis of enterprise or organisation operations
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 90/00 Systems or methods specially adapted for administrative, commercial, financial, managerial or supervisory purposes, not involving significant data processing

Abstract

A method of identifying and prioritizing automated customer care applications for use in connection with interactive voice response systems is disclosed. The method includes receiving a first set of data produced by a first data-driven evaluation process relating to a call center environment responsive to calls received by the interactive voice response systems; receiving a second set of data produced by a second data-driven evaluation process relating to customer preferences with respect to self-service for each of a set of tasks; and generating a prioritized list of automated customer care applications based on the first set of data and the second set of data.

Claims

1. A method of identifying and prioritizing automated customer care applications, the method comprising:

receiving interview data derived from call center interviews relating to customer tasks;

analyzing opening statement data to identify information relating to customer preferences for self-service with respect to the customer tasks;

collecting data and metrics relating to customer care to determine cost savings information relating to a plurality of automated customer care applications associated with the customer tasks;

assigning a risk level to each of the plurality of automated customer care applications to identify a level of risk associated with development of each of the automated customer care applications;

generating a prioritized list of the plurality of automated customer care applications based on the interview data, the customer preference information, the cost savings information, and the risk level associated with each of the automated customer care applications.

2. The method of claim 1, wherein the prioritized list of automated customer care applications is associated with at least one of call volume data, customer adoption rate data, estimated return on investment data, and technical risk data.

3. The method of claim 1, wherein the interview data is retrieved in response to an interview with call center callers, the interview data capturing information related to identifying opportunities for automating customer tasks, information related to cost savings, and information relating to call volume.

4. The method of claim 3, further comprising analyzing the interview data to create a list of customer tasks that can be at least partially automated.

5. The method of claim 1, wherein the step of analyzing opening statement data includes sampling a plurality of customer opening statements to collect customer task data and categorizing the customer task data to create a customer task frequency table.

6. The method of claim 5, further comprising generating a plurality of task scenarios based on the customer task frequency table.

7. The method of claim 6, further comprising determining a customer preference level associated with at least one of phone self-service and internet self-service for each of the plurality of task scenarios.

8. The method of claim 1, wherein the data and metrics relating to customer care include call volume by customer task data, number of agents per call center, number of call centers affected by each task, agent loaded cost, and number of days worked per year.

9. The method of claim 1, wherein an agent task cost is determined based on an agent cost, an interactive voice response system access cost, and a transport cost.

10. The method of claim 9, wherein an automation task cost is determined based on a cost of automation and the interactive voice response system access cost.

11. The method of claim 10, wherein an automation opt-out task cost is determined based on a sum of agent cost, interactive voice response system cost and transport cost multiplied by a customer opt-out rate.

12. The method of claim 11, wherein a cost savings metric is determined based on the agent task cost, the automation task cost, and the opt-out task cost.

13. The method of claim 1, wherein the risk level is determined based on risk scores and weights provided by technical experts.

14. A method of identifying and prioritizing automated customer care applications for use in connection with interactive voice response systems, the method comprising:

receiving a first set of data produced by a first data-driven evaluation process relating to a call center environment responsive to calls received by the interactive voice response systems;

receiving a second set of data produced by a second data-driven evaluation process relating to customer preferences with respect to self-service for each of a set of tasks; and

generating a prioritized list of automated customer care applications based on the first set of data and the second set of data.

15. The method of claim 14, wherein generation of the prioritized list of automated customer care applications is further based on automation cost savings data.

16. The method of claim 15, wherein generation of the prioritized list of automated customer care applications is further based on a technology risk assessment.

17. The method of claim 14, wherein the first set of data includes interview data derived from call center interviews.

18. The method of claim 14, wherein the second set of data is derived from customer opening statement data retrieved by at least one of the interactive voice response systems.

19. The method of claim 14, wherein the first data-driven evaluation process in an initial data-driven evaluation process and the second data-driven evaluation process is subsequent to the initial data-driven evaluation process.