Customer-centric interface and method of designing an interface

• US 7,907,719 B2
• Filed: 08/21/2006
• Issued: 03/15/2011
• Est. Priority Date: 03/21/2000
• Status: Expired due to Fees

• Current Assignee: AT&T Labs Incorporated (AT&T, Inc.)
• Original Assignee: AT&T Labs Incorporated (AT&T, Inc.)
• Inventors: Bushey, Robert R., Liddell, Gregory W., Martin, John M., Pasquale, Theodore
• Primary Examiner(s): Kuntz, Curtis
• Assistant Examiner(s): NGUYEN, PHUNG HOANG JOSEPH
• Application Number:
  US11/506,790
• Publication Number:
  US 20070121837A1
• Time in Patent Office: 1,667 Days
• Field of Search: 379/88.16, 379/265.01, 379/266.07, 379/67.01, 379 8818- 24, 345/146, 345/334, 345/339, 345/352, 715/810, 715/811, 705/10
• US Class Current: 379/265.01
• CPC Class Codes: G06F 8/20 Software design
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: H04M 15/00 Arrangements for metering, time-control or time indication ; Metering, charging or billing arrangements for voice wireline or wireless communications, e.g. VoIP

Abstract

A method of designing an interface system that allows users to map the representation of their task directly to the interface. There are three major phases to the Customer-Centric Approach to Interface Design (C-CAID). End-users'"'"' tasks are categorized to determine the frequency of reasons or tasks of why users interact with a particular system. These reasons and their relative frequencies are used to design interface options that emphasize the user'"'"'s task categories. Finally, the customer-centric interface designs are evaluated and compared with existing system interfaces using usability tests with actual users performing the tasks. The results from usability tests are used to pinpoint the task-option combinations that do not work well and which should be revised. Benefits of this customer-centric design are improved systems performance and increased user satisfaction.

Claims

1. A method for developing a prediction model for a user interface design based upon expected user task volume and task frequency, the method comprising:

determining a task frequency of a plurality of call centers;

adjusting the task frequency based on call volumes corresponding to the plurality of call centers;

determining an estimated percentage of correctly routed calls; and

multiplying, by a tangible physical computer processor, the estimated percentage by the adjusted task frequency to obtain a predicted routing percentage score.

2. The method of claim 1, in which adjusting the task frequency comprises:

multiplying a call volume for each call center by a call center task frequency of the call center to obtain a weighted call center task frequency; and

summing each weighted call center task frequency.

3. The method of claim 1, in which a potential performance of the user interface design is evaluated by applying the prediction model.

4. The method of claim 1, in which the prediction model is compared with a prediction model of a current interface to estimate performance gains resulting from the user interface design.

5. The method of claim 1, further comprising:

obtaining a plurality of predicted routing percentage scores corresponding to a plurality of task categories; and

summing the plurality of routing percentage scores to obtain an overall predicted routing percentage for the user interface design.

6. The method of claim 1, in which the user interface design comprises an interactive voice response design.

7. The method of claim 1, in which determining the estimated percentage of correctly routed calls comprises attempting to route a call based upon historical data, the attempt including routing while using the user interface design.

8. A non-transitory computer readable medium that stores a computer program for developing a prediction model for a user interface design based upon expected user task volume and task frequency, the computer readable medium comprising:

a task frequency determining code segment for determining a task frequency of a plurality of call centers;

an adjusting code segment for adjusting the task frequency based on call volumes corresponding to the plurality of call centers;

a correctly routed calls determining code segment for determining an estimated percentage of correctly routed calls; and

a scoring code segment for multiplying the estimated percentage by the adjusted task frequency to obtain a predicted routing percentage score.

9. The non-transitory computer readable medium of claim 8, in which the adjusting code segment adjusts the task frequency by multiplying a call volume for each call center by a call center task frequency of the call center to obtain a weighted call center task frequency, and summing each weighted call center task frequency.

10. The non-transitory computer readable medium of claim 8, in which a potential performance of the user interface design is evaluated by applying the prediction model.

11. The non-transitory computer readable medium of claim 8, further comprising:

a comparing code segment for comparing the prediction model with a prediction model of a current interface to estimate performance gains resulting from the user interface design.

12. The non-transitory computer readable medium of claim 8, further comprising:

a scoring code segment for obtaining a plurality of predicted routing percentage scores corresponding to a plurality of task categories, and summing the plurality of routing percentage scores to obtain an overall predicted routing percentage for the user interface design.

13. The non-transitory computer readable medium of claim 8, in which the user interface design comprises an interactive voice response design.

14. The non-transitory computer readable medium of claim 8, in which the correctly routed calls determining code segment determines the estimated percentage of correctly routed calls by attempting to route a call based upon historical data, the attempt including routing while using the user interface design.