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Design and evaluation of adaptive interfaces for IVR systems

Published: 27 April 2013 Publication History

Abstract

Dialing a telephone number of an organization and coming across an automated system attending the call, instead of a human, has become common. These automated applications are known as Interactive Voice Response (IVR) systems. In developing nations, IVR systems are mostly touch-tone (key-press) because of under-supported speech technologies for non-English native languages and accents. Optimizing such IVR systems is crucial as they are often considered as time consuming and frustrating and further becomes more challenging when the data for individual caller to perform optimization are often unavailable.
In this work, we investigate the performances of adaptive interfaces for touch-tone IVR system to optimize for future callers based on past system usage by other callers. We show that a portion of the call duration goes into selecting the correct menu option in IVR. To reduce this, desired menu options by a prospective caller must appear early in the sequence. We show that adaptive approaches to decide the optimal menu structure for future caller outperform static menu based IVR system. We have designed, deployed and evaluated different adaptive schemes for IVR in a real world study.

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    cover image ACM Conferences
    CHI EA '13: CHI '13 Extended Abstracts on Human Factors in Computing Systems
    April 2013
    3360 pages
    ISBN:9781450319522
    DOI:10.1145/2468356
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 27 April 2013

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    Author Tags

    1. adaptive systems
    2. ivr
    3. navigation time
    4. order of relevance

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    CHI EA '13 Paper Acceptance Rate 630 of 1,963 submissions, 32%;
    Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

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