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  • Using AI to Improve Product Teams’ Customer Empathy

    Valentina Grigoreanu, Monty Hammontree, Travis Lowdermilk

    Chapter from the book: Loizides, F et al. 2020. Human Computer Interaction and Emerging Technologies: Adjunct Proceedings from the INTERACT 2019 Workshops.

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    During customer conversations, it is important to know both what questions to ask at any point during the development cycle, and how to ask them. Asking the right questions to capture rich, accurate, and relevant customer feedback is not easy, and professionally-trained researchers cannot be a part of every customer conversation. To scale out researchers’ knowledge, we built an artificial intelligence system, the VIVID whisper-bot, trained on three theories: the Hypothesis Progression Framework (contextual research questions for each product development phase), the VIVID grammar framework (asking who, what, why, how, where, how much, and when type questions to recreate rich stories), and the syntactical structure of biased and leading questions. The whisper-bot listens in on a customer conversation, highlights customers’ key verbalization (e.g., pain points using the product), and suggests follow-up interview questions (e.g., removing bias or enriching a story). It thereby encourages good interview practices for everyone, which we believe will increase empathy on product development teams, and lead to improvements in the products’ user experience.

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    How to cite this chapter
    Grigoreanu, V et al. 2020. Using AI to Improve Product Teams’ Customer Empathy. In: Loizides, F et al (eds.), Human Computer Interaction and Emerging Technologies. Cardiff: Cardiff University Press. DOI: https://doi.org/10.18573/book3.ag
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    This is an Open Access chapter distributed under the terms of the Creative Commons Attribution 4.0 license (unless stated otherwise), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. Copyright is retained by the author(s).

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    Additional Information

    Published on May 7, 2020

    DOI
    https://doi.org/10.18573/book3.ag