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• Case | HBS Case Collection | September 2020

  Gong: Resonating Conversational Insights

  Alison Wood Brooks and Trevor Spelman

  In 2015, Amit Bendov was struck by a realization about a new technology that might be able to transcribe musical notation in real-time, which eventually became known as Gong. Gong’s business proposition was simple: provide software that automatically captures, understands, and analyzes written and spoken sales conversations (not music) to help sales teams sell more effectively. It was a compelling idea with the potential to create significant value for its users. There were, however, many questions. Could the technology live up to its promises? What could salespeople learn from their own conversations and from the best - and worst - salespeople among them? Could leveraging conversational insights make a measurable impact on a company’s bottom line? And how could Gong defend against competitors, both now and in the future?

  Keywords: Software; Communication; Sales;

  Citation:

  Brooks, Alison Wood, and Trevor Spelman. "Gong: Resonating Conversational Insights." Harvard Business School Case 921-015, September 2020.  View Details

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• Teaching Note | HBS Case Collection | March 2020

  The Boss Has the Wrong Idea

  Katherine B. Coffman, Alison Wood Brooks, Judith A. Clair, Katherine Chen, Manuela Collis and Kathleen L. McGinn

  Teaching Note for HBS Nos. 920-023 and 920-024.

  Citation:

  Coffman, Katherine B., Alison Wood Brooks, Judith A. Clair, Katherine Chen, Manuela Collis, and Kathleen L. McGinn. "The Boss Has the Wrong Idea." Harvard Business School Teaching Note 920-053, March 2020.  View Details

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• Working Paper | HBS Working Paper Series | 2020

  Topic Preference Detection: A Novel Approach to Understand Perspective Taking in Conversation

  Michael Yeomans and Alison Wood Brooks

  Although most humans engage in conversations constantly throughout their lives, conversational mistakes are commonplace— interacting with others is difficult, and conversation re-quires quick, relentless perspective-taking and decision making. For example: during every turn of every conversation, people must decide: Should we stay on this topic or switch to another one? In this paper, we introduce topic preference detection—accurately predicting a conversation partner’s topic preferences (i.e., what they want to talk about)—as a novel approach to evaluate perspective taking. Across synchronous and asynchronous conversations between people in close relationships and among strangers, we find that people want to accommodate their partner's topic preferences in conversation. However, they routinely fall short in learning what other people prefer, compared to benchmark models that rely on natural language processing algorithms to detect topic preferences from people's conversational behavior. Even after a ten-minute face-to-face conversation, people were still missing important cues to their partner's topic interest. Compared to earlier perspective-taking paradigms, our work suggests that topic preference detection captures many naturalistic elements of perspective taking, and demonstrates that conversation can fail to produce mutual understanding, even when people want to accommodate each other’s preferences.

  Keywords: natural language processing; Interpersonal Communication; Perspective; Decision Making; Perception;

  Citation:

  Yeomans, Michael, and Alison Wood Brooks. "Topic Preference Detection: A Novel Approach to Understand Perspective Taking in Conversation." Harvard Business School Working Paper, No. 20-077, February 2020.  View Details

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