AI and AI-Human Based Salesforce Hiring using Conversational Interview Videos

55 Pages Posted: 27 Jun 2022 Last revised: 5 Dec 2023

See all articles by Ishita Chakraborty

Ishita Chakraborty

University of Wisconsin - Madison - Department of Marketing

Khai Chiong

University of Texas at Dallas - Naveen Jindal School of Management

Howard Dover

University of Texas at Dallas

K. Sudhir

Yale School of Management; Yale University-Department of Economics; Yale University - Cowles Foundation

Date Written: April 7, 2023

Abstract

We develop an AI and AI-human-based model for salesforce hiring using recordings of conversational video interviews that involve two-sided, back-and-forth interactions with messages conveyed through multiple modalities (text, voice, and body language). We extract theory-relevant objective measures of interviewees’ sales performance from the text, voice and video modalities as explanatory features in the AI model. Our key contribution to the broader research on persuasion and influence is that we show how to use conversational videos to capture features related to (i) two-way conversational interactivity; (ii) real time adaptation and (iii) human body language, with minimal measurement error relative to extant survey-based approaches that suffer from recall biases. We use rubric-based scores by panels of sales professionals (correlated with hiring decisions) to isolate a candidate’s “latent sales ability;” and use these as outcome variables to be predicted by the AI model. The AI model achieves reasonable predictive accuracy, but integrating human input into an AI-Human hybrid model further enhances performance -- it improves workforce quality relative to a random benchmark by 67%. While the content of what is spoken is most important in prediction, conversational interactivity, sellers' real-time adaptation to the buyer, and body language also have good explanatory power. Finally, in terms of performance-cost trade-offs, the addition of just one human professional evaluation in the hiring loop in combination with AI is optimal. Further, using human input based on only the two early stages of the interview in a task-based hybrid model is the most cost-effective in improving performance.

Keywords: Human-AI, Video Analytics, B2B, Salesforce, Machine Learning

Suggested Citation

Chakraborty, Ishita and Chiong, Khai and Dover, Howard and Sudhir, K., AI and AI-Human Based Salesforce Hiring using Conversational Interview Videos (April 7, 2023). Available at SSRN: https://ssrn.com/abstract=4137872 or http://dx.doi.org/10.2139/ssrn.4137872

Ishita Chakraborty (Contact Author)

University of Wisconsin - Madison - Department of Marketing ( email )

United States
53717 (Fax)

HOME PAGE: http://https://sites.google.com/view/ishitachakraborty/

Khai Chiong

University of Texas at Dallas - Naveen Jindal School of Management ( email )

P.O. Box 830688
Richardson, TX 75083-0688
United States

Howard Dover

University of Texas at Dallas ( email )

2601 North Floyd Road
Richardson, TX 75083
United States

K. Sudhir

Yale School of Management ( email )

135 Prospect Street
P.O. Box 208200
New Haven, CT 06520-8200
United States
203-432-3289 (Phone)
203-432-3003 (Fax)

Yale University-Department of Economics ( email )

28 Hillhouse Ave
New Haven, CT 06520-8268
United States

Yale University - Cowles Foundation ( email )

Box 208281
New Haven, CT 06520-8281
United States

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