Artificial Intelligence Coaches for Sales Agents: Caveats and Solutions

Journal of Marketing, Forthcoming

51 Pages Posted: 21 Nov 2020

See all articles by Xueming Luo

Xueming Luo

Temple University

Marco Qin

Fox School of Business, Temple University

Zheng Fang

Sichuan University - Business School

Zhe Qu

Fudan University

Date Written: September 2020

Abstract

Firms are exploiting AI coaches to provide training to sales agents and improve their job skills. The authors present several caveats associated with such practices based on a series of randomized field experiments. Experiment 1 shows that the incremental benefit of the AI coach over human managers is heterogeneous across agents in an inverted-U shape. While middle-ranked agents improve their performance by the largest amount, both bottom- and top-ranked agents show limited incremental gains. This pattern is driven by a learning-based mechanism in which bottom-ranked agents encounter the most severe information overload problem with the AI versus human coach, while top-ranked agents hold the strongest aversion to the AI relative to human coach. To alleviate the challenge faced by bottom-ranked agents, experiment 2 redesigns the AI coach by restricting the training feedback level and observes a significant improvement in agent performance. Experiment 3 reveals that the AI–human coach assemblage outperforms either the AI or human coach alone. This assemblage can harness the hard data skills of the AI coach and soft interpersonal skills of human managers, solving both problems faced by bottom- and top-ranked agents. These findings offer novel insights into AI coaches for researchers and managers alike.

Keywords: Artificial Intelligence, AI Coach, Salesforce Management, Sales Training, New Technology, Aversion, Information Overload

JEL Classification: M31

Suggested Citation

Luo, Xueming and Qin, Marco and Fang, Zheng and Qu, Zhe, Artificial Intelligence Coaches for Sales Agents: Caveats and Solutions (September 2020). Journal of Marketing, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3704283 or http://dx.doi.org/10.2139/ssrn.3704283

Xueming Luo (Contact Author)

Temple University ( email )

1810 N. 13th Street
Floor 2
Philadelphia, PA 19128
United States

HOME PAGE: http://www.fox.temple.edu/mcm_people/xueming-luo/

Marco Qin

Fox School of Business, Temple University ( email )

Philadelphia, PA 19122
United States
215-204-6829 (Phone)

Zheng Fang

Sichuan University - Business School ( email )

China

Zhe Qu

Fudan University ( email )

No. 670, Guoshun Road
No.670 Guoshun Road
Shanghai, 200433
China

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